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Combined Machine Learning and Molecular Modelling Workflow for the Recognition of Potentially Novel Fungicides

机译：结合机器学习和分子建模工作流程来识别潜在的新型杀菌剂

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Novel machine learning and molecular modelling filtering procedures for drug repurposing have been carried out for the recognition of the novel fungicide targets of Cyp51 and Erg2. Classification and regression approaches on molecular descriptors have been performed using stepwise multilinear regression (FS-MLR), uninformative-variable elimination partial-least square regression, and a non-linear method called Forward Stepwise Limited Correlation Random Forest (FS-LM-RF). Altogether, 112 prediction models from two different approaches have been built for the descriptor recognition of fungicide hit compounds. Aiming at the fungal targets of sterol biosynthesis in membranes, antifungal hit compounds have been selected for docking experiments from the Drugbank database using the Autodock4 molecular docking program. The results were verified by Gold Protein-Ligand Docking Software. The best-docked conformation, for each high-scored ligand considered, was submitted to quantum mechanics/molecular mechanics (QM/MM) gradient optimization with final single point calculations taking into account both the basis set superposition error and thermal corrections (with frequency calculations). Finally, seven Drugbank lead compounds were selected based on their high QM/MM scores for the Cyp51 target, and three were selected for the Erg2 target. These lead compounds could be recommended for further in vitro studies.
机译：为识别新的Cyp51和Erg2杀真菌目标，已经进行了新的机器学习和分子模型过滤程序进行药物再利用。已经使用逐步多线性回归（FS-MLR），无信息变量消除偏最小二乘回归和称为前向逐步有限相关随机森林（FS-LM-RF）的非线性方法对分子描述符进行了分类和回归方法。总共建立了来自两种不同方法的112个预测模型，用于描述符识别杀真菌剂化合物。针对膜中固醇生物合成的真菌靶标，已使用Autodock4分子对接程序从Drugbank数据库中选择了抗真菌命中化合物进行对接实验。结果通过金蛋白配体对接软件进行了验证。对于每种所考虑的高配位体，将最佳配伍构象提交给量子力学/分子力学（QM / MM）梯度优化，同时考虑基组叠加误差和热校正（通过频率计算），最后进行单点计算）。最后，根据对Cyp51靶标的高QM / MM分数，选择了7种Drugbank前导化合物，对Erg2靶标选择了3种。这些先导化合物可建议用于进一步的体外研究。

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