VALID-Mol: a Systematic Framework for Validated LLM-Assisted Molecular Design

TL;DR

VALID-Mol is a comprehensive framework that combines chemical validation with LLM-driven molecular design, significantly improving the generation of valid, synthesizable molecules with enhanced properties for pharmaceutical development.

Contribution

It introduces a systematic methodology integrating prompt optimization, chemical verification, and domain fine-tuning to reliably generate feasible molecules, advancing LLM applications in scientific domains.

Findings

Valid chemical structure generation increased from 3% to 83%.

Framework predicts molecules with up to 17-fold binding affinity improvements.

Enhanced reliability in generating synthesizable, property-optimized molecules.

Abstract

Large Language Models demonstrate substantial promise for advancing scientific discovery, yet their deployment in disciplines demanding factual precision and specialized domain constraints presents significant challenges. Within molecular design for pharmaceutical development, these models can propose innovative molecular modifications but frequently generate chemically infeasible structures. We introduce VALID-Mol, a comprehensive framework that integrates chemical validation with LLM-driven molecular design, achieving an improvement in valid chemical structure generation from 3% to 83%. Our methodology synthesizes systematic prompt optimization, automated chemical verification, and domain-adapted fine-tuning to ensure dependable generation of synthesizable molecules with enhanced properties. Our contribution extends beyond implementation details to provide a transferable methodology for scientifically-constrained LLM applications with measurable reliability enhancements. Computational analyses indicate our framework generates promising synthesis candidates with up to 17-fold predicted improvements in target binding affinity while preserving synthetic feasibility.