Collaborative Intelligence in Sequential Experiments: A Human-in-the-Loop Framework for Drug Discovery
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要