人工智能驱动科学发现时代指导抗菌肽设计的基础模型方法

Jike Wang, Jianwen Feng, Yu Kang, Peichen Pan, Jingxuan Ge, Yan Wang, Mingyang Wang, Zhenxing Wu, Xingcai Zhang, Jiameng Yu, Xujun Zhang, Tianyue Wang, Lirong Wen, Guangning Yan, Yafeng Deng, Hui Shi, Chang-Yu Hsieh, Zhihui Jiang, Tingjun Hou
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摘要

我们提出的 AMP-Designer 是一种基于 LLM 的基础模型方法,用于快速设计具有多种所需特性的新型抗菌肽 (AMP)。随后的体外验证实验表明,几乎所有硅学推荐的候选化合物都表现出显著的抗菌活性,阳性率高达 94.4%。其中两种候选化合物表现出卓越的活性、最小的毒性、在人体血浆中的高度稳定性,以及在小鼠肺部感染实验中观察到的较低的诱发抗生素耐药性倾向,这些都表明它们在减少细菌负荷方面具有约 100 倍的显著功效。此外,AMP-Designer 还展示了它在设计特异性 AMPs 方面的卓越能力,它能在标记数据极其有限的情况下针对目标菌株设计特异性 AMPs。AMP-Designer提出的针对痤疮丙酸杆菌的最杰出候选药物的体外最小抑菌浓度值为2.0$\mu$g/ml。通过在 AMP-Designer 框架中整合先进的机器学习方法,如对比提示调整、知识提炼和强化学习,AMP 的设计过程表现出了极高的效率。即使在面对标记数据稀缺所带来的挑战时,这种效率仍然非常显著。这些发现凸显了 AMP 设计的巨大潜力,它是对抗抗生素耐药性这一全球健康威胁的有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A foundation model approach to guide antimicrobial peptide design in the era of artificial intelligence driven scientific discovery
We propose AMP-Designer, an LLM-based foundation model approach for the rapid design of novel antimicrobial peptides (AMPs) with multiple desired properties. Within 11 days, AMP-Designer enables de novo design of 18 novel candidates with broad-spectrum potency against Gram-negative bacteria. Subsequent in vitro validation experiments demonstrate that almost all in silico recommended candidates exhibit notable antibacterial activity, yielding a 94.4% positive rate. Two of these candidates exhibit exceptional activity, minimal hemotoxicity, substantial stability in human plasma, and a low propensity of inducing antibiotic resistance as observed in murine lung infection experiments, showcasing their significant efficacy in reducing bacterial load by approximately one hundredfold. The entire process, from in silico design to in vitro and in vivo validation, is completed within a timeframe of 48 days. Moreover, AMP-Designer demonstrates its remarkable capability in designing specific AMPs to target strains with extremely limited labeled datasets. The most outstanding candidate against Propionibacterium acnes suggested by AMP-Designer exhibits an in vitro minimum inhibitory concentration value of 2.0 $\mu$g/ml. Through the integration of advanced machine learning methodologies such as contrastive prompt tuning, knowledge distillation, and reinforcement learning within the AMP-Designer framework, the process of designing AMPs demonstrates exceptional efficiency. This efficiency remains conspicuous even in the face of challenges posed by constraints arising from a scarcity of labeled data. These findings highlight the tremendous potential of AMP-Designer as a promising approach in combating the global health threat of antibiotic resistance.
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