Ruđer Bošković Institute, Bijenička cesta 54, 10 000 Zagreb, Croatia.
Molecules. 2020 May 8;25(9):2198. doi: 10.3390/molecules25092198.
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.
已经针对 Cyp51 和 Erg2 进行了新型机器学习和分子建模过滤程序，以识别新型杀真菌剂靶标。使用逐步多元线性回归（FS-MLR）、无信息变量消除偏最小二乘回归以及一种称为正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。总共针对两种不同方法的描述符识别构建了 112 个预测模型。针对膜中固醇生物合成的真菌靶标，使用 Autodock4 分子对接程序从 Drugbank 数据库中选择了用于对接实验的抗真菌命中化合物。结果通过 Gold Protein-Ligand Docking Software 进行了验证。对于每个高分配体，考虑到最佳对接构象，提交给量子力学/分子力学（QM/MM）梯度优化，并考虑基组叠加误差和热校正（带有频率计算）进行最终单点计算。最后，根据 Cyp51 靶标和 Erg2 靶标的高 QM/MM 分数，从 Drugbank 中选择了 7 种先导化合物。这些先导化合物可能会被推荐用于进一步的体外研究。
