| Autor: |
Wu, Tianyu, Zhou, Min, Zou, Jingcheng, Chen, Qi, Qian, Feng, Kurths, Jürgen, Liu, Runhui, Tang, Yang |
| Předmět: |
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| Zdroj: |
Nature Communications; 7/26/2024, Vol. 15 Issue 1, p1-22, 22p |
| Abstrakt: |
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 (<102), much smaller than public polymer datasets (>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. Host defense peptide (HDP)-mimicking polymers are promising therapeutic alternatives. Here, the authors develop an AI-guided framework, and synthesize a polymer DM0.8iPen0.2, which exhibits broad-spectrum and potent antibacterial activity. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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