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.