De novo molecular design and generative models.
De novo molecular design and generative models.
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DOI：
10.1016/j.drudis.2021.05.019
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发表时间：
2021-11-01
影响因子：
7.4
通讯作者：
Brown, Nathan
中科院分区：
文献类型：
--
作者：
Meyers, Joshua;Fabian, Benedek;Brown, Nathan
分子设计策略是药物发现中治疗进展的组成部分。从头分子设计的计算方法在过去的三十年中已经发展起来，最近，部分归功于机器学习（ML）和人工智能（AI）的进步，药物发现领域已经获得了实践经验。在这里，我们回顾这些学习和目前的从头方法，根据其分子表示的粗糙度：也就是说，分子设计是否是基于原子，片段为基础的，或基于反应的范例建模。此外，我们强调了强有力的基准的价值，描述了在实践中使用这些方法的主要挑战，并提供了一个观点，在未来几年的探索和挑战的进一步机会。
Molecular design strategies are integral to therapeutic progress in drug discovery. Computational approaches for de novo molecular design have been developed over the past three decades and, recently, thanks in part to advances in machine learning (ML) and artificial intelligence (AI), the drug discovery field has gained practical experience. Here, we review these learnings and present de novo approaches according to the coarseness of their molecular representation: that is, whether molecular design is modeled on an atom-based, fragment-based, or reaction-based paradigm. Furthermore, we emphasize the value of strong benchmarks, describe the main challenges to using these methods in practice, and provide a viewpoint on further opportunities for exploration and challenges to be tackled in the upcoming years.