AI-Powered Pesticide R&D: A Global Race and China’s Breakthrough
Release time：Nov 19,2025
Driven jointly by food security and sustainable development, artificial intelligence is reshaping the R&D paradigm for pesticide development. The traditional pesticide R&D model—characterized by "high investment, long development cycles, and high risks"—is gradually shifting toward AI-driven computational design processes. Globally, AI has become a crucial technology for enhancing the efficiency and accuracy of pesticide R&D.
On the international stage, both multinational corporations and research institutions are advancing the deep integration of AI into pesticide R&D. Google DeepMind’s AlphaFold has broken through the bottleneck in protein structure prediction, offering a new approach to target discovery. Bayer has developed a generative AI-based molecular screening platform that can simultaneously predict the activity and toxicity of candidate compounds. Syngenta is leveraging graph neural networks to parallelly evaluate molecular activity and environmental safety attributes. At the academic level, the U.S. EPA has built a deep-learning toxicity prediction database using ToxCast and Tox21 data; MIT’s generative Transformer model has demonstrated outstanding performance in predicting molecular-target interactions. Overall, AI is driving the shift in pesticide R&D from an “experience-driven” to a “computation-driven” paradigm.
Against the backdrop of the aforementioned international development trends, China is advancing its own initiatives along a path tailored to its unique circumstances. The Chinese Academy of Agricultural Sciences and Zhejiang University have conducted relatively systematic research in the field of small-molecule optimization; meanwhile, the National Key Laboratory for Green Pesticides has been promoting the practical application of AI methods as tools for pesticide molecule design and optimization. The National Key Laboratory for Green Pesticides has launched... Pesticide Discovery AI Platform It is the first domestic software for pesticide molecule design that leverages artificial intelligence and high-performance computing, covering key stages such as “target identification—molecule generation—structure optimization—property prediction.” The platform integrates molecular modeling, deep learning, and high-performance computing to establish a complete R&D closed loop—from protein structure to candidate molecules. Adopting a multiscale hybrid modeling strategy that combines molecular dynamics simulations with deep learning, the platform achieves precise predictions of binding free energies and ecotoxicity for small pesticide molecules. Based on equivariant graph neural networks (EGNN) and diffusion models, it integrates three-dimensional geometric and chemical features of protein-binding pockets, enabling de novo design of drug-like molecules targeted specifically at certain protein pockets. By combining AI-based scaffold hopping with ADMET predictions, the platform achieves dual optimization—enhancing the activity and reducing the toxicity of pesticide molecules. The Pesticide Discovery AI platform boasts the nation’s first systematic pesticide database, which includes pesticides and their molecular fragments, target protein structures, and screening molecule libraries, providing a high-quality data foundation for model training and prediction. At the software level, the platform adopts a web-based architecture, offering one-stop AI-assisted design. Users can complete task configuration and result analysis without any programming skills, significantly lowering the barrier to entry.
Overall, AI has gradually become integrated into key stages of pesticide molecule discovery. As generative models, multimodal representations, and high-performance computing continue to converge, the role of AI-driven design approaches in the pesticide R&D process will keep growing. Take the Pesticide Discovery AI platform as an example: its practical experience in algorithmic frameworks, database construction, and software development provides a reusable implementation pathway for related research and applications in China.
Under this technological development trend, to promote the application of AI and high-performance computing technologies in the field of pesticide research and development, the China National Pesticide Industry Association and the National Key Laboratory of Green Pesticides (Central China Normal University) are jointly hosting... The Nation’s First “AI+Green Pesticide Innovation Capability Enhancement Training Workshop” Registration is now open. The training will focus on the theoretical foundations of artificial intelligence in pesticide research and development, molecular design and prediction of molecular properties, and hands-on exercises demonstrating the use of the Pesticide Discovery AI functional module. This training aims to help participants understand the basic workflows and operational procedures for applying AI methods in pesticide molecular design practice, thereby further enhancing China’s innovation capabilities in green pesticides within both academic research and corporate R&D centers. It will also lay a solid foundation for the future application of AI and high-performance computing technologies in scientific research and corporate R&D efforts.
Upon completion of all courses and passing the assessment, participants will receive a certificate of completion jointly issued by the China Pesticide Industry Association and the National Key Laboratory of Green Pesticides (Central China Normal University). Outstanding participants will receive further encouragement.
Recommend News
Contacts
Telephone: 86-513-84543278 / 84543408
Fax: 86-513-84414369
Factory address: No. 20 Huanghai 2nd Road, Chemical Industry Park, Coastal Economic Development Zone, Rudong County, Jiangsu Province, China.
Office address: Room 416, No. 99 Jinggangshan Road, Jugang Town, Rudong County, Jiangsu Province, China.
Mobile phone official website