Details

Autor(en) / Beteiligte

• Rahman, A. S. M. Zisanur
• Liu, Chengyou
• Sturm, Hunter
• Hogan, Andrew M
• Davis, Rebecca
• Hu, Pingzhao
• Cardona, Silvia T
• Bollenbach, Tobias

Titel

A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery

Ist Teil von

• PLoS computational biology, 2022-10, Vol.18 (10), p.e1010613-e1010613

Ort / Verlag

San Francisco: Public Library of Science

Erscheinungsjahr

2022

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Screening for novel antibacterial compounds in small molecule libraries has a low success rate. We applied machine learning (ML)-based virtual screening for antibacterial activity and evaluated its predictive power by experimental validation. We first binarized 29,537 compounds according to their growth inhibitory activity (hit rate 0.87%) against the antibiotic-resistant bacterium Burkholderia cenocepacia and described their molecular features with a directed-message passing neural network (D-MPNN). Then, we used the data to train an ML model that achieved a receiver operating characteristic (ROC) score of 0.823 on the test set. Finally, we predicted antibacterial activity in virtual libraries corresponding to 1,614 compounds from the Food and Drug Administration (FDA)-approved list and 224,205 natural products. Hit rates of 26% and 12%, respectively, were obtained when we tested the top-ranked predicted compounds for growth inhibitory activity against B. cenocepacia, which represents at least a 14-fold increase from the previous hit rate. In addition, more than 51% of the predicted antibacterial natural compounds inhibited ESKAPE pathogens showing that predictions expand beyond the organism-specific dataset to a broad range of bacteria. Overall, the developed ML approach can be used for compound prioritization before screening, increasing the typical hit rate of drug discovery.