AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria

Tianyu Wu, Min Zhou, Jingcheng Zou, Qi Chen, Feng Qian, Jürgen Kurths, Runhui Liu () and Yang Tang ()
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Tianyu Wu: East China University of Science and Technology
Min Zhou: East China University of Science and Technology
Jingcheng Zou: East China University of Science and Technology
Qi Chen: East China University of Science and Technology
Feng Qian: East China University of Science and Technology
Jürgen Kurths: Potsdam Institute for Climate Impact Research (PIK)
Runhui Liu: East China University of Science and Technology
Yang Tang: East China University of Science and Technology

Nature Communications, 2024, vol. 15, issue 1, 1-22

Abstract: Abstract Host defense peptide (HDP)-mimicking polymers are promising therapeutic alternatives to antibiotics and have large-scale untapped potential. Artificial intelligence (AI) exhibits promising performance on large-scale chemical-content design, however, existing AI methods face difficulties on scarcity data in each family of HDP-mimicking polymers ( 105), and multi-constraints on properties and structures when exploring high-dimensional polymer space. Herein, we develop a universal AI-guided few-shot inverse design framework by designing multi-modal representations to enrich polymer information for predictions and creating a graph grammar distillation for chemical space restriction to improve the efficiency of multi-constrained polymer generation with reinforcement learning. Exampled with HDP-mimicking β-amino acid polymers, we successfully simulate predictions of over 105 polymers and identify 83 optimal polymers. Furthermore, we synthesize an optimal polymer DM0.8iPen0.2 and find that this polymer exhibits broad-spectrum and potent antibacterial activity against multiple clinically isolated antibiotic-resistant pathogens, validating the effectiveness of AI-guided design strategy.

Date: 2024
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DOI: 10.1038/s41467-024-50533-4

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