Journal of advanced research · 2025
Reinforcement learning-based generative artificial intelligence for novel pesticide design.
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Yang Ruoqi, Li Biao, Dong Jin, Cai Zhuomei, Lin Hongyan, Wang Fan, Yang Guangfu
Аннотация
INTRODUCTION: Pesticides play a pivotal role in ensuring food security, and the development of green pesticides is an inevitable trend in global agricultural progress. Although deep learning-based generative models have revolutionized de novo drug design in pharmaceutical research, their application in pesticide research and development remains unexplored.
OBJECTIVES: This study aims to pioneer the application of generative artificial intelligence to pesticide design by proposing a reinforcement learning-based framework for obtaining pesticide-like molecules with high binding affinity.
METHODS: This framework comprises two key components: PestiGen-G, which systematically explores the pesticide-like chemical space using a character-based generative model coupled with the REINFORCE algorithm; and PestiGen-S, which combines a fragment-based generative model with the Monte Carlo Tree Search algorithm to generate molecules that stably bind to the specific target protein.
RESULTS: Experimental results show that the molecules generated by PestiGen have superior pesticide-likeness and binding affinity compared to those generated by existing methods. In addition, we employ an active learning strategy to reduce the false-positive rate of the generated molecules. Finally, through collaboration with domain experts, we successfully designed a novel 4-hydroxyphenylpyruvate dioxygenase inhibitor (YH23768) with favorable enzyme inhibition and herbicidal potency.
CONCLUSION: This proof-of-concept study highlights the utility of PestiGen as a valuable tool for pesticide design. The web server based on the model is freely available at https://dpai.ccnu.edu.cn/PestiGen/.
OBJECTIVES: This study aims to pioneer the application of generative artificial intelligence to pesticide design by proposing a reinforcement learning-based framework for obtaining pesticide-like molecules with high binding affinity.
METHODS: This framework comprises two key components: PestiGen-G, which systematically explores the pesticide-like chemical space using a character-based generative model coupled with the REINFORCE algorithm; and PestiGen-S, which combines a fragment-based generative model with the Monte Carlo Tree Search algorithm to generate molecules that stably bind to the specific target protein.
RESULTS: Experimental results show that the molecules generated by PestiGen have superior pesticide-likeness and binding affinity compared to those generated by existing methods. In addition, we employ an active learning strategy to reduce the false-positive rate of the generated molecules. Finally, through collaboration with domain experts, we successfully designed a novel 4-hydroxyphenylpyruvate dioxygenase inhibitor (YH23768) with favorable enzyme inhibition and herbicidal potency.
CONCLUSION: This proof-of-concept study highlights the utility of PestiGen as a valuable tool for pesticide design. The web server based on the model is freely available at https://dpai.ccnu.edu.cn/PestiGen/.