Reinforcement learning-based generative artificial intelligence for novel pesticide design.

ABSTRACT:

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

SUBMITTER: Yang R 

PROVIDER: S-EPMC12685504 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature