Applications of machine‐learning methods for the discovery of NDM‐1 inhibitors

Abstract The emergence of New Delhi metal beta‐lactamase (NDM‐1)‐producing bacteria and their worldwide spread pose great challenges for the treatment of drug‐resistant bacterial infections. These bacteria can hydrolyze most β‐lactam antibacterials. Unfortunately, there are no clinically useful NDM‐1 inhibitors. In the current work, we manually collected NDM‐1 inhibitors reported in the past decade and established the first NDM‐1 inhibitor database. Four machine‐learning models were constructed using the structural and property characteristics of the collected compounds as input training set to discover potential NDM‐1 inhibitors. In order to distinguish between high active inhibitors and putative positive drugs, a three‐classification strategy was introduced in our study. In detail, the commonly used positive and negative divisions are converted into strongly active, weakly active, and inactive. The accuracy of the best prediction model designed based on this strategy reached 90.5%, compared with 69.14% achieved by the traditional docking‐based virtual screening method. Consequently, the best model was used to virtually screen a natural product library. The safety of the selected compounds was analyzed by the ADMET prediction model based on machine learning. Seven novel NDM‐1 inhibitors were identified, which will provide valuable clues for the discovery of NDM‐1 inhibitors.