Molecular Foundation Models for Lead Optimization Using Bioactivity, ADMET, and Synthetic Feasibility Prompts Download PDF

Lead optimization involves the simultaneous enhancement of potency, ADMET properties, and synthetic feasibility, making the progression from an initial hit or lead to a viable drug candidate a challenging multi-objective design problem. Traditional medicinal chemistry workflows remain iterative, expert-intensive, and reliant on repeated cycles of design, synthesis, and testing, while existing molecular generative models often target single-property optimization or employ reward functions that, though powerful, are not always intuitive for medicinal chemists to guide. To address these limitations, this article proposes a molecular foundation model for prompt-conditioned lead optimization, designed to generate optimized lead molecules from natural-language or structured prompts specifying desired bioactivity, ADMET, and synthetic feasibility constraints. The system leverages a pre-trained transformer-based molecular language model fine-tuned for conditional generation, where a prompt encoder directs molecule generation toward the requested target profile, and reinforcement learning aligns outputs with bioactivity, ADMET, and synthesis-oriented reward signals. The model aims to produce a small, diverse set of chemically valid candidates tailored to the prompt rather than an exhaustive random library, providing medicinal chemists with a curated selection for review. By combining chemical language modeling with multi-objective reward design, prompt-conditioned molecular foundation models have the potential to make lead optimization more interactive, transparent, and parallelizable, supporting more efficient exploration of drug-like chemical space.