Machine learning (ML) is advancing various industries, including drug discovery. We applied ML for predicting compounds with anti-SARS activity, which can be used for drug discovery projects related with COVID-19.

OTAVAchemicals offers Machine Learning (ML) SARS Targeted Library, which contain 1472 compounds with predicted activity against SARS-CoV-2.The ML SARS Targeted Library has been carefully designed using machine learning (artificial neural networks and Bayesian statistics).

For machine learning 306 compounds with anti-SARS activity were randomly divided into two equal groups. Each group had the same amounts of active and inactive compounds and was used as training and test set. The training sets were used for development of Bayesian and artificial neural networks models. Both methods were based on a number of different molecular descriptors - fingerprints, molecular weight, number of hydrogen acceptors and donors, number of rings, number of rotatable bonds, LogP, PSA, topological descriptors and other. The test sets were used for validation of all models.

Example of compounds from training set for machine learning and their anti-SARS activities

Virtual screenings of Drug-like Green Collection toward best Bayesian and artificial neural networks models were performed. Top-scored compounds, selected by machine learning methods, were visually analyzed. The application of number different artificial neural networks and models (21 in total) should allow increasing the number of active compounds identified during screenings.

Example of compounds selected by machine learning

The designed ML SARS Targeted Library comprises only drug-like compounds (PAINS compounds are filtered off). The library is intended for screening projects to find new compounds with activity against SARS-CoV-2.

The summary of the ML SARS Targeted Library characteristics (average values):

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