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首页> 外文期刊>Drug discovery today >Active-learning strategies in computer-assisted drug discovery
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Active-learning strategies in computer-assisted drug discovery

机译：计算机辅助药物发现中的主动学习策略

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摘要

High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the selection process by focusing on areas of chemical space that have the greatest chance of success while considering structural novelty. The core feature of these algorithms is their ability to adapt the structure-activity landscapes through feedback. Instead of full-deck screening, only focused subsets of compounds are tested, and the experimental readout is used to refine molecule selection for subsequent screening cycles. Once implemented, these techniques have the potential to reduce costs and save precious materials. Here, we provide a comprehensive overview of the various computational active-learning approaches and outline their potential for drug discovery.
机译：高通量化合物筛选非常耗时且耗费资源，并且在筛选化合物库，分析和选择最有希望的候选物进行进一步测试方面投入了大量精力。主动学习方法通​​过专注于在考虑结构新颖性的同时具有最大成功机会的化学空间领域来辅助选择过程。这些算法的核心特征是它们能够通过反馈适应结构活动态。代替完整的筛选，仅测试化合物的集中子集，并使用实验读数来完善分子选择，以用于后续的筛选循环。一旦实施，这些技术就有可能降低成本并节省宝贵的材料。在这里，我们提供了各种计算主动学习方法的全面概述，并概述了它们在药物发现中的潜力。

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