ASCoVPred: a machine learning-based platform for quantitative prediction of anti-SARS-CoV-2 activity and human cell toxicity of molecules

Abstract

There is an urgent need to accelerate the discovery of effective drugs for COVID-19. We have developed machine learning models for rapid discovery of molecules potentially inhibitory to SARS-CoV-2 and negligible or no human cell toxicity. The machine learning (ML) QSAR models were trained and optimized with features (descriptors and fingerprints) of the experimentally validated SARS-CoV-2 inhibitory compounds. Several molecular descriptors and fingerprints were calculated to select the decisive ones for the training and evaluation of thousands of ML models. The best-optimized models are deployed as ASCoVPred webserver and standalone software, that provides easy and free access to the models. The feature selection for selecting the best descriptors for ML models training helped identify a set of decisive descriptors and fingerprints that correlate positively or negatively with the anti-SARS-CoV-2 activity and toxicity of the compounds. Systematic prediction and optimization of compounds with the help of ASCoVPred can facilitate the discovery of novel anti-SARS-CoV-2 compounds. The ASCoVPred web server and standalone software are freely available at http://14.139.62.220/ascovpred/.