Geometric Deep Learning Autonomously Learns Chemical Features That Outperform Those Engineered by Domain Experts,Molecular Pharmaceutics

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Geometric Deep Learning Autonomously Learns Chemical Features That Outperform Those Engineered by Domain Experts
Molecular Pharmaceutics ( IF 4.5 ) Pub Date : 2018-06-04 00:00:00 , DOI: 10.1021/acs.molpharmaceut.7b01144
Patrick Hop ₁ , Brandon Allgood ₁ , Jessen Yu ₁

Affiliation

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.

中文翻译：

几何深度学习可自主学习优于领域专家设计的化学功能

人工智能以前所未有的速度发展，支持自然语言处理，语音识别和计算机视觉方面的最新突破：自然界中数据是欧几里得的领域。最近，在工程深度学习体系结构方面已经取得了可观的进步，这些体系结构可以接受非欧几里得数据，例如图形和流形：几何深度学习。这一进展对药物发现界非常感兴趣，因为分子自然可以表示为图，其中原子是节点，键是边缘。在这项工作中，我们将在药物发现的背景下探索几何深度学习方法的性能，将机器学习的功能与制药行业主流的领域专家设计的功能进行比较。

更新日期：2018-06-04

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