  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.
