Abstract
The results of directly comparing the prediction accuracy of optimized 3D Quantitative Structure-Activity Relationship (3D-QSAR) models and linear Support Vector Machine (SVM) classifiers to identify small molecule inhibitors of the BRAF-V600E and HIV Integrase targets are reported. Performance comparisons were carried out using 303 compounds (68 active) against BRAF-V600E and 204 compounds (159 active) against HIV Integrase. A SVM prediction accuracy of 95% (BRAF-V600E) and 100% (HIV Integrase) and 3D-QSAR prediction accuracy of 76% (BRAFV600E) and 82% (HIV Integrase) was observed. To help explain the better performance of SVM in the comparison reported here and to help assess the degree to which a SVM or 3D-QSAR model is likely to perform best for other targetligands of interest a new EPP (Expected Predictive Performance) metric is introduced. How EPP can be used to help predict future performance of SVM and 3D-QSAR models by quantifying the degree of similarity between candidate compounds and training data is also demonstrated. Results show that the EPP metric is capable of predicting future prediction accuracy of SVM and 3D-QSAr models within 7% of actual performance.
Highlights
The drug discovery and development process is highly inefficient, risky and complex (DiMasi et al, 2015; Lamberti and Getz, 2015)
The comparison of 3D Quantitative Structure-Activity Relationship (3D-QSAR) and Support Vector Machine (SVM) classifiers to predict the inhibitory activity of small molecule compounds was carried out on two different targets: BRAF-V600E and HIV Integrase. 3D-QSAR modeling and prediction was conducted in a Windows 7 environment using the Molecular Operating Environment (MOE, 2015) version 2014.0901 software product from the Chemical Computing Group
Observed results indicate that the SVM classifier performed over 15% better than the 3D-QSAR classifier for both targets
Summary
The drug discovery and development process is highly inefficient, risky and complex (DiMasi et al, 2015; Lamberti and Getz, 2015). Despite the use of High Throughput Screening (HTS) to help address efficiency concerns, drug failure rates and inefficiencies remain unacceptably high (Torfinn, 2014). The ML methods used in HTS carry out virtual screening by training supervised classifiers or regression-based methods to predict affinity and activity interactions between targets and candidate compounds. A subset of the candidate compounds that are predicted to be active and at times a smaller subset of compounds predicted to be not active, have bioassay tests conducted to confirm or refute predictions. This ML-based virtual screening process helps improve
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