分子建模和模拟的深度学习透视
A Perspective on Deep Learning for Molecular Modeling and Simulations
摘要：
深度学习正在改变许多科学领域，它在分子系统建模方面具有巨大潜力。然而，与深度学习在计算机视觉和自然语言处理中的成熟部署不同，它在分子建模和模拟方面的发展仍处于早期阶段，这主要是因为分子的归纳偏差与图像或文本的归纳偏差完全不同。基于这些差异，我们首先从分子物理学的角度回顾了传统深度学习模型的局限性，并总结了分子建模和深度学习之间接口的一些相关技术进展。我们不仅仅关注越来越复杂的神经网络模型，相反，我们强调现代深度学习背后的理论和思想。我们希望将这些想法转化为分子建模将创造新的机会。为此，我们总结了几个代表性的应用，从监督到无监督和强化学习，并讨论了它们与深度学习新兴趋势的联系。最后，我们展望了有前途的方向，这可能有助于解决现有的框架中存在的问题，深分子建模。
英文摘要：
Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vision and natural language processing, its development in molecular modeling and simulations is still at an early stage, largely because the inductive biases of molecules are completely different from those of images or texts. Footed on these differences, we first reviewed the limitations of traditional deep learning models from the perspective of molecular physics, and wrapped up some relevant technical advancement at the interface between molecular modeling and deep learning. We do not focus merely on the ever more complex neural network models, instead, we emphasize the theories and ideas behind modern deep learning. We hope that transacting these ideas into molecular modeling will create new opportunities. For this purpose, we summarized several representative applications, ranging from supervised to unsupervised and reinforcement learning, and discussed their connections with the emerging trends in deep learning. Finally, we outlook promising directions which may help address the existing issues in the current framework of deep molecular modeling.
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