Approaches for network based drug discovery
Molecular network-based studies have gained tremendous importance in biomedical research. Several such advanced technologies in molecular biology have evolved in the past decade and have contributed to building up enormous molecular data. These molecular networks gained much significance among researchers triggering widespread use of experimental and computational tools. This interest led researchers to compile data of biomolecules systematically and to develop various computational tools for analyzing data. In the present scenario, an enormous amount of molecular network databases are available which can be accessed freely by the public. This is the central focus of this article.
Drug discovery; Online tools; Networks; Therapeutics; Drug development; Drug design; Web tools; Review
A drug is a chemical component whose structure is probably known, exerts a biological effect when it is administered to an organism . The role of a drug is to prevent or cure a particular disease or disorder. In the past, health practitioners and people used plant extracts as a medicine to treat or cure diseases. However, currently, most of the drugs available commercially are either synthesized or produced using genetic engineering and scaling up techniques .
A pharmaceutical drug alters the structure and activity of molecular networks by changing the activity of biomolecules. The drug targets may be peptides, proteins, or nucleic acids. Drugs are classified into three categories (i) biological compounds that target membrane receptors and extracellular proteins (ii) nucleic acids which target mRNA [3, 4], (iii) low molecular weight compounds which target receptors or enzymes . These small molecules are mostly preferred because of their low cost and can be delivered easily to the target site. But the major disadvantage of these small molecules is that they can target a very less number of proteins . Many alternative forms of medicines are now also available for the treatment of several diseases albeit their scientific background is yet to be established [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]. Therefore, controversies are also seen in literature with such remedies . Although both in silico and in vivo approaches are adopted to discover drugs with potential , drug, dose, compatibility, side effects are existing issues in biomedical sciences. Therefore, many nutraceuticals are suggested for consumption and championed to be equally important as medicines to handle diseases [13, 23, 24, 25, 26, 27].
In the field of biomedical research, identification of a drug target is a time consuming and laborious task. Moreover, it is not possible to screen every target for a drug in the laboratory for both direct and alternative medical approaches [13, 14, 15, 16, 18, 19, 26]. Starting from environmental health [7, 27], nutritional data , to complicated miRNA interaction in health and aging [3, 4], the generation of vast data is very common. For example, bio-medical pathway studies, not only provide new insights, but also vast data [13, 23, 24]. Although many in silico tools are used to study bio-phenomena in cells [28, 29, 30, 31], tools for handling data, as well as techniques for identification of drugs and their targets by in silico methods have gained momentum only lately. Most of the computational methods for analyzing drug-target interactions are based on receptor or ligand models. In the case of receptor-based methods, a target with a known structure is subjected to docking to analyze its drug binding efficiency . On the other hand, the ligand-based prediction of Drug Target Interaction (DTI) involves the comparison of drugs with the target protein’s ligand. Singh et al.  studied some potential targets of drugs by using the ligand-based prediction method. In recent years, network-based methods play a major role in the identification of drugs and their targets. The major advantage of this method is that even if the 3D Structure of a drug or target is not known, their interaction can be predicted.
Analyzing the human genome by sequence homology to the known drug targets is one of the ways to identify an effective drug target. For this analysis, information from protein structural databases is very vital. Structural information plays a vital role because some homologs cannot be identified using sequence data alone, moreover, analysis of the effectiveness of drug-target binding can also be predicted through this. Side effect similarity is one of the methods which can also be used to identify drugs and their targets . In this article, we have discussed Network-Based Drug Discovery (NBDD) and its use in health sciences.
3. Interaction of drug with target
Interaction of a drug with its target is the basis for drug discovery. Identifying drug-target interaction experimentally is a laborious and expensive undertaking. With the development of internet and data repositories, it is efficient and inexpensive to use computational methods for identifying the interaction of a drug with its target. Computational methods can be subdivided into different categories like molecular docking-based, pharmacophore-based, similarity-based, machine learning-based and network-based methods. Among these methods, network-based methods are highly reliable and have an advantage over others (Fig. 1).
Since there is an enormous development in network pharmacology and systems biology, there is a leap in the drug discovery paradigm from linear mode (single drug → single target → single disease) to a network mode (multiple drugs → multiple targets → multiple-diseases) [35, 36, 37]. The change in this paradigm means that there is a possibility for a drug can interact with multiple targets rather than with a single target [38, 39, 40]. The interaction of a drug with its target can lead either to a desired therapeutic effect or some undesired side effects [33, 37, 38, 41, 42, 43]. Knowledge of the above can help predict drug-target interaction during the discovery of a new drug and prevent unnecessary side effects thereby increasing the therapeutic efficacy of the drug.
Traditionally, researchers used to identify the drug-target interactions using some parameters like their dissociation and inhibition constant, half-maximum effective and inhibitory concentration via biochemical experiments both in vivo and in vitro. The major drawback of adopting these methods is the time and expense required to systematically test every drug for its interaction with the target through biochemical experiments. To overcome these problems, a lot of computational tools have been developed in the past decade which is cost-effective and highly efficient [44, 45, 46].
4. Methods to study drug interaction
4.1 Molecular docking-based method
It is a traditional method for the prediction of a drug-target interaction. This method is mainly based on the three-dimensional structure of the targets and it is widely used [47, 48, 49, 50, 51, 52]. In this method, scoring functions are used to analyze the drug-target interaction, and a quantitative docking score can be obtained for a drug to its corresponding target. This docking score can be directly correlated with the binding affinity of a drug with its target [53, 54]. In another docking method called reverse docking, a potential target can be predicted for a known drug which is vice versa of the above method [47, 55, 56]. The major applications of molecular docking include polypharmacology, drug repositioning, analysis of adverse side effects, and target hunting . Many web applications including TarFisDock  and DRAR-CPI  were designed to perform docking based on target hunting.
4.2 Pharmacophore-based method
Pharmacophore modeling is a widely used method in the identification of drug-target interaction. Pharmacophore based methods are mainly categorized into two types namely structure-based and ligand-based methods (Fig. 2). Both the subtypes can be used efficiently in the prediction of drug-target interactions [47, 59]. The programs that are available for pharmacophore modeling includes Molecular Operating Environment (MOE) (Chemical Computing Group), Pharmer , LigandScout , Screen (ChemAxon Screen Suite), DiscoveryStudio (BIOVA Discovery Studio) and Phase . Some of the web servers for pharmacophore modeling include PharmMapper , ZINCPharmer , CavityPlus , and Pharmit . The interaction of a protein with the ligand normally exerts a pharmacological effect and this is the basis of ligand-based pharmacophore modeling. The web server that can be used to identify the pharmacophore of selected ligands is pharmagist. The major advantage of this web server is it is available free of cost and it will give the results in few minutes . On the other hand, the structure-based pharmacophore modeling requires a ligand-binding structure which will analyze the interaction site and produce a suitable pharmacophore model. Zinc Pharmer is open-source software that is used mainly in this method. Usually, the structures are derived from the protein database bank and ZINC database and subjected to analysis in ZINC Pharmer for pharmacophore modeling [68, 69]. After analysis, a statistical score for the potential targets will be available which further enhances the method. More than 7000 ligand-based pharmacophore models are available in this software.
4.3 Similarity-based method
It is also a traditional method for the prediction of drug-target interaction . It purely depends on the assumption that similar drugs will have similar targets and vice versa. The similarity among two different drugs can be analyzed either using the structure of a drug or its profile, and the similarity between the targets can be identified using a sequence of the targets. The input data including the structure or profile of a drug or the target sequence should be given by the user. if the drug structure is given as an input data the webserver analyses for the similarity between the given data and the data available in the databases which can be used to predict the DTI from known drug target to the unknown one . Different types of similarity-based methods are available including 3D shape-based similarity , two-dimensional (2D) fingerprint-based similarity , and phenotypic based similarity  methods. Many web applications including ChemMapper  and Similarity Ensemble Approach (SEA)  are available to which applies 2D and 3D similarity for the prediction of drug-target interaction (Fig. 3).
