Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here, we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, and S) make up ∼90% of drug-like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and nonbonded interactions. ANI-2x is shown to accurately predict molecular energies compared to density functional theory with a ∼10⁶ factor speedup and a negligible slowdown compared to ANI-1x and shows subchemical accuracy across most of the COMP6 benchmark. The resulting model is a valuable tool for drug development which can potentially replace both quantum calculations and classical force fields for a myriad of applications.

中文翻译：

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将 ANI 深度学习分子潜能的适用性扩展到硫和卤素。

机器学习 (ML) 方法已成为广泛应用中强大的预测工具，例如面部识别和自动驾驶汽车。在科学领域，计算化学家和物理学家一直在使用 ML 来预测物理现象，例如原子势能面和反应路径。已开发可转移 ML 电位，例如 ANI-1x，其目标是准确模拟包含化学元素 H、C、N 和 O 的有机分子。在这里，我们提供了 ANI-1x 模型的扩展。被称为 ANI-2x 的新模型接受了三种额外化学元素的训练：S、F 和 Cl。此外，ANI-2x 接受了扭转改进训练，以更好地预测分子扭转曲线。这些新功能在有机化学和药物开发中开辟了广泛的新应用。这七种元素（H、C、N、O、F、Cl 和 S）构成了大约 90% 的类药物分子。为了表明这些添加不会牺牲准确性，我们已经在一系列有机分子和应用中测试了该模型，包括 COMP6 基准、二面角旋转、构象异构体评分和非键相互作用。与具有 ~10与 ANI-1x 相比，⁶因子加速和可以忽略不计的减速，并在大多数 COMP6 基准测试中显示亚化学精度。由此产生的模型是药物开发的宝贵工具，它可以潜在地取代量子计算和经典力场，用于无数应用。