ANI neural network potentials for small molecule pKa prediction,Physical Chemistry Chemical Physics

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ANI neural network potentials for small molecule pKa prediction
Physical Chemistry Chemical Physics ( IF 2.9 ) Pub Date : 2024-08-29 , DOI: 10.1039/d4cp01982b
Ross James Urquhart ₁ , Alexander van Teijlingen ₁ , Tell Tuttle ₁

Affiliation

The pK_a value of a molecule is of interest to chemists across a broad spectrum of fields including pharmacology, environmental chemistry and theoretical chemistry. Determination of pK_a values can be accomplished through several experimental methods such as NMR techniques and titration together with computational techniques such as DFT calculations. However, all of these methods remain time consuming and computationally expensive. In this work we develop a method for the rapid calculation of pK_a values of small molecules which utilises a combination of neural network potentials, low energy conformer searches and thermodynamic cycles. We show that neural network potentials trained on different phase and charge states can be employed in tandem to predict the full thermodynamic energy cycle of molecules. Focusing here on imidazolium derived carbene species, the method utilised can easily be extended to other functional groups of interest such as amines with further training.

中文翻译：

ANI 神经网络用于小分子 pKa 预测的潜力

分子的 p K _a值引起了药理学、环境化学和理论化学等广泛领域的化学家的兴趣。 p K _a值的测定可以通过多种实验方法（例如 NMR 技术和滴定）以及计算技术（例如 DFT 计算）来完成。然而，所有这些方法仍然耗时且计算成本昂贵。在这项工作中，我们开发了一种快速计算小分子 p K _a值的方法，该方法结合了神经网络势、低能构象异构体搜索和热力学循环。我们证明，在不同相和电荷状态上训练的神经网络势可以串联使用来预测分子的完整热力学能量循环。这里重点关注咪唑衍生的卡宾物种，通过进一步训练，所使用的方法可以轻松扩展到其他感兴趣的官能团，例如胺。

更新日期：2024-08-29

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