4.4 Machine learning-based
Machine-learning based methods are generally used in drug-target interaction and are experiencing rapid development in recent years [76, 77]. When compared to the traditional machine learning techniques, some modern techniques like deep learning techniques are applied more recently for the prediction of drug-target interaction [78, 79]. Even though the three-Dimensional structure data of the target can be used for developing machine learning methods , of late protein sequence descriptors and molecular descriptors are mostly used [76, 81, 82].
Machine learning-based methods are broadly classified into two subtypes: supervised and semi-supervised methods. In the case of the supervised machine learning method, a training set requires both the positive and negative labeled samples for the prediction of drug-target interaction. Here the positive labeled sample indicates the known interaction of known drug targets and the others are labeled as negative. The supervised method is again subdivided into similarity-based methods and feature vector-based methods. Similarity-based methods mainly rely on the similarity between the different drugs or targets but in the feature vector based method, feature vectors are used as the training data. These feature vectors contain the different properties of targets and drugs. Huge number of supervised models have been proposed by the researchers and most of them are found to be feasible. A semi-supervised machine learning-based method requires a little labeled data and more unlabeled data for the prediction of drug-target interaction. The semi-supervised method uses the labeled data to identify the unlabeled data .
4.5 Network based methods
When compared to all other DTI prediction methods, the network-based method has gained much prominence recently. The network-based methods do not only depend based on 3 Dimensional structures of the targets. The algorithms used to derive the network-based method include recommendation algorithms and link prediction algorithms. In the past decade, a recommendation algorithm called network-based inference was derived. This algorithm is also called probabilistic spreading (ProbS) .
Network-based methods are one of the simplest methods and it does not need any other additional information like structure or sequence for the prediction of drug-target interaction. Several other methods were also developed based on this method [85, 86, 87]. Since these methods are not dependent on the 3-dimensional structure of the protein it can be useful for the targets whose 3D structures are not known. The major advantage of these techniques includes simple, fast, and accurate. It is based on very simple physical processes such as resource diffusion [86, 87, 88], collaborative filtering [88, 89], and random walk  on networks. When compared to the other drug-target interaction prediction methods explained earlier the calculation procedure for the network-based methods is very simple. So, network-based methods are preferred mostly, and they can run very fast on the computer.
5. Drug repositioning
Drug repositioning is a process which is used to find a new curative value for pre-existing drug, and it is quite closely related to drug target prediction . Since an already existing drug is used it is very easy to identify the target when compared to working with a new drug. For drug repositioning, the above-mentioned methods for drug-target interaction can also be applied. Many software programs have already been developed for drug repositioning. Some examples of drug repositioning are the use of thalidomide for severe erythema nodosum leprosum, retinoic acid for acute promyelocytic leukemia and sildenafil for erectile dysfunction and pulmonary hypertension [92, 93].
In technical words, the process of identifying new indications for the already approved drugs is called drug repositioning . Since this process uses existing drugs, some of the preapproval tests usually performed for the drugs are not needed since they are already proved to be safe for human consumption. Therefore, the drug discovery process is very short in the case of drug repositioning [95, 96]. For this reason, governmental, non-governmental agencies, and academic researchers are showing a great interest in drug repositioning. Drug repurposing, drug redirecting, drug re-tasking, drug re-profiling, and therapeutic switching are the synonyms for drug repositioning. New therapeutic uses for failed drugs (which are safe to use) can also be developed using drug repositioning . More in silico tools have been developed for drug repositioning .
Drug repositioning has major advantages, first and foremost they are cost-effective and there is less risk for drug development since it is developed from an already existing drug. Repositioned drugs can easily pass through all the clinical trials and they have a very less possibility of developing any adverse effects thereby facilitating the development of a potential drug in a shorter span [99, 100, 101]. It has a wide range of applications in disease and related therapeutic areas. Evolving new anticancer drugs from available anticancer drugs is one of the leading fields in drug repositioning because the demand for anticancer drugs continues to increase .
Drug repositioning plays a vital role in developing drugs for diseases by which affect only a few people, the major reason behind this is that there is an insufficient financial benefit for developing a new drug by the pharmaceutical industry . With the help of drug repositioning, it is possible to identify novel therapies for those diseases with limited development costs. The major problem for drug inefficiency is drug resistance. For the use of non-antibiotic drugs to overcome antimicrobial resistance, Drug repositioning will be very helpful . Drug repositioning helps to improve the efficacy of a drug and to avoid drug failure .
6. Data sources for network construction
Drug target interaction networks form the basis for network-based models. For the construction of a quality network enormous and sufficient data is needed [105, 106]. Large amounts of data are available from small molecules to macromolecules and they are available online for free (Table 1). These data include structures, properties, etc. .
|1||Binding DB||It is a publicly accessible database that contains measured binding affinities. It contains 1,794,819 binding data, for 7,438 protein targets and 796,104 small molecules.|
|2||Binding MOAD||It is a subset of the protein data bank and it contains many examples for ligand-protein binding. So it is called the Mother of All Databases (MOAD). The main aim of this database is to collect the data about protein crystal structures with their relevant ligands. These data are normally extracted from the literature.|
|3||ChEMBL||It is a database that contains biologically active molecules with drug-like properties. It contains information including chemical, genomic data, and bioactivity to translate the genomic information into an effective drug.|
|4||DrugCentral||Drug Central contains information about the mode of action of a drug, pharmacological action, chemical entities, and pharmaceutical products. Some information about discontinued drugs is also available in this database.|
|5||IUPHAR/BPS Guide to PHARMACOLOGY||This database has information about ligand-activity-target relationships; this information is taken from chemistry and medicinal literature which are high in quality. The main aim of this database is to provide maximum information about drug targets.|
|6||PDBbind-CN||It is a compilation of experimentally determined binding affinity data for all the biomolecules present in PDB.|
|7||PDSP Ki Database||This database contains information about the potentiality of the drugs to interact with the molecular targets. It contains experimentally derived and published affinity values for more number of drugs with their targets.|
|8||PubChemBioAssay||This database is a repository of biological activity of different compounds and it also contains descriptions of bioactivity assays that are used to screen chemical substances that are present in the PubChem Substance database.|
|9||RCSB Protein Data Bank||It has information about 3D shapes of nucleic acids, proteins, and complex molecules. This database helps students and researchers by creating tools and resources for molecular biology, structural biology, computational biology, and beyond.|
|10||SuperTarget||It is an extensive database that contains 332828 drug-target interactions.|
|11||STITCH||This database contains predicted interactions between a variety of proteins and chemicals.|
|12||TDR Targets||This Database helps to identify molecular targets for the drug discovery process, by focusing on pathogens responsible for a particular disease. It is an integration of pathogen-specific genomic information with functional data that are retrieved from various sources, including literature.|
Several ways are available for the construction of a DTI network. Drug target interaction data can be downloaded from databases such as Drug Bank  and Therapeutic Target Database  and these databases are available online for free access to the public. The drug-target pairs downloaded from these databases can be used for constructing a drug-target interaction network. But the disadvantage of these databases is that the drug-target interaction is not proven experimentally so it is not quantitative. Hence there may be some problems during the merging of drug-target interaction data from different sources. On the contrary, some drug target interaction databases provide experimentally determined DTI data with quantitative activity values such as EC50, Ki, IC50, and Kd values. For example BindingDB , Binding MOAD , ChEMBL , DrugCentral , IUPHAR/BPS Guide to PHARMACOLOGY , PDBbind-CN , PDSP Ki Database , PubChemBioAssay , RCSB Protein Data Bank , SuperTarget , STITCH , TDR Targets , Thomson Reuters Integrity, etc. Once the quantitative drug target interaction is retrieved from these databases, data filtering and merging can be done.
