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【奖励】本帖被评价1次，作者MolAICal增加金币 0.8 个

MolAICal

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[资源] 分享一篇人工智能深度学习在药物设计和分子模拟应用方面的总结

这篇文章总结了深度学习在de novo drug design和分子动力学模拟，模型可解释性等方面的应用，如果有人有更好的建议，比如最新进展，可以告知并讨论，谢谢。

名称：Application advances of deep learning methods for de novo drug design and molecular dynamics simulation
https://wires.onlinelibrary.wiley.com/doi/full/10.1002/wcms.1581

Abstract
De novo drug design is a stationary way to build novel ligands in the confined pocket of receptor by assembling the atoms or fragments, while molecular dynamics (MD) simulation is a dynamical way to study the interaction mechanism between the ligands and receptors based on the molecular force field. De novo drug design and MD simulation are effective tools for novel drug discovery. With the development of technology, deep learning methods, and interpretable machine learning (IML) have emerged in the research area of drug design. Deep learning methods and IML can be used further to improve the efficiency and accuracy of de novo drug design and MD simulations. The application summary of deep learning methods for de novo drug design, MD simulations, and IML can further promote the technical development of drug discovery. In this article, two major workflow methods and the related components of classical algorithm and deep learning are described for de novo drug design from a new perspective. The application progress of deep learning is also summarized for MD simulations. Furthermore, IML is introduced for the deep learning model interpretability of de novo drug design and MD simulations. Our paper deals with an interesting topic about deep learning applications of de novo drug design and MD simulations for the scientific community.

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