Numerate Inc. , San Francisco , California 94107 , United States.
Mol Pharm. 2018 Oct 1;15(10):4371-4377. doi: 10.1021/acs.molpharmaceut.7b01144. Epub 2018 Jul 7.
Artificial Intelligence has advanced at an unprecedented pace, backing recent breakthroughs in natural language processing, speech recognition, and computer vision: domains where the data is euclidean in nature. More recently, considerable progress has been made in engineering deep-learning architectures that can accept non-Euclidean data such as graphs and manifolds: geometric deep learning. This progress is of considerable interest to the drug discovery community, as molecules can naturally be represented as graphs, where atoms are nodes and bonds are edges. In this work, we explore the performance of geometric deep-learning methods in the context of drug discovery, comparing machine learned features against the domain expert engineered features that are mainstream in the pharmaceutical industry.
人工智能以前所未有的速度发展，为自然语言处理、语音识别和计算机视觉等领域的最新突破提供了支持：这些领域的数据本质上是欧几里得的。最近，在工程深度学习架构方面取得了相当大的进展，这些架构可以接受非欧几里得数据，如图和流形：几何深度学习。这一进展引起了药物发现界的极大兴趣，因为分子可以自然地表示为图，其中原子是节点，键是边。在这项工作中，我们探讨了几何深度学习方法在药物发现中的性能，将机器学习特征与制药行业主流的领域专家设计特征进行了比较。
