Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, via Mangiagalli 25, 20133, Milano, Italy.
Dipartimento di Fisica, Università degli Studi di Cagliari, Cittadella Universitaria, S.P. Monserrato-Sestu Km 0.7, I-09042, Monserrato, CA, Italy.
Sci Data. 2024 Aug 27;11(1):929. doi: 10.1038/s41597-024-03707-0.
In silico prediction of xenobiotic metabolism is an important strategy to accelerate the drug discovery process, as candidate compounds often fail in clinical phases due to their poor pharmacokinetic profiles. Here we present Meta, a dataset of quantum-mechanical (QM) optimized metabolic substrates, including force field parameters, electronic and physicochemical properties. Meta comprises 2054 metabolic substrates extracted from the MetaQSAR database. We provide QM-optimized geometries, General Amber Force Field (FF) parameters for all studied molecules, and an extended set of structural and physicochemical descriptors as calculated by DFT and PM7 methods. The generated data can be used in different types of analysis. FF parameters can be applied to perform classical molecular mechanics calculations as exemplified by the validating molecular dynamics simulations reported here. The calculated descriptors can represent input features for developing improved predictive models for metabolism and drug design, as exemplified in this work. Finally, the QM-optimized molecular structures are valuable starting points for both ligand- and structure-based analyses such as pharmacophore mapping and docking simulations.
计算机预测外源性物质代谢是加速药物发现过程的重要策略，因为候选化合物由于其较差的药代动力学特性，往往在临床阶段失败。在这里，我们提出了 Meta，这是一个量子力学（QM）优化代谢底物的数据集，包括力场参数、电子和物理化学性质。Meta 由从 MetaQSAR 数据库中提取的 2054 种代谢底物组成。我们提供了 QM 优化的几何形状、所有研究分子的通用安伯力场（FF）参数，以及由 DFT 和 PM7 方法计算得到的扩展结构和物理化学描述符集。生成的数据可用于不同类型的分析。FF 参数可用于执行经典的分子力学计算，这里报告的验证分子动力学模拟就是一个例子。计算得到的描述符可以作为输入特征，用于开发代谢和药物设计的改进预测模型，正如本文所举例说明的那样。最后，QM 优化的分子结构是配体和基于结构的分析（如药效团映射和对接模拟）的有价值起点。
