美国纳米材料中心Sankaranarayanan Subramanian K. R. S.及团队报道，机器学习克服了人类发现自组装肽的偏见。相关研究成果于2022年10月31日发表于国际顶尖学术期刊《自然—化学》。

肽材料从组织工程和表面涂层到催化和传感，具有广泛的应用。调整包含肽的氨基酸序列可以调节肽功能，但序列长度的微小增加会导致候选肽的数量急剧增加。传统上，肽设计是由人类的专业知识和直觉指导的，每次研究通常产生不到十个肽，但这些方法不容易扩展，容易受到人类偏见的影响。

该文中，研究人员介绍了一位机器学习工作流AI专家系统，该专家系统将蒙特卡罗树搜索和随机森林与分子动力学模拟相结合，开发了一个完全自主的计算搜索引擎，以发现具有高度自组装潜力的肽序列。研究证明了人工智能专家有效搜索三肽和五肽的大空间的功效。人工智能专家的可预测性表现与人类专家不相上下或更好，并提出了几个具有高度自组装倾向的非直觉序列，从而了其克服人类偏见和加速肽发现的潜力。

附：英文原文

Title: Machine learning overcomes human bias in the discovery of self-assembling peptides

Author: Batra, Rohit, Loeffler, Troy D., Chan, Henry, Srinivasan, Srilok, Cui, Honggang, Korendovych, Ivan V., Nanda, Vikas, Palmer, Liam C., Solomon, Lee A., Fry, H. Christopher, Sankaranarayanan, Subramanian K. R. S.

Issue&Volume: 2022-10-31

Abstract: Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias. Here we introduce a machine learning workflow—AI-expert—that combines Monte Carlo tree search and random forest with molecular dynamics simulations to develop a fully autonomous computational search engine to discover peptide sequences with high potential for self-assembly. We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. The predictability of AI-expert performs on par or better than our human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.

DOI: 10.1038/s41557-022-01055-3

Source: https://www.nature.com/articles/s41557-022-01055-3