Advanced Analytics and Data Sciences, Eli Lilly and Company, Indianapolis, IN 46285, United States.
Discovery Chemistry Research & Technologies, Eli Lilly and Company, Indianapolis, IN 46285, United States.
Drug Discov Today Technol. 2019 Dec;32-33:29-36. doi: 10.1016/j.ddtec.2020.05.001. Epub 2020 Jul 1.
Artificial intelligence (AI) has become a powerful tool in many fields, including drug discovery. Among various AI applications, molecular property prediction can have more significant immediate impact to the drug discovery process since most algorithms and methods use predicted properties to evaluate, select, and generate molecules. Herein, we provide a brief review of the state-of-art molecular property prediction methodologies and discuss examples reported recently. We highlight key techniques that have been applied to molecular property prediction such as learned representation, multi-task learning, transfer learning, and federated learning. We also point out some critical but less discussed issues such as data set quality, benchmark, model performance evaluation, and prediction confidence quantification.
人工智能（AI）已成为许多领域的强大工具，包括药物发现。在各种 AI 应用中，分子性质预测对药物发现过程可能具有更显著的直接影响，因为大多数算法和方法都使用预测性质来评估、选择和生成分子。在此，我们简要回顾了最先进的分子性质预测方法，并讨论了最近报道的示例。我们重点介绍了已应用于分子性质预测的关键技术，如学习表示、多任务学习、迁移学习和联邦学习。我们还指出了一些关键但讨论较少的问题，如数据集质量、基准、模型性能评估和预测置信度量化。