Numerous data from different databases are used by the researchers to enable DTI prediction. It includes chemical tool box such as Open Babel  to generate chemical sub-structures for the drugs and PaDEL-Descriptor . Some databases like DrugBank , DrugCentral , and KEGG DRUG  can be used to obtain the Anatomical Therapeutic Chemical classification (ATC) codes of drugs.
Comparative Toxicogenomics Database (CTD) , SIDER , and OFFSIDES  are the databases that can be used to collect the Side effects of drugs. UniProt knowledgebase  can be used to download the Sequences of target proteins. From the information obtained from these databases construction of many types of data can be done. An example of this is that substructures of the drugs and targets can be used to calculate the chemical likeness of drug-drug pairs [88, 90]. ATC codes and side effects databases can be used to calculate curative and adverse-effect similarity networks of drug-drug pairs . Protein sequence databases can be used to calculate the sequence similarity of target-target pairs [88, 90]. Once these similarity data are obtained from these similarity databases, this information can be used to construct networks.
7. Side effects nbdd analysis
When a drug binds to some other proteins in addition to their desired targets there is a major chance for it to induce some physiological side effects. Since these side effects are to be considered during drug trials, it is necessary to avoid drug interaction with proteins other than the target. This prevents the disapproval of the drug. In the SIDER database, all the information regarding side effects is available; this database contains side effect information for more than 1,000 drugs which are already available in the market [125, 129] correlated the side effects of a drug molecule with the proteins it binds to using the SIDER database. From this, it is concluded that the drug’s target can be predicted based on their side effects (Fig. 4).
8. Drug discovery from medicinal plants by nbdds
Medicinal plants are complex, and they contain multiple active components which makes them highly efficient. The presence of these active components makes them a potential candidate for the preparation of network-based multipotent drugs [84, 130]. Active components of these herbs can be isolated using various isolation techniques and can be used with the chemical drugs which are commercially available to produce a synergistic effect. A suitable and well-known example of this kind of synergistic effect is the use of Human Immunodeficiency Virus (HIV) triple cocktail treatment along with the administration of tannin phytoconstituent isolated from a medicinal plant. This treatment is very effective against the propagation of HIV. Tannin is found to exert this effect by suppressing the activities of protease, reverse transcriptase, and integrase which are mainly responsible for the HIV propagation. Tannin also obstructs the viral fusion and entry into the host .
Another example of this is the artemisinin, an antimalarial drug extracted from the Qinghao, a Chinese medicinal herb. This artemisinin is effective when it is combined with some other chemical drugs. When artemisinin is combined with chloroquine it prevents drug resistance by Plasmodium falciparum in malaria [132, 133, 134]. Other chemical drugs like meflorquine, fansidar are also effective against malaria when they are combined with artemisinin .
The traditional method of treating diseases using herbal formulas will be the major source for the development of multi-target drugs. In traditional medicinal treatment, our ancestors developed herbal formulations on their own based on their experience and long-term practice. There are three major steps involved in the development of network-based multi-target drugs. They are i) analyzing the efficiency of original herbal formulation ii) isolation of potent bioactive compound from the formulation iii) development of a new multi-component drug from the formulation. This component will exhibit a cumulative effect [136, 137]. Traditional herbal drugs with multi-target potential and modern systems biology are the greatest milestone in the revolution of network-based drug discovery.
9. Elucidation of molecular mechanisms by nbdds
When a drug is administered to a host there are two possible outcomes one is a therapeutic effect and the other is undesirable side effects. The main application of network-based methods is analyzing the molecular mechanism of both these effects. The network namely Drug-Gene-Disease networks are commonly used to elucidate molecular mechanisms [86, 87, 128, 138, 139]. When the information about a drug, its target site so-called gene, and specific disease is known it is easy to construct a Drug- Gene- Disease network.
The association of gene and disease is available in the databases like CTD , HuGE Navigator , Online Mendelian Inheritance in Man , and PharmGKB . Once the drug-gene-disease network has been constructed, the constructed network can be visualized using visualization tools including Cytoscape . This can be used to see the network visually. Some other bioinformatics tools like gene set enrichment analysis  are used for the gene function analysis in the network. With the help of these analysis results and from the published data the molecular mechanisms can be elucidated (Fig. 5).
10. Protein-protein interaction (PPI) networks
Protein-protein interaction networks are responsible for the formation of enzymes, macromolecules that are needed for cellular processes. It plays a major role in maintaining the body’s homeostasis. When there is any dis-regulation in this network it results in cellular dysfunction and finally leads to various diseases. Protein-protein interaction networks are highly specific triggering researchers to consider them as targets for drug designing. Researchers specifically target disease-related pathways [145, 146, 147].
With the proper knowledge of the Protein-protein interaction network of an organism or a cell, it is possible to predict the relationship between its genotypes and phenotypes. Change in this network leads to abnormal conditions and diseases. The surface molecules present in protein-protein interaction sites are important for studying unnecessary interactions by molecular recognition mechanism. Because of these advantages, Protein-protein interaction networks have a great therapeutic application in the rational designing of drugs. Appropriate knowledge about the molecular recognition mechanism of PPIs and the interpretation of PPI networks can ease the experimental methods. Experimental methods are divided into two types. They are i) methods used to identify large scale PPIs ii) methods used to identify single PPIs . Some of the high-end methods like phage display, affinity purification, and yeast two-hybrid system can explore more of PPIs by expression of an individual protein. Cryo-electron microscopy, X-ray crystallography, and nuclear magnetic resonance (NMR) spectroscopy are some of the analytical methods that can analyze a specific PPI. These analytical methods can determine PPI sites even at their atomic level. These analytical methods have some disadvantages due to some physicochemical factors including post-translational modification (PTM), transient dynamics [149, 150], and proteins with intrinsically disordered regions [151, 152, 153, 154].
Usually, proteins are expressed in a location and transported to another location to exert its role. These proteins will never interact unnecessarily with the other in vivo but there is a chance for them to undergo unnecessary interactions in vitro. The major disadvantage of the experimental methods is that they consume more time, need more manpower and also, they are expensive. So, in silico approaches are efficient in identifying PPIs and PPI sites.
