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.

中文翻译：

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机器学习方法在发现 NDM-1 抑制剂中的应用。

新德里金属β-内酰胺酶（NDM-1）产生菌的出现及其在世界范围内的传播对耐药细菌感染的治疗提出了巨大挑战。这些细菌可以水解大多数β-内酰胺类抗菌剂。不幸的是，没有临床上有用的 NDM-1 抑制剂。在目前的工作中，我们手动收集了过去十年报道的 NDM-1 抑制剂，并建立了第一个 NDM-1 抑制剂数据库。使用收集到的化合物的结构和性质特征作为输入训练集构建了四个机器学习模型，以发现潜在的 NDM-1 抑制剂。为了区分高活性抑制剂和推定的阳性药物，我们的研究中引入了三分类策略。详细，常用的正负分法转换为强活性、弱活性和非活性。基于该策略设计的最佳预测模型的准确率达到了 90.5%，而传统的基于对接的虚拟筛选方法的准确率达到了 69.14%。因此，最好的模型用于虚拟筛选天然产物库。所选化合物的安全性通过基于机器学习的 ADMET 预测模型进行分析。鉴定了7种新型NDM-1抑制剂，这将为NDM-1抑制剂的发现提供有价值的线索。最佳模型用于虚拟筛选天然产物库。所选化合物的安全性通过基于机器学习的 ADMET 预测模型进行分析。鉴定了7种新型NDM-1抑制剂，这将为NDM-1抑制剂的发现提供有价值的线索。最佳模型用于虚拟筛选天然产物库。所选化合物的安全性通过基于机器学习的 ADMET 预测模型进行分析。鉴定了7种新型NDM-1抑制剂，这将为NDM-1抑制剂的发现提供有价值的线索。