HMAMP: Designing Highly Potent Antimicrobial Peptides Using a Hypervolume-Driven Multiobjective Deep Generative Model
Wang Li, Yiping Liu, Xiangxiang Zeng, Xiucai Ye, Junfeng Shi, Gary G. Yen, Quan Zou, Xiangzheng Fu, Dongsheng Cao
Abstract
Antimicrobial peptides (AMPs) have exhibited unprecedented potential as biomaterials in combating multidrug-resistant bacteria, prompting the proposal of many excellent generative models. However, the multiobjective nature of AMP discovery is often overlooked, contributing to the high attrition rate of drug candidates. Here, we propose a novel approach termed hypervolume-driven multiobjective AMP design (HMAMP), which prioritizes the simultaneous optimization of multiattribute AMPs. By synergizing reinforcement learning and a gradient descent algorithm rooted in the hypervolume maximization concept, HMAMP effectively biases generative processes and mitigates the pattern collapse issue. Comparative experiments show that HMAMP significantly outperforms state-of-the-art methods in effectiveness and diversity. A knee-based decision strategy is then employed to fast screen candidates with favorable physicochemical properties, aligning with the enhanced antimicrobial activity and reduced side effects. Molecular visualization further elucidates structural and functional properties of the AMPs. Overall, HMAMP is an effective approach to traverse large and complex exploration spaces to search for idealism-realism trade-off AMPs.