Molecular Lead Optimization via Agentic Tool Planning

2026-05-21

AI Summary

Traditional lead compound optimization often relies on single-step strategies that struggle to balance structural constraints with long-term optimization objectives, frequently disrupting critical pharmacophores and yielding limited improvements in ADMET properties. This work reframes molecular optimization as a trajectory-aware sequential decision-making problem and introduces TRACE, an intelligent agent powered by large language models that plans multi-step tool invocation sequences to achieve forward-looking optimization while preserving essential molecular scaffolds. By integrating molecular generation and editing tools, ADMET prediction, and structural similarity constraints into a unified framework, TRACE substantially outperforms existing baselines across multiple tasks, significantly enhancing optimization success rates, the magnitude of property improvement, molecular validity, and retention of original structural features.

Abstract

Drug discovery is a lengthy and resource-intensive process composed of multiple stages. Among these stages, lead optimization plays a critical role in transforming early hit compounds into viable drug candidates. This stage requires improving ADMET-related properties through subtle structural refinement while preserving key molecular substructures responsible for binding affinity to disease targets. Recent advances in artificial intelligence have shown promise in accelerating various aspects of drug discovery; however, most existing approaches to lead optimization rely on one-step molecular optimization, which fail to account for the long-term consequences of sequential design decisions. To address this limitation, we propose TRACE, a trajectory-aware, LLM-reasoning agent for molecular lead optimization that formulates tool selection as a sequential decision-making problem over action trajectories. Given a lead molecule and an optimization objective, TRACE makes trajectory-aware decisions over molecular optimization tools, enabling forward-looking refinement under structural constraints. Experiments on multiple ADMET optimization tasks show that our agent achieves higher optimization success, larger property improvements, and higher validity, while preserving molecular similarity compared to baseline models.

Problem

• lead optimization
• ADMET
• sequential decision-making
• molecular refinement
• drug discovery

Innovation

• trajectory-aware optimization
• LLM reasoning agent
• sequential decision-making
• molecular lead optimization
• ADMET property refinement