Acellera Labs , C/Doctor Trueta 183 , 08005 Barcelona , Spain.
Computational Science Laboratory , Universitat Pompeu Fabra , PRBB, C/Doctor Aiguader 88 , 08003 Barcelona , Spain.
J Chem Inf Model. 2019 Aug 26;59(8):3485-3493. doi: 10.1021/acs.jcim.9b00439. Epub 2019 Aug 8.
Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FF are not generally available for all molecules, like novel druglike molecules. While methods based on quantum mechanics (QM) exist to parameterize them with better accuracy, they are computationally expensive and slow, which limits applicability to a small number of molecules. Here, we present an automated FF parameterization method which can utilize either density functional theory (DFT) calculations or approximate QM energies produced by different neural network potentials (NNPs), to obtain improved parameters for molecules. We demonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small molecules in a fraction of time compared with an equivalent parameterization using DFT QM calculations while producing more accurate parameters than FF (GAFF2). We expect our method to be of critical importance in computational structure-based drug discovery (SBDD). The current version is available at ( www.playmolecule.org ) and implemented in HTMD, allowing to parameterize molecules with different QM and NNP options.
快速准确的分子力场（FF）参数化仍然是一个未解决的问题。对于所有分子，例如新型药物样分子，通常无法获得准确的 FF。虽然存在基于量子力学（QM）的方法来更准确地对其进行参数化，但这些方法计算成本高且速度慢，这限制了其在少数分子上的适用性。在这里，我们提出了一种自动化的 FF 参数化方法，该方法可以利用密度泛函理论（DFT）计算或不同神经网络势（NPP）产生的近似 QM 能量，为分子获得改进的参数。我们证明，对于 torchani-ANI-1x NNP 的情况，与使用 DFT QM 计算进行等效参数化相比，我们可以在一小部分时间内对小分子进行参数化，同时产生比 FF（GAFF2）更准确的参数。我们预计我们的方法在基于计算的结构药物发现（SBDD）中具有至关重要的意义。当前版本可在 （www.playmolecule.org） 获得，并在 HTMD 中实现，允许使用不同的 QM 和 NNP 选项对分子进行参数化。
