A client-enhanced language model for interactive compound optimization guided by explainable artificial intelligence

Compound optimization is of central relevance in medicinal chemistry. We introduce a new machine learning framework for iterative chemical optimization that integrates compound potency predictions, the explanation of predictions, and generative modeling and that is applicable to individual compounds. The approach identifies substituents in active compounds that limit their potency and iteratively replaces these substituents with others supporting potency increases. In proof-of-concept calculations, the methodology effectively optimizes compound potency. Furthermore, the optimization framework is combined with a large language model via the model concept protocol to generate an {AI} agent system for interactive optimization. The system is shown to successfully carry out optimization tasks of increasing complexity based on simple prompts, without the need for additional fine-tuning. The interactive computational optimization approach is accessible to non-experts and expected to be of particular interest for practical medicinal chemistry.