10.1 Analyses of PPI network
For the understanding of complex biological systems, protein-protein interaction network-based analyses are extremely necessary since they play a major role in most of the cellular events. These protein-protein interaction data are not available in a single database. They are found in different databases. Therefore all this information available in different databases have to be gathered and a single repository should be created for easy access by the public (Table 2). Chel et al.  and Razick et al.  have attempted to integrate all this information for the creation of integrated repositories.
|1||PRISM PROTOCOL||Performs PPI by structural matching|
|2||Coev2Net||It contains interaction information collected from high throughput analysis.|
|3||InPrePPI||It is based on genomic content for predicting PPIs especially in prokaryotes.|
|4||TSEMA||It predicts the interaction between two protein families.|
|5||G-NEST||It uses chromosomal closeness/gene neighborhood to predict PPI.|
|6||InterPreTS||It uses the tertiary structure of the proteins to predict PPI.|
|7||MirrorTree||Predicts PPI based on taxonomic context|
|8||Struct2Net||Structure-based prediction of PPI networks|
|9||STRING||Used for the functional enrichment analysis of PPIs|
|10||PoiNet||It combines or correlates tissue-specific expression data and PPIs.|
|11||OrthoMCL-DB||It is used to align proteins based on their structural similarity.|
|12||PrePPI||It contains both experimentally determined and predicted PPIs of the human proteome.|
|13||COG||It is based on the Phylogenetic classification of proteins.|
|14||iWARP||It uses protein sequences to predict PPIs.|
|15||PHOG||It uses a novel algorithm for the identification of orthologs based on phylogeny.|
|16||PIPE2||It predicts PPI based on the sensitivity and specificity of the selected proteins.|
|17||BLASTO||It uses ortholog group data to perform BLAST.|
|18||PreSPI||It is used to predict the interaction probability of proteins. It is a domain combination based prediction system.|
|19||SPPS||It is a sequence-based method for the prediction of PPI.|
|20||HomoMINT||It assigns proteins to orthology groups. It assigns proteins to ortholog groups applying human protein as the important ortholog.|
The major disadvantage of these PPI networks is that they may have some false positive or false negatives which make its quality poorer, so it is much necessary to evaluate the PPI network properly. Before constructing a PPI network, it is much advisable to collect the data from different analytical methods that are published in different publications. 3D structure of interacting proteins is essential for constructing PPI networks . The important properties of PPI should be taken into consideration before constructing a network which includes post-translational modifications, interaction type, homologous associations, cellular/tissue environment, gene expression patterns, and subcellular localization .
10.2 PPI characterization by in silico approaches
In silico approaches for the prediction of PPIs has gained much importance since the experimental mapping of protein interactomes is not possible because vast numbers of proteins are available. This in silico approaches demonstrates whether two proteins interact or will not. An in silico docking between two proteins that rely on the physicochemical and structural properties of individual proteins is an alternative for PPI network construction [148, 158]. The main disadvantage of this docking technique is that it is difficult to interpret proteins with large conformational changes without proper knowledge about PPI interaction sites .
Various in silico approaches for the prediction of protein-protein interaction network involves structure-based and sequence-based approaches, gene fusion, phylogenetic tree, chromosomal closeness, and gene expression study-based approaches. The main principle behind structure-based approaches is that if two proteins have a similar structure then their interaction network also may be similar. For example, if proteins 1 and 2 interacts with each other than protein 1 and 2 whose structures are like 1 and 2 will interact with each other . The protein-protein interaction observed in one species may be similar to the interaction observed in another species. This is the basis for sequence-based prediction approaches . Gene fusion method is also called a Rosetta stone method. It is because some proteins will have a single domain in one organism and multiple domains in another organism. This domain arrangement information plays a major role in predicting protein-protein interaction networks in this kind. But this method applies only to the proteins that exist as a domain [162, 163]. A phylogenetic tree is one of the most important methods in the prediction of PPI. The Phylogenetic tree gives information about the evolutionary history of a protein. It is believed that the interacting proteins may have a similarity in their evolutionary history . Chromosomal closeness can be used to predict PPI based on the fact that the functionally same proteins are closely organized in the genome . Based on the expression level of different genes the functional relationship between the genes can be identified using an algorithm called clustering algorithms. This information can also be used in the prediction of the PPI network .
Several protein-protein interaction databases are available online for the researchers including PINA2.0, IntAct, BioGrid, APID, MINT, HitPredict, DIP, BIND .
11. Integrated network-based methods
All the networks will have their unique characteristics, for example, PPI networks provide knowledge about which kind of protein is involved in interaction, but it will not provide information about the reaction of these proteins to an external stimulus. This disadvantage can be overcome by the application of gene expression data as it provides the reaction of an organism to an external stimulus. Thus, a single network cannot give the entire information about an organism. It is necessary to integrate the different types of networks to make a repository with the entire information about an organism [169, 170].
A popular and well-known model for integrated networks is the ABC model. This model gives information about the relationship between two concepts. Consider that C is a disease; B is one of the properties of the disease which is mined from a database. Consider A is an individual drug and it has some specific effect on the characteristics of a disease which is obtained from another database. So, if we integrate these data and create a single repository, all the information about the specified disease can be accessed easily.
Two drug discovery types that have been proposed based on this ABC model. They are (i) open discovery model (ii) closed discovery model. As far as the closed discovery model is considered the components A and C are known but component B alone is unknown. So an attempt is made to find the relation of A and C to the component B. open discovery model undergoes two stages one is the identification of the relationship between A and B and the other is the identification of the relationship between B and C. In both the cases, multiple relationships between ABC can be studied .
Various other methods were also constructed successfully based on this method. One such kind CoPub  that gives a clear knowledge about the relationship between genes, diseases, drugs, and pathways. Yet another method which is similar to this is the method proposed by  that provides information about the relationship between disease-gene and gene-drug by applying the ABC model. The main objective of this ABC model is to identify anticancer drug candidates for repurposing.
Contemporary biomedical research is most meaningful with molecular network-based studies, especially NBDD studies because it has good potential for prediction of interaction between important biomolecules involved in the disease. The main advantage of NBDD is that it deals with enormous molecular data associated with medical science which are generated from several advanced molecular biology technologies. NBDD not only helps in simplifying access and understanding of data from the huge molecular networks, but it also helps to study biomedical phenomena deeply, employing both in silico and in vitro tools. The results help in silico biologists to develop new tools for target-oriented work precisely. NBDD data are crucial for achieving individual targets in bio-medicines through publicly available molecular data.
13. Ethics approval and consent to participate
14. Author contributions
PJ and RN conceived and designed the review outlines. PJ and SI originally wrote the paper. RN, BP and SB have critically evaluated, edited the original draft.
We thank the three anonymous reviewers for excellent criticism of the article.
This study was supported by grants from SERB, DST, Govt. of India, New Delhi (ECR/2016/001984 by and Department of Biotechnology, DST, Government of Odisha, Bhubaneswar (1188/ST, Bhubaneswar, dated 01.03.17, ST- (Bio)-02/2017).
17. Conflict of interest
The author declares no conflict of interest.
2D, two dimensional; 3D, three dimensional; CTD, Comparative Toxicogenomics Database; DTI, Drug Target Interaction; HIV, Human Immunodeficiency Virus; MOE, Molecular Operating Environment; NBDD, Network-Based Drug Discovery; PPI, Protein-Protein Interaction.
-  Rang HP, Dale MM, Ritter JM, Flower RJ, Henderson G. What is pharmacology? Rang & Dale’S Pharmacology. 2012; 1–5.
-  Atanasov AG, Waltenberger B, Pferschy-Wenzig E, Linder T, Wawrosch C, Uhrin P, et al. Discovery and resupply of pharmacologically active plant-derived natural products: a review. Biotechnology Advances. 2015; 33: 1582–1614.
-  Nirmaladevi R. MicroRNAs Epigenetic players in cancer and aging. Frontiers in Bioscience (Scholar edition). 2019; 11: 29–55.
-  Ilango S, Priyanka J, Paital B, Padma PR, Nirmaladevi R. Epigenetic alterations in cancer. Frontiers in Bioscience (Landmark edition). 2020; 25: 1058–1109.
-  Gashaw I, Ellinghaus P, Sommer A, Asadullah K. What makes a good drug target? Drug Discovery Today. 2012; 16: 1037–1043.
-  Overington JP, Al-Lazikani B, Hopkins AL. How many drug targets are there? Nature Reviews Drug Discovery. 2007; 5: 993–996.
