Can AI Design Safer Pesticides Faster?

Syngenta and Insilico Medicine are using AI-driven chemistry to design faster, more sustainable crop protection compounds

19 Jun 2026

Syngenta has partnered with Insilico Medicine to embed artificial intelligence into its crop protection research, the companies said on 16 June 2026. The agreement centres on Insilico's small-molecule generative chemistry platform, which will feed directly into Syngenta's early-stage discovery pipeline for pesticides and fungicides.

Generative chemistry uses AI models to propose novel molecular structures that meet pre-set performance and safety criteria, effectively widening the range of candidates researchers can evaluate without running each through a physical laboratory. For agrochemical development, where candidate molecules typically fail at high rates in early testing, the approach could reduce both cost and time-to-market.

Camilla Corsi, Head of Crop Protection Research at Syngenta, pointed to the scale of the opportunity. "This collaboration with Insilico Medicine means that Syngenta can harness the potential and scope of AI to develop the next generation of sustainable crop protection solutions," she said.

Regulatory pressure is part of the context. Both the European Union and the United States have tightened environmental assessments for new agrochemicals, requiring manufacturers to demonstrate lower persistence in soil and water. Molecules designed with those constraints embedded from the outset may face a shorter, less expensive regulatory path than those adapted after discovery.

The deal follows a pattern visible across pharmaceutical and agricultural science. Drug developers including Exscientia and Recursion Pharmaceuticals have already applied generative chemistry to human medicine, compressing discovery cycles from years to months in some cases. Insilico has positioned its platform as transferable across sectors, and the Syngenta agreement is among its first formal moves into agriculture.

Neither company disclosed financial terms. The collaboration's output, novel active ingredients, remains subject to regulatory review before any commercialisation, a process that typically takes several years regardless of how quickly the discovery phase proceeds. Whether AI-assisted design materially changes approval timelines remains to be demonstrated in practice.