Small Molecule Optimization with Large Language Models

Philipp Guevorguian; Menua Bedrosian; Tigran Fahradyan; Gayane Chilingaryan; Armen Aghajanyan; Hrant Khachatrian

cs.LGJul 26, 2024

Small Molecule Optimization with Large Language Models

Authors: Philipp Guevorguian, Menua Bedrosian, Tigran Fahradyan, Gayane Chilingaryan, Armen Aghajanyan, Hrant Khachatrian

Abstract

Molecular optimization, the process of designing molecules with desirable properties, represents a critical challenge in drug discovery. The recent advancements in large language models (LLMs) have opened new opportunities for their integration with traditional molecular optimization algorithms to improve performance. In this work, we propose Molecular Language Model powered Evolutionary Algorithm (Mol-E), an evolutionary algorithm that relies on the generative capabilities of LLMs trained on molecules and molecular properties. Scientific Contribution. Mol-E obtains the highest aggregate Top-10 AUC among the comparable full-23-task results considered here, scoring 17.500 in the task-agnostic regime, in which the oracle is treated strictly as a black box, and 20.551 in the task-informed regime, in which the optimizer receives a fixed semantic description of the objective. Mol-E also improves over the evaluated baselines on multi-property optimization with docking against DRD2, MK2, and AChE.

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Apr 24, 2026 · cs.LGJ/K move · Enter open · S save

C-MORAL: Controllable Multi-Objective Molecular Optimization with Reinforcement Alignment for LLMs

Rui Gao, Youngseung Jeon, Swastik Roy, Morteza Ziyadi+1

University of California, Los Angeles · Amazon

Large language models (LLMs) show promise for molecular optimization, but aligning them with selective and competing drug-design constraints remains challenging. We propose C-Moral, a reinforcement learning post-training framework for controllable multi-objective molecular optimization. C-Moral combines group-based relative optimization, property score alignment for heterogeneous objectives, and bottleneck-sensitive non-linear reward aggregation to improve stability across competing molecular properties. Experiments on C-MuMOInstruct and S

^2

-Bench MolOpt show that C-Moral achieves the best performance among compared methods on both benchmarks. On C-MuMOInstruct, C-Moral achieves the best Success Optimized Rate (SOR) of 48.9% on in-domain tasks and 39.5% on out-of-domain tasks while preserving scaffold similarity. On S

^2

-Bench MolOpt, it also achieves the strongest results across LogP, MR, and QED optimization tasks. These results suggest that C-Moral is an effective way to align molecular LLMs with continuous and constrained molecular design objectives. Our code and models are publicly available at https://github.com/Rwigie/C-MORAL.

Molecular Optimization Training-Free Reward Alignment Multi-Objective Optimization Moral Reasoning

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Abstract

Explore similar work

C-MORAL: Controllable Multi-Objective Molecular Optimization with Reinforcement Alignment for LLMs

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