Drug discovery is a complex process that involves sequentially screening and examining a vast array of molecules to identify those with the target properties. This process, also referred to as sequential experimentation, faces challenges due to the vast search space, the rarity of target molecules, and constraints imposed by limited data and experimental budgets. To address these challenges, we introduce a human-in-the-loop framework for sequential experiments in drug discovery. This collaborative approach combines human expert knowledge with deep learning algorithms, enhancing the discovery of target molecules within a specified experimental budget. The proposed algorithm processes experimental data to recommend both promising molecules and those that could improve its performance to human experts. Human experts retain the final decision-making authority based on these recommendations and their domain expertise, including the ability to override algorithmic recommendations. We applied our method to drug discovery tasks using real-world data and found that it consistently outperforms all baseline methods, including those which rely solely on human or algorithmic input. This demonstrates the complementarity between human experts and the algorithm. Our results provide key insights into the levels of humans' domain knowledge, the importance of meta-knowledge, and effective work delegation strategies. Our findings suggest that such a framework can significantly accelerate the development of new vaccines and drugs by leveraging the best of both human and artificial intelligence.

翻译：药物发现是一个复杂过程，需通过序贯筛选与检测海量分子来识别具有目标特性的分子。该过程（亦称序贯实验）面临搜索空间庞大、目标分子稀缺、以及受限于有限数据和实验预算等多重挑战。为应对这些挑战，我们提出一种面向药物发现序贯实验的人机协同框架。这种协作方法融合人类专家知识与深度学习算法，能在指定实验预算内提升目标分子的发现效率。所提出的算法通过处理实验数据，既能推荐有前景的分子，也能推荐可提升算法性能的候选分子供人类专家审阅。人类专家基于这些建议及其领域专长保留最终决策权，包括否决算法建议的能力。我们采用真实数据将本方法应用于药物发现任务，发现其始终优于所有基线方法——包括仅依赖人类或仅依赖算法输入的方法。这一结果证明了人类专家与算法之间的互补性。研究揭示了人类领域知识水平、元知识的重要性以及有效工作委派策略的关键见解。我们的发现表明，此类框架通过融合人类与人工智能各自优势，可显著加速新型疫苗和药物研发进程。