Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens
Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens
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DOI：
10.1021/acs.jctc.0c00121
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发表时间：
2020-07-14
影响因子：
5.5
通讯作者：
Roitberg, Adrian E.
中科院分区：
文献类型：
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作者：
Devereux, Christian;Smith, Justin S.;Roitberg, Adrian E.
机器学习（ML）方法已成为面部识别和自动驾驶汽车等广泛应用中强大的预测工具。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。开发了可转移的ML电位，如ANI-1x，其目标是精确模拟含有化学元素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。新模型被称为ANI-2x，它被训练成三种额外的化学元素：S，F和Cl。此外，ANI-2x进行了扭转细化训练，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）组成了类似药物分子的90%。为了证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这个模型，包括COMP 6基准，二面角旋转，构象评分和非键相互作用。与密度泛函理论相比，ANI-2x可以准确地预测分子能量，与ANI-1x相比，其加速比为106，减速可以忽略不计，并且在大多数COMP 6基准中都显示出亚化学准确性。由此产生的模型是药物开发的一个有价值的工具，它可以在无数的应用中取代量子计算和经典力场。
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 similar to 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 similar to 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.