-  Paital B, Panda SK, Hati AK, Mohanty B, Mohapatra MK, Kanungo S, et al. Longevity of animals under reactive oxygen species stress and disease susceptibility due to global warming. World Journal of Biological Chemistry. 2016; 7: 110–127.
-  Paital B, Hati AK, Nanda LK, Mishra AK, Nayak C. Combined effects of constitutional and organopathic homeopathic medicines for better improvement of benign prostatic hyperplasia cases. International Journal of Clinical & Medical Imaging. 2017; 04: 1000571.
-  Nayak C, Hati AK, Dash SK, Paital B. A case report on benign prostatic hyperplasia with homeopathic remedies. Indo American Journal of Pharmaceutical Sciences. 2017; 4: 4398–4403.
-  Nayak C, Hati AK, Dash SK, Paital B. Benign prostatic hyperplasia and homoeopathic treatment: case study of a 64 years old patient. Indo American Journal of Pharmaceutical Sciences. 2017; 4: 4695–4703.
-  Nayak C, Sahoo AK, Chaturbhuja N, Prusti U, Hati AK, Paital B. A case report of ureteric calculus treated with homoeopathic medicine, Hydrangea arborescens 30. Indo American Journal of Pharmaceutical Sciences. 2018; 05: 627–633.
-  Nayak C, Hati AK, Pati S, Paital B. A view of homoeopathy on musculoskeletal disorder in sports injuries. Journal of Drug Delivery and Therapeutics. 2019; 9: 857–866.
-  Mishra P, Paital B, Jena S, Samanta L, Kumar S, Chainy GBN, et al. Possible activation of NRF2 by Vitamin E/Curcumin against altered thyroid hormone induced oxidative stress via NFĸB/AKT/mTOR/KEAP1 signaling in rat heart. Scientific Reports. 2019; 9: 7408.
-  Hati AK, Paital B, Sahoo AR, Shankar U. A case study for successful treatment of vitiligo with a constitutional homoeopathic formulation Calcareacarbonica. Indo American Journal of Pharmaceutical Sciences. 2018; 05: 299–303.
-  Hati AK, Paital B, Naik KN, Mishra AK, Chainy GBN, Nanda LK. Constitutional, organopathic and combined homeopathic treatment of benign prostatic hypertrophy: a clinical trial. Homeopathy. 2012; 101: 217–223.
-  Hati AK, Rath S, Nayak C, Raj I, Sahoo AR, Paital B. Successful treatment of ureteric calculi with constitutional homoeopathic medicine Lycopodium clavatum: a case report. Journal of Drug Delivery and Therapeutics. 2018; 8: 1–7.
-  Sahoo AR, Paital B, Taneja D, Hati AK. Knowledge, attitude and practice of Anganwadi workers on homoeopathic formulations. Indo American Journal of Pharmaceutical Research. 2017; 7: 574–581.
-  Sahoo AR, Barik B, Hati AK, Paital B. Right potency matters: a case report of homoeopathic treatment of verruca palmaris. Homœopathic Links. 2018; 31: 204–208.
-  Sahoo AR, Nayak C, Hati AK, Rath S, Paital B. A review on research evidences in homoeopathy for urinary tract infection. World Journal of Pharmaceutical Research. 2018; 7: 185–200.
-  Raja M, Nayak C, Paital B, Rath P, Moorthy K, Raj S, et al. Randomized trial on weight and lipid profile of obese by formulation from Garcina cambogia. Medical Sciences. 2020; 24: 1000–1009.
-  Paital B, Hati AK, Naik KN, Mishra AK, Nanda LK, Chainy GBN. Re: editorial comment on constitutional, organopathic and combined homeopathic treatment of begin prostatic hypertrophy: a clinical trial. The Journal of Urology. 2014; 190: 1818–1819.
-  Afiqah RN, Paital B, Kumar S, Majeed ABA, Tripathy M. AgNO3 dependant modulation of glucose mediated respiration kinetics inEscherichia coliat different pH and temperature. Journal of Molecular Recognition. 2016; 29: 544–554.
-  Subudhi U, Das K, Paital B, Bhanja S, Chainy GBN. Alleviation of enhanced oxidative stress and oxygen consumption of L-thyroxin induced hyperthyroid rat liver mitochondria by vitamin E and curcumin. Chemico-Biological Interactions. 2008; 173: 105–114.
-  Subudhi U, Das K, Paital B, Bhanja S, Chainy GBN. Supplementation of curcumin and vitamin E enhances oxidative stress, but restores hepatic histoarchitecture in hypothyroid rats. Life Sciences. 2009; 84: 372–379.
-  Paital B. Nutraceutical values of fish demand their ecological genetic studies: a short review. The Journal of Basic and Applied Zoology. 2018; 79: 16.
-  Paital B, HAti A, Prusrty U, Prusty U, Panda F. Importance of diet/nutrition and regimen in homoeopathic treatment. Journal of Drug Delivery and Therapeutics. 2019; 9: 575–583.
-  Pradhan M, Guru P, Paital B. Daily dietary nutrition and nutraceutical intake in agricultural laborers of Hirakud command area, Sambalpur, Odisha, India. Journal of Drug Delivery and Therapeutics. 2019; 9: 56–61.
-  Kumar S. An insight into molecular interaction of PGIP with PG for banana cultivar. Frontiers in Bioscience (Landmark edition). 2020; 25: 335–362.
-  Paital B, Kumar S, Farmer R, Tripathy NK, Chainy GBN. In silico prediction and characterization of 3D structure and binding properties of catalase from the commercially important crab, Scylla serrata. Interdisciplinary Sciences: Computational Life Sciences. 2011; 3: 110–120.
-  Paital B, Kumar S, Farmer R, Chainy GB. In silico prediction of 3D structure of superoxide dismutase of Scylla serrata and its binding properties with inhibitors. The Journal Interdisciplinary Sciences-Computational Life Sciences. 2013; 5: 69–76.
-  Paital B, Sablok G, Kumar S, Singh SK, Chainy GBN. Investigating the conformational structure and potential site interactions of SOD inhibitors on Ec-SOD in marine mud crab Scylla serrata: a molecular modeling approach. Interdisciplinary Sciences: Computational Life Sciences. 2016; 8: 312–318.
-  Luo H, Chen J, Shi L, Mikailov M, Zhu H, Wang K, et al. DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical-protein interactome. Nucleic Acids Research. 2011; 39: W492–W498.
-  Singh S, Sablok G, Farmer R, Singh AK, Gautam B, Kumar S. Molecular dynamic simulation and inhibitor prediction of cysteine synthase structured model as a potential drug target for trichomoniasis. BioMed Research International. 2013; 2013: 1–15.
-  Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P. Drug target identification using side-effect similarity. Science. 2008; 321: 263–266.
-  Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology. 2008; 4: 682–690.
-  Medina-Franco JL, Giulianotti MA, Welmaker GS, Houghten RA. Shifting from the single to the multitarget paradigm in drug discovery. Drug Discovery Today. 2013; 18: 495–501.
-  Anighoro A, Bajorath J, Rastelli G. Polypharmacology: challenges and opportunities in drug discovery. Journal of Medicinal Chemistry. 2014; 57: 7874–7887.
-  Roth BL, Sheffler DJ, Kroeze WK. Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nature Reviews Drug Discovery. 2004; 3: 353–359.
-  Paolini GV, Shapland RHB, van Hoorn WP, Mason JS, Hopkins AL. Global mapping of pharmacological space. Nature Biotechnology. 2006; 24: 805–815.
-  Yıldırım MA, Goh K, Cusick ME, Barabási A, Vidal M. Drug-target network. Nature Biotechnology. 2007; 25: 1119–1126.
