Random Copolymer inverse design system orienting on Accurate discovering of Antimicrobial peptide-mimetic copolymers

TL;DR

This paper presents a novel AI-driven inverse design system for antimicrobial peptide-mimetic copolymers, enabling efficient discovery of candidates with broad-spectrum efficacy and low toxicity despite limited data.

Contribution

The authors develop a universal multi-model copolymer representation learning and reinforcement learning framework for polymer inverse design, addressing high-dimensional space and data scarcity.

Findings

High-precision antimicrobial activity prediction with few-shot data

Contracted copolymer space for efficient exploration

Discovery of candidate copolymers with desired properties

Abstract

Antimicrobial resistance is one of the biggest health problem, especially in the current period of COVID-19 pandemic. Due to the unique membrane-destruction bactericidal mechanism, antimicrobial peptide-mimetic copolymers are paid more attention and it is urgent to find more potential candidates with broad-spectrum antibacterial efficacy and low toxicity. Artificial intelligence has shown significant performance on small molecule or biotech drugs, however, the higher-dimension of polymer space and the limited experimental data restrict the application of existing methods on copolymer design. Herein, we develop a universal random copolymer inverse design system via multi-model copolymer representation learning, knowledge distillation and reinforcement learning. Our system realize a high-precision antimicrobial activity prediction with few-shot data by extracting various chemical information from multi-modal copolymer representations. By pre-training a scaffold-decorator generative model via knowledge distillation, copolymer space are greatly contracted to the near space of existing data for exploration. Thus, our reinforcement learning algorithm can be adaptive for customized generation on specific scaffolds and requirements on property or structures. We apply our system on collected antimicrobial peptide-mimetic copolymers data, and we discover candidate copolymers with desired properties.