The number of ‘small’ molecules that may be of interest to chemical biologists — chemical space — is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved ‘forward’ problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). ‘Deep’ (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.

中文翻译：

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化学信息学和化学生物学中的深度学习和生成方法：智能导航小分子空间


化学生物学家可能感兴趣的“小”分子（化学空间）的数量是巨大的，但所制造的部分却很小。大多数策略都是有区别的，即涉及“前向”问题（具有分子，建立属性）。然而，我们通常希望解决更困难的生成或逆问题（描述所需的属性，找到分子）。基于大规模神经网络的“深度”（机器）学习支撑着计算机视觉、自然语言处理、无人驾驶汽车以及围棋等游戏中世界领先的性能等技术；它还可以应用于解决化学生物学中的反问题。特别是，深度学习的最新发展承认候选分子结构的计算机生成及其特性的预测，从而使人们能够智能地导航（生物）化学空间。这些方法是革命性的，但需要了解（生物）化学和计算机科学才能发挥最大优势。我们为深度学习革命提供了高级（非数学）背景，并将化学生物学和信息学的关键问题提出为从单个分子的离散性质到连续但高维潜在的双向映射最能反映化学空间的表示法。多种架构都可以做到这一点；我们专注于一种称为变分自动编码器的特殊类型。然后，我们提供了此类方法最近成功的一些示例，并对未来进行了展望。