Summary of Collaborative Intelligence in Sequential Experiments: a Human-in-the-loop Framework For Drug Discovery, by Jinghai He et al.
Collaborative Intelligence in Sequential Experiments: A Human-in-the-Loop Framework for Drug Discovery
by Jinghai He, Cheng Hua, Yingfei Wang, Zeyu Zheng
First submitted to arxiv on: 7 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to drug discovery, combining human expertise with deep learning algorithms to enhance the identification of target molecules within a specified experimental budget. The proposed framework, which relies on sequential experimentation, addresses challenges in the process such as the vast search space and limited data and budgets. By processing experimental data to recommend promising molecules, the algorithm complements human experts’ decision-making authority based on their domain knowledge. In real-world drug discovery tasks, the method consistently outperforms baseline methods, demonstrating the value of integrating human and AI capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps scientists find new medicines by combining what computers can do with what people know. Right now, finding a new medicine is like looking for a specific grain of sand on a huge beach. The computer can help by sorting through the possibilities and suggesting which ones to look at next. A human expert then makes the final decision based on their knowledge and experience. This way, computers and humans work together to find new medicines faster than either one could alone. |
Keywords
» Artificial intelligence » Deep learning
