Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02142, United States.
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Acc Chem Res. 2021 Jan 19;54(2):263-270. doi: 10.1021/acs.accounts.0c00699. Epub 2020 Dec 28.
Recent advances in computer hardware and software have led to a revolution in deep neural networks that has impacted fields ranging from language translation to computer vision. Deep learning has also impacted a number of areas in drug discovery, including the analysis of cellular images and the design of novel routes for the synthesis of organic molecules. While work in these areas has been impactful, a complete review of the applications of deep learning in drug discovery would be beyond the scope of a single Account. In this Account, we will focus on two key areas where deep learning has impacted molecular design: the prediction of molecular properties and the de novo generation of suggestions for new molecules.One of the most significant advances in the development of quantitative structure-activity relationships (QSARs) has come from the application of deep learning methods to the prediction of the biological activity and physical properties of molecules in drug discovery programs. Rather than employing the expert-derived chemical features typically used to build predictive models, researchers are now using deep learning to develop novel molecular representations. These representations, coupled with the ability of deep neural networks to uncover complex, nonlinear relationships, have led to state-of-the-art performance. While deep learning has changed the way that many researchers approach QSARs, it is not a panacea. As with any other machine learning task, the design of predictive models is dependent on the quality, quantity, and relevance of available data. Seemingly fundamental issues, such as optimal methods for creating a training set, are still open questions for the field. Another critical area that is still the subject of multiple research efforts is the development of methods for assessing the confidence in a model.Deep learning has also contributed to a renaissance in the application of de novo molecule generation. Rather than relying on manually defined heuristics, deep learning methods learn to generate new molecules based on sets of existing molecules. Techniques that were originally developed for areas such as image generation and language translation have been adapted to the generation of molecules. These deep learning methods have been coupled with the predictive models described above and are being used to generate new molecules with specific predicted biological activity profiles. While these generative algorithms appear promising, there have been only a few reports on the synthesis and testing of molecules based on designs proposed by generative models. The evaluation of the diversity, quality, and ultimate value of molecules produced by generative models is still an open question. While the field has produced a number of benchmarks, it has yet to agree on how one should ultimately assess molecules "invented" by an algorithm.
近年来，计算机硬件和软件的进步引发了深度学习的革命，这一革命影响了从语言翻译到计算机视觉等多个领域。深度学习也对药物发现的多个领域产生了影响，包括细胞图像分析和有机分子合成新途径的设计。虽然这些领域的工作具有影响力，但对深度学习在药物发现中的应用进行全面回顾将超出单个账户的范围。在本账户中，我们将重点关注深度学习对分子设计产生影响的两个关键领域：分子性质的预测和新分子生成的提议。
定量构效关系（QSAR）发展的最重要进展之一来自于将深度学习方法应用于预测药物发现计划中分子的生物活性和物理性质。研究人员现在不再使用通常用于构建预测模型的专家衍生的化学特征，而是使用深度学习来开发新的分子表示。这些表示形式，加上深度神经网络发现复杂、非线性关系的能力，导致了最先进的性能。虽然深度学习改变了许多研究人员处理 QSAR 的方式，但它并不是万能的。与任何其他机器学习任务一样，预测模型的设计取决于可用数据的质量、数量和相关性。看似基本的问题，例如创建训练集的最佳方法，仍然是该领域的悬而未决的问题。另一个仍然是多项研究努力主题的关键领域是开发评估模型置信度的方法。
深度学习也为从头分子生成的应用带来了复兴。深度学习方法不再依赖于手动定义的启发式方法，而是根据现有分子集来学习生成新分子。最初为图像生成和语言翻译等领域开发的技术已被改编为分子生成。这些深度学习方法已与上文所述的预测模型结合使用，并用于生成具有特定预测生物活性谱的新分子。虽然这些生成算法很有前景，但基于生成模型提出的设计进行合成和测试分子的报告很少。生成模型生成的分子的多样性、质量和最终价值的评估仍然是一个悬而未决的问题。尽管该领域已经产生了许多基准，但尚未就如何最终评估算法“发明”的分子达成一致。
