MOST: most-similar ligand based approach to target prediction

Abstract

BackgroundMany computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power.ResultsHere we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi ≥ 5, and 0.87 for pKi ≥ 6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi ≥ 5, and 0.76 for pKi ≥ 6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone. In the case of aloe-emodin’s laxative effect, MOST predicted that acetylcholinesterase was the mechanism-of-action target; in vivo studies validated this prediction.ConclusionsUsing the MOST approach can result in highly accurate and robust target prediction. Integrated with a FDR control procedure, MOST provides a reliable framework for multiple-target inference. It has prospective applications in drug repurposing and mechanism-of-action target prediction.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1586-z) contains supplementary material, which is available to authorized users.

Associating 197 Chinese herbal medicine with drug targets and diseases using the similarity ensemble approach

“…For instance, Zobir et al studied the mode of action of 45 TCM therapeutic action classes by in silico target prediction algorithms, of which the targets were annotated with the Kyoto Encyclopedia of Genes and Genomes pathway [ 14 ]. Huang et al used a most-similar ligand-based approach to predict the mechanism of action targets of aloe-emodin discovered from phenotypic screening and traditional medicine [ 15 ]. However, the systems pharmacology of individual herbs or herbal formulas remains largely elusive, which to some extent has hindered modern herbal drug development.…”

Uncovering the Mechanisms of Chinese Herbal Medicine (MaZiRenWan) for Functional Constipation by Focused Network Pharmacology Approach

“…Secondly, a computational method, namely MOST ( Huang et al, 2017 ), was used to predict the possible targets of representative compounds. The machine learning models of MOST were trained by datasets from CHEMBL19 database ( Gaulton et al, 2012 ).…”

“…The targets of MZRW representative compounds were predicted by the MOST method ( Huang et al, 2017 ). Based on the bioactivity type, the pK i , pIC 50 , and pEC 50 datasets were generated from the CHEMBL20 database ( Gaulton et al, 2012 ) by using procedures described in previous work ( Huang et al, 2017 ). The machine learning model was training with dataset from CHEMBL19 and the performance of prediction were summarized in Supplementary Table S8 .…”

Predicted Biological Activity of Purchasable Chemical Space

“…We adopted a statistical significance threshold of negative log SEA p-value 54 (pSEA) ≥ 40 and a MaxTc cutoff ≥0.40 guided by the work on belief theory from the Abbvie group. 34 MaxTc is complementary to pSEA as it provides a single-nearest-neighbor-molecule view of similarity, compared to SEA’s global view arising from the ensemble of annotated ligands. To quantify how this bivariate threshold improves predictive capability, we evaluated the performance of SEA, SEA+TC, and a Naïve-Bayesian classifier (NBC) via 5-fold cross-validation of ChEMBL’s bioactivity data set (version 21; Figure 1 ).…”