-  Besnard J, Ruda GF, Setola V, Abecassis K, Rodriguiz RM, Huang X, et al. Automated design of ligands to polypharmacological profiles. Nature. 2012; 492: 215–220.
-  Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J, Jenkins JL, et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature. 2012; 486: 361–367.
-  Zhang C, Hong H, Mendrick DL, Tang Y, Cheng F. Biomarker-based drug safety assessment in the age of systems pharmacology: from foundational to regulatory science. Biomarkers in Medicine. 2015; 9: 1241–1252.
-  Zheng M, Liu X, Xu Y, Li H, Luo C, Jiang H. Computational methods for drug design and discovery: focus on China. Trends in Pharmacological Sciences. 2013; 34: 549–559.
-  Chen X, Yan CC, Zhang X, Zhang X, Dai F, Yin J, et al. Drug-target interaction prediction: databases, web servers and computational models. Briefings in Bioinformatics. 2016; 17: 696–712.
-  Lavecchia A, Cerchia C. In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discovery Today. 2016; 21: 288–298.
-  Rognan D. Structure-based approaches to target fishing and ligand profiling. Molecular Informatics. 2010; 29: 176–187.
-  Fazil MH, Kumar S, Subbarao N, Pandey HP, Singh DV. Homology modeling of a sensor histidine kinase from Aeromonas hydrophila. Journal of Molecular Modeling. 2010; 16: 1003–1009.
-  Fazil MHUT, Kumar S, Farmer R, Pandey HP, Singh DV. Binding efficiencies of carbohydrate ligands with different genotypes of cholera toxin B: molecular modeling, dynamics and docking simulation studies. Journal of Molecular Modeling. 2012; 18: 1–10.
-  Tandon G, Jaiswal S, Iquebal MA, Kumar S, Kaur S, Rai A, et al. Evidence of salicylic acid pathway with EDS1 and PAD4 proteins by molecular dynamics simulation for grape improvement. Journal of Biomolecular Structure and Dynamics. 2015; 33: 2180–2191.
-  Waszkowycz B, Clark DE, Gancia E. Outstanding challenges in protein-ligand docking and structure-based virtual screening. Wiley Interdisciplinary Reviews: Computational Molecular Science. 2011; 1: 229–259.
-  Ma D, Chan DS, Leung C. Drug repositioning by structure-based virtual screening. Chemical Society Reviews. 2013; 42: 2130.
-  Li Y, Han L, Liu Z, Wang R. Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results. Journal of Chemical Information and Modeling. 2014; 54: 1717–1736.
-  Liu Z, Su M, Han L, Liu J, Yang Q, Li Y, et al. Forging the basis for developing protein-ligand interaction scoring functions. Accounts of Chemical Research. 2017; 50: 302–309.
-  Chen YZ, Zhi DG. Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins: Structure, Function, and Genetics. 2001; 43: 217–226.
-  Tang Y, Zhu W, Chen K, Jiang H. New technologies in computer-aided drug design: toward target identification and new chemical entity discovery. Drug Discovery Today. Technologies. 2014; 3: 307–313.
-  Pinzi L, Rastelli G. Molecular docking: shifting paradigms in drug discovery. International Journal of Molecular Sciences. 2019; 20: 4331.
-  Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, et al. TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acids Research. 2006; 34: W219–W224.
-  Yang S. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discovery Today. 2010; 15: 444–450.
-  Koes DR, Camacho CJ. Pharmer: efficient and exact pharmacophore search. Journal of Chemical Information and Modeling. 2011; 51: 1307–1314.
-  Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. Journal of Chemical Information and Modeling. 2005; 45: 160–169.
-  Dixon SL, Smondyrev AM, Rao SN. PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chemical Biology & Drug Design. 2006; 67: 370–372.
-  Wang X, Shen Y, Wang S, Li S, Zhang W, Liu X, et al. PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Research. 2017; 45: W356–W360.
-  Koes DR, Camacho CJ. ZINCPharmer: pharmacophore search of the ZINC database. Nucleic Acids Research. 2012; 40: W409–W414.
-  Xu Y, Wang S, Hu Q, Gao S, Ma X, Zhang W, et al. CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction. Nucleic Acids Research. 2018; 46: W374–W379.
-  Sunseri J, Koes DR. Pharmit: interactive exploration of chemical space. Nucleic Acids Research. 2016; 44: W442–W448.
-  Schneidman-Duhovny D, Dror O, Inbar Y, Nussinov R, Wolfson HJ. PharmaGist: a webserver for ligand-based pharmacophore detection. Nucleic Acids Research. 2008; 36: W223–W228.
-  Koes DR, Camacho CJ. ZINCPharmer: pharmacophore search of the ZINC database. Nucleic Acids Research. 2012; 40: W409–W414.
-  Koes DR, Camacho CJ. Pharmer: efficient and exact pharmacophore search. Journal of Chemical Information and Modeling. 2011; 51: 1307–1314.
-  Willett P, Barnard JM, Downs GM. Chemical Similarity Searching. Journal of Chemical Information and Computer Sciences. 1998; 38: 983–996.
-  Wang C, Kurgan L. Survey of similarity-based prediction of drug-protein interactions. Current Medicinal Chemistry. 2019; 27: 5856–5886.
-  Hu G, Kuang G, Xiao W, Li W, Liu G, Tang Y. Performance evaluation of 2D fingerprint and 3D shape similarity methods in virtual screening. Journal of Chemical Information and Modeling. 2012a; 52: 1103–1113.
-  Willett P. Similarity-based virtual screening using 2D fingerprints. Drug Discovery Today. 2006; 11: 1046–1053.
-  Gong J, Cai C, Liu X, Ku X, Jiang H, Gao D, et al. ChemMapper: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method. Bioinformatics. 2013; 29: 1827–1829.
-  Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK. Relating protein pharmacology by ligand chemistry. Nature Biotechnology. 2007; 25: 197–206.
-  Ding H, Takigawa I, Mamitsuka H, Zhu S. Similarity-based machine learning methods for predicting drug–target interactions: a brief review. Briefings in Bioinformatics. 2014; 15: 734–747.
-  Chen Y, Tripathi LP, Mizuguchi K. An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework. Database. 2016; 2016: baw009.
-  Tian K, Shao M, Wang Y, Guan J, Zhou S. Boosting compound-protein interaction prediction by deep learning. Methods. 2018; 110: 64–72.
-  Wen M, Zhang Z, Niu S, Sha H, Yang R, Yun Y, et al. Deep-learning-based drug-target interaction prediction. Journal of Proteome Research. 2017; 16: 1401–1409.
-  Hwang H, Dey F, Petrey D, Honig B. Structure-based prediction of ligand-protein interactions on a genome-wide scale. Proceedings of the National Academy of Sciences of the United States of America. 2018; 114: 13685–13690.
-  Cheng F, Zhou Y, Li J, Li W, Liu G, Tang Y. Prediction of chemical-protein interactions: multitarget-QSAR versus computational chemogenomic methods. Molecular BioSystems. 2012c; 8: 2373.
-  Yu H, Chen J, Xu X, Li Y, Zhao H, Fang Y, et al. A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PLoS one. 2012; 7: e37608.
-  Chen R, Liu X, Jin S, Lin J, Liu J. Machine learning for drug-target interaction prediction. Molecules. 2018; 23: 2208.
-  Zhao J, Jiang P, Zhang W. Molecular networks for the study of TCM Pharmacology. Briefings in Bioinformatics. 2010; 11: 417–430.
