A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data

By Hua Yu, Jianxin Chen, Xue Xu, Yan Li, Huihui Zhao, Yupeng Fang, Xiuxiu Li, W. Zhou, Wei Wang, Yonghua Wang. PLoS ONE, 2012

381 citations recorded by the source corpus.

In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/d73119fb79ee0487f875a581985e787fce5f60f4

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Citations in the corpus

TCMSP: a database of systems pharmacology for drug discovery from herbal medicines (2014, methodology): “For those compounds without validated targets, the SysDT model constructed in our previous work [27] was used to predict the potential targets of a compound.”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, neutral): “This have been previously proposed by authors in [ 5, 131, 153, 302, 303].”
Artificial intelligence in drug discovery and development (2020, positive): “Also, these modes were capable of predicting drug–target associations that could be further extended to target–disease and target–target associations, thereby speeding up the drug discovery process [61].”
Machine learning with operational costs (2011, background): “• Pharmaceutical companies choosing a subset of possible drug targets to test, where the drugs are predicted to be effective, and cannot be overly expensive to produce (Yu et al., 2012).”
Toward more realistic drug–target interaction predictions (2014, positive): “…with another widely used machine learning prediction model, random forests (RFs), to test whether the observations made with the Kronecker RLS generalized Yu et al. [31], where each drug–target pair was represented as a concatenation of drug and target similarity vectors (see Supplementary”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “…network- or machine learningbased clustering (Hopkins et al., 2006; Hopkins, 2008; Chen et al., 2009e; Xiong et al., 2010; Yang et al., 2010; Hu et al., 2011; Takigawa et al., 2011; Yabuuchi et al., 2011; Cheng et al., 2012a, 2012b; Lee et al., 2012c; Nacher & Schwartz, 2012; Yu et al., 2012).”
BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (2016, methodology): “Liu et al. and Li et al. both used the method that integrates the chemical, genomic and pharmacological information 19 to predict targets of potential bioactive components of TCM and then analyzed compound-target-disease networks to respectively uncover Licorice’s and Eucommia ulmoides Oliv.’s…”