Deep learning-based prediction of chemical accumulation in a pathogenic mycobacterium.

Drugs must accumulate at their target site to be effective, and inadequate uptake of drugs is a substantial barrier to the design of potent therapies. This is particularly true in the development of antibiotics, as bacteria possess numerous barriers to prevent chemical uptake. Designing compounds that circumvent bacterial barriers and accumulate to high levels in cells could dramatically improve the success rate of antibiotic candidates. However, a comprehensive understanding of which chemical structures promote or prevent drug uptake is currently lacking. Here we use liquid chromatography-mass spectrometry to measure accumulation of 1528 approved drugs in Mycobacterium abscessus, a highly drug-resistant, opportunistic pathogen. We find that simple chemical properties fail to effectively predict drug accumulation in mycobacteria. Instead, we use our data to train deep learning models that predict drug accumulation in M. abscessus with high accuracy, including for chemically diverse compounds not included in our original drug library. We find that differential drug uptake is a critical determinant of the efficacy of drugs currently in development and can identify compounds which accumulate well and have antibacterial activity in M. abscessus. These predictive algorithms can be an important complement to chemical synthesis and accumulation assays in the evaluation of drug candidates.