Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks.
Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks.
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DOI：
10.1021/acs.jpclett.8b01837
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
Masashi Tsubaki;T. Mizoguchi
中科院分区：
文献类型：
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作者：
Masashi Tsubaki;T. Mizoguchi
具有特殊性质的分子的发现对于开发有效的材料和有用的药物至关重要。最近，为了利用机器学习加速这类发现，基于密度泛函理论(DFT)的深度神经网络(DNN)被应用到量子化学计算中。虽然已经提出了各种用于量子化学的DNN，但这些网络需要各种化学描述符作为输入和大量的学习参数来模拟原子相互作用。在本文中，我们提出了一种新的基于DNN的分子性质预测方法，它(I)不依赖于描述符，(Ii)更紧凑，(Iii)涉及额外的神经网络来模拟分子结构中所有原子之间的相互作用。在考虑分子结构的同时，我们还对所有原子之间的势进行了建模，这使得神经网络能够同时学习原子之间的相互作用和势。我们强调，这些原子的“对”相互作用和势是用全局分子结构来表征的，这是神经网络深度的函数；这导致在DNN中隐含或间接地考虑原子的“多体”相互作用和势。在用基准的QM9数据集对我们的模型进行评估时，我们获得了对各种量子化学性质的快速准确的预测性能。此外，我们还分析了学习相互作用和势能对每个属性的影响。此外，我们还演示了外推评估，即，我们用小分子训练模型，并用大分子测试它。我们相信，对外推评估的见解将有助于在基于DNN的分子性质预测中开发更多的实际应用。
The discovery of molecules with specific properties is crucial to developing effective materials and useful drugs. Recently, to accelerate such discoveries with machine learning, deep neural networks (DNNs) have been applied to quantum chemistry calculations based on the density functional theory (DFT). While various DNNs for quantum chemistry have been proposed, these networks require various chemical descriptors as inputs and a large number of learning parameters to model atomic interactions. In this paper, we propose a new DNN-based molecular property prediction that (i) does not depend on descriptors, (ii) is more compact, and (iii) involves additional neural networks to model the interactions between all the atoms in a molecular structure. In the consideration of the molecular structure, we also model the potentials between all the atoms; this allows the neural networks to simultaneously learn the atomic interactions and potentials. We emphasize that these atomic "pair" interactions and potentials are characterized using the global molecular structure, a function of the depth of the neural networks; this leads to the implicit or indirect consideration of atomic "many-body" interactions and potentials within the DNNs. In the evaluation of our model with the benchmark QM9 data set, we achieved fast and accurate prediction performances for various quantum chemical properties. In addition, we analyzed the effects of learning the interactions and potentials on each property. Furthermore, we demonstrated an extrapolation evaluation, i.e., we trained a model with small molecules and tested it with large molecules. We believe that insights into the extrapolation evaluation will be useful for developing more practical applications in DNN-based molecular property predictions.