-  Cheng F, Zhou Y, Li W, Liu G, Tang Y. Prediction of chemical-protein interactions network with weighted network-based inference method. PLoS one. 2012d; 7: e41064.
-  Wu Z, Lu W, Wu D, Luo A, Bian H, Li J, et al. In silico prediction of chemical mechanism of action via an improved networkbased inference method. British Journal of Pharmacology. 2016; 173: 3372–3385.
-  Wu Z, Cheng F, Li J, Li W, Liu G, Tang Y. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug–target interactions and drug repositioning. Briefings in Bioinformatics. 2016; 4: bbw012.
-  Cheng F, Liu C, Jiang J, Lu W, Li W, Liu G, et al. Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Computational Biology. 2012b; 8: e1002503.
-  Cheng F, Zhou Y, Li J, Li W, Liu G, Tang Y. Prediction of chemical-protein interactions: multitarget-QSAR versus computational chemogenomic methods. Molecular BioSystems. 2012c; 8: 2373.
-  Chen X, Liu M, Yan G. Drug-target interaction prediction by random walk on the heterogeneous network. Molecular BioSystems. 2012; 8: 1970.
-  Tobinick EL. The value of drug repositioning in the current pharmaceutical market. Drug News & Perspectives. 2009; 22: 119.
-  Aronson JK. An agenda for research on adverse drug reactions. British Journal of Clinical Pharmacology. 2007; 64: 119–121.
-  Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Science Translational Medicine. 2011; 3: 96ra77.
-  Naylor S, Schonfeld JM. Therapeutic drug repurposing, repositioning and rescue-part 1: overview. Drug Discovery World. 2014; 49.
-  Dudley JT, Deshpande T, Butte AJ. Exploiting drug-disease relationships for computational drug repositioning. Briefings in Bioinformatics. 2011; 12: 303–311.
-  Cockell SJ, Weile J, Lord P, Wipat C, Andriychenko D, Pocock M, et al. An integrated dataset for in silico drug discovery. Journal of Integrative Bioinformatics. 2010; 7: 15–27.
-  Wu Z, Wang Y, Chen L. Network-based drug repositioning. Molecular BioSystems. 2013; 9: 1268–1281.
-  Brown AS, Kong SW, Kohane IS, Patel CJ. ksRepo: a generalized platform for computational drug repositioning. BMC Bioinformatics. 2016; 17: 78.
-  Chen H, Zhang H, Zhang Z, Cao Y, Tang W. Network-based inference methods for drug repositioning. Computational and Mathematical Methods in Medicine. 2015; 2015: 130620.
-  Ye H, Liu Q, Wei J. Construction of drug network based on side effects and its application for drug repositioning. PLoS One. 2014; 9: e87864.
-  Zou J, Zheng M, Li G, Su Z. Advanced systems biology methods in drug discovery and translational biomedicine. BioMed Research International. 2013; 2013: 1–8.
-  Setoain J, Franch M, Martínez M, Tabas-Madrid D, Sorzano CO, Bakker A, et al. NFFinder: an online bioinformatics tool for searching similar transcriptomics experiments in the context of drug repositioning. Nucleic Acids Research. 2015; 43: W193–W199.
-  Younis W, Thangamani S, Seleem M. Repurposing non-antimicrobial drugs and clinical molecules to treat bacterial infections. Current Pharmaceutical Design. 2015; 21: 4106–4111.
-  Li YY, Jones SJ. Drug repositioning for personalized medicine. Genome Medicine. 2012; 4: 27.
-  Schadt EE. The changing privacy landscape in the era of big data. Molecular Systems Biology. 2012; 8: 612.
-  Ma’ayan A, Rouillard AD, Clark NR, Wang Z, Duan Q, Kou Y. Lean big data integration in systems biology and systems pharmacology. Trends in Pharmacological Sciences. 2014; 35: 450–460.
-  Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research. 2018; 46: D1074–D1082.
-  Li YH, Yu CY, Li XX, Zhang P, Tang J, Yang Q, et al. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Research. 2018; 46: D1121–D1127.
-  Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Research. 2016; 44: D1045–D1053.
-  Ahmed A, Smith RD, Clark JJ, Dunbar JB, Carlson HA. Recent improvements to Binding MOAD: a resource for protein-ligand binding affinities and structures. Nucleic Acids Research. 2015; 43: D465–D469.
-  Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, et al. The ChEMBL database in 2017. Nucleic Acids Research. 2017; 45: D945–D954.
-  Ursu O, Holmes J, Knockel J, Bologa CG, Yang JJ, Mathias SL, et al. DrugCentral: online drug compendium. Nucleic Acids Research. 2017; 45: D932–D939.
-  Harding SD, Sharman JL, Faccenda E, Southan C, Pawson AJ, Ireland S, et al. The IUPHAR/BPS guide to pharmacology in 2018: updates and expansion to encompass the new guide to immune-pharmacology. Nucleic Acids Research. 2018; 46: D1091–D1106.
-  Liu Z, Li Y, Han L, Li J, Liu J, Zhao Z, et al. PDB-wide collection of binding data: current status of the PDBbind database. Bioinformatics. 2015; 31: 405–412.
-  Roth BL, Lopez E, Patel S, Kroeze WK. The multiplicity of serotonin receptors: uselessly diverse molecules or an embarrassment of riches? The Neuroscientist. 2000; 6: 252–262.
-  Wang Y, Bryant SH, Cheng T, Wang J, Gindulyte A, Shoemaker BA, et al. PubChem bioassay: 2017 update. Nucleic Acids Research. 2017; 45: D955–D963.
-  Rose PW, Prlić A, Altunkaya A, Bi C, Bradley AR, Christie CH, et al. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Research. 2017; 45: D271–D281.
-  Hecker N, Ahmed J, von Eichborn J, Dunkel M, Macha K, Eckert A, et al. SuperTarget goes quantitative: update on drug-target interactions. Nucleic Acids Research. 2012; 40: D1113–D1117.
-  Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Research. 2016; 44: D380–D384.
-  Magariños MP, Carmona SJ, Crowther GJ, Ralph SA, Roos DS, Shanmugam D, et al. TDR Targets: a chemogenomics resource for neglected diseases. Nucleic Acids Research. 2012; 40: D1118–D1127.
-  O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open babel: an open chemical toolbox. Journal of Cheminformatics. 2011; 3: 33.
-  Yap CW. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry. 2011; 32: 1466–1474.
-  Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Research. 2017; 45: D353–D361.
-  Davis AP, Grondin CJ, Johnson RJ, Sciaky D, King BL, McMorran R, et al. The comparative toxicogenomics database: update 2017. Nucleic Acids Research. 2017; 45: D972–D978.
-  Kuhn M, Letunic I, Jensen LJ, Bork P. The SIDER database of drugs and side effects. Nucleic Acids Research. 2016; 44: D1075–D1079.
-  Tatonetti NP, Ye PP, Daneshjou R, Altman RB. Data-driven prediction of drug effects and interactions. Science Translational Medicine. 2012; 4: 125ra31.
-  The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Research. 2017; 45: D158–D169.
-  Cheng F, Li W, Wu Z, Wang X, Zhang C, Li J, et al. Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. Journal of Chemical Information and Modeling. 2013c; 53: 753–762.
-  Mizutani S, Pauwels E, Stoven V, Goto S, Yamanishi Y. Relating drug-protein interaction network with drug side effects. Bioinformatics. 2012; 28: i522–i528.
-  Hsiao W, Liu L. The role of traditional chinese herbal medicines in cancer therapy-from TCM theory to mechanistic insights. Planta Medica. 2010; 76: 1118–1131.
