Leveraging artificial intelligence and antibiotic data to facilitate the design of novel fungicides.

Abstract

BACKGROUND Rapid advances in generative artificial intelligence (AI) are accelerating the process of pesticide development. However, transfer learning-based de novo design focuses on generating molecules that are highly similar to existing inhibitors, which may limit the exploration of novel scaffolds and thereby constrain innovative breakthroughs in pesticide development. RESULTS This study proposes a new strategy for fungicide design using antibiotics. First, by combining pre-training and transfer learning, a character-level recurrent neural network model was able to generate antibiotic-like molecules that retained key features while avoiding excessive similarity to existing fungicides. Fungicide-like molecules were then further identified by training graph neural network models that could discriminate between fungicides and antibiotics. Interestingly, two of the generated molecules were found to share the same scaffold as florylpicoxamid, a recently approved fungicide that was not included in the training set. As a proof-of-concept, the inhibitory activity of the screened molecules against cytochrome bc1 complex was determined. Compound cp461 displayed enzyme inhibition comparable to that of florylpicoxamid, with a median inhibitory concentration of 17.9 ± 1.1 nm. CONCLUSION Overall, the pioneering work leveraged AI and antibiotic data to facilitate the design of novel fungicides and provided a viable research idea for pesticide development. © 2025 Society of Chemical Industry.