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.

药物研发是一个复杂的过程，本文介绍了一种人工智能与人类专家知识相结合的人机协作框架，用于药物研发中的顺序实验，以提高针对目标分子的发现效率。研究结果表明，这种框架能够有效加速新疫苗和药物的开发，充分利用人类专家和人工智能的优势。