-  Borkow G, Lapidot A. Multi-targeting the entrance door to block HIV-1. Current Drug Targets. Infectious Disorders. 2005; 5: 3–15.
-  Tu Y. The discovery of artemisinin (qinghaosh) and gifts from Chinese medicine. Nature Medicine. 2011; 17: 1217–1220.
-  Mohapatra AD, Kumar S, Satapathy AK, Ravindran B. Caspase dependent programmed cell death in developing embryos: a potential target for therapeutic intervention against pathogenic nematodes. PLoS Neglected Tropical Diseases. 2011; 5: e1306.
-  Miller LH, Siu X. Artemisinin: discovery from the Chinese herbal garden. Nature Medicine. 2011; 146: 855–858.
-  Li G, Guo X, Arnold K, Jian H, Fu L. Randomised comparative study of mefloquine, qinghaosu, and pyrimethamine-sulfadoxine in patients with falciparum malaria. The Lancet. 1984; 324: 1360–1361.
-  Tian X, Liu L. Drug discovery enters a new era with multi-target intervention strategy. Chinese Journal of Integrative Medicine. 2013; 18: 539–542.
-  Liang L, Wong JH, Lou XY. Methodological approach for pharmacological research of Chinese herbal formulas through investigation of the formulas on anti-gastric ulcers in rats. Journal of Traditional Chinese Medicine. 1985; 2: 50–53.
-  Wu Z, Lu W, Yu W, Wang T, Li W, Liu G, et al. Quantitative and systems pharmacology. In silico polypharmacology of G protein-coupled receptor ligands via network-based approaches. Pharmacological Research. 2018; 129: 400–413.
-  Fang J, Wu Z, Cai C, Wang Q, Tang Y, Cheng F. Quantitative and systems pharmacology. 1. In silico prediction of drug-target interaction of natural products enables new targeted cancer therapy. Journal of Chemical Information and Modeling. 2017b; 57: 2657–2671.
-  Yu W, Gwinn M, Clyne M, Yesupriya A, Khoury MJ. A navigator for human genome epidemiology. Nature Genetics. 2008; 40: 124–125.
-  Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Research. 2015; 43: D789–D798.
-  Hewett M, Oliver DE, Rubin DL, Easton KL, Stuart JM, Altman RB, et al. PharmGKB: the pharmacogenetics knowledge base. Nucleic Acids Research. 2002; 30: 163–165.
-  Smoot ME, Ono K, Ruscheinski J, Wang P-, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011; 27: 431–432.
-  Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America. 2005; 102: 15545–15550.
-  Wells JA, McClendon CL. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature. 2007; 450: 1001–1009.
-  Jubb H, Blundell TL, Ascher DB. Flexibility and small pockets at protein-protein interfaces: New insights into druggability. Progress in Biophysics and Molecular Biology. 2015; 119: 2–9.
-  Prathipati P, Mizuguchi K. Systems biology approaches to a rational drug discovery paradigm. Current Topics in Medicinal Chemistry. 2015; 16: 1009–1025.
-  Tuncbag N, Kar G, Keskin O, Gursoy A, Nussinov R. A survey of available tools and web servers for analysis of protein-protein interactions and interfaces. Briefings in Bioinformatics. 2008; 10: 217–232.
-  Seet BT, Dikic I, Zhou M, Pawson T. Reading protein modifications with interaction domains. Nature Reviews Molecular Cell Biology. 2006; 7: 473–483.
-  Duan G, Walther D. The roles of post-translational modifications in the context of protein interaction networks. PLoS Computational Biology. 2015; 11: e1004049.
-  Acuner Ozbabacan SE, Engin HB, Gursoy A, Keskin O. Transient protein-protein interactions. Protein Engineering Design and Selection. 2011; 24: 635–648.
-  Lua RC, Marciano DC, Katsonis P, Adikesavan AK, Wilkins AD, Lichtarge O. Prediction and redesign of protein-protein interactions. Progress in Biophysics and Molecular Biology. 2014; 116: 194–202.
-  Mészáros B, Simon I, Dosztányi Z. Prediction of protein binding regions in disordered proteins. PLoS Computational Biology. 2009; 5: e1000376.
-  Babu MM, Kriwacki RW, Pappu RV. Versatility from protein disorder. Science. 2012; 337: 1460–1461.
-  Razick S, Magklaras G, Donaldson IM. IRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics. 2008; 9: 405.
-  De Las Rivas J, Fontanillo C. Protein-protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Computational Biology. 2010; 6: e1000807.
-  Schaefer MH, Lopes TJS, Mah N, Shoemaker JE, Matsuoka Y, Fontaine J, et al. Adding protein context to the human protein-protein interaction network to reveal meaningful interactions. PLoS Computational Biology. 2013; 9: e1002860.
-  Keskin O, Tuncbag N, Gursoy A. Predicting protein-protein interactions from the molecular to the proteome level. Chemical Reviews. 2016; 116: 4884–4909.
-  Janin J, Henrick K, Moult J, Eyck LT, Sternberg MJE, Vajda S, et al. CAPRI: a critical assessment of predicted interactions. Proteins: Structure, Function, and Genetics. 2003; 52: 2–9.
-  Berman H, Henrick K, Nakamura H, Markley JL. The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data. Nucleic Acids Research. 2007; 35: D301–D303.
-  Hosur R, Xu J, Bienkowska J, Berger B. IWRAP: an interface threading approach with application to prediction of cancer-related protein-protein interactions. Journal of Molecular Biology. 2011; 405: 1295–1310.
-  Enright AJ, Iliopoulos I, Kyrpides NC, Ouzounis CA. Protein interaction maps for complete genomes based on gene fusion events. Nature. 1999; 402: 86–90.
-  Marcotte EM. Detecting protein function and protein-protein interactions from genome sequences. Science. 1999; 285: 751–753.
-  Sato T, Yamanishi Y, Kanehisa M, Horimoto K, Toh H. Improvement of the mirror tree method by extracting evolutionary information. In: Sequence and genome analysis: method and applications. Concept Press. 2011; 129–139.
-  Yamada M, Kabir MS, Tsunedomi R. Divergent promoter organization may be a preferred structure for gene control in Escherichia coli. Journal of Molecular Microbiology and Biotechnology. 2004; 6: 206–210.
-  Grigoriev A. A relationship between gene expression and protein interactions on the proteome scale: analysis of the bacteriophage T7 and the yeast Saccharomyces cerevisiae. Nucleic Acids Research. 2001; 29: 3513–3519.
-  Rao VS, Srinivas K, Sujini GN, Kumar GNS. Protein-protein interaction detection: methods and analysis. International Journal of Proteomics. 2014; 2014: 1–12.
-  Janin J, Wodak S. The third CAPRI assessment meeting Toronto, Canada, April 20–21, 2007. Structure. 2007; 15: 755–759.
-  Rider AK, Chawla NV, Emrich SJA. Survey of current integrative network algorithms for systems biology. In: Systems biology: integrative biology and simulation tools (Prokop A, Csuk B, eds). Dordrecht: Springer Netherlands. 2013; 479–495.
-  Dai Y, Zhao X. A Survey on the computational approaches to identify drug targets in the postgenomic era. BioMed Research International. 2015; 2015: 1–9.
-  Frijters R, van Vugt M, Smeets R, van Schaik R, de Vlieg J, Alkema W. Literature mining for the discovery of hidden connections between drugs, genes and diseases. PLoS Computational Biology. 2010; 6: e1000943.
-  Yang H, Ju J, Wong Y, Shmulevich I, Chiang J. Literature-based discovery of new candidates for drug repurposing. Briefings in Bioinformatics. 2016; 2014: bbw030.