MFG: Multi-turn Feedback Guided Molecular Optimization

Trieu Nguyen; Frazier N. Baker; Ruoxi Gao; Daniel Adu-Ampratwum; Xia Ning

cs.AIOct 13, 2026

MFG: Multi-turn Feedback Guided Molecular Optimization

Authors: Trieu Nguyen, Frazier N. Baker, Ruoxi Gao, Daniel Adu-Ampratwum, Xia Ning

Organizations: Department of Computer Science and Engineering, The Ohio State University · Division of Medicinal Chemistry and Pharmacognosy, The Ohio State University · Department of Biomedical Informatics, The Ohio State University · Translational Data Analytics Institute, The Ohio State University

Abstract

Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple properties while maintaining a similar overall structure. This process often requires iterative modifications guided by computational or experimental feedback. Recent large language model (LLM)-based methods have begun to explore this iterative approach; however, these methods are not explicitly trained to leverage such feedback, while existing approaches primarily focus on single-turn molecular optimization. To address this gap, we introduce Multi-turn Feedback Guided (MFG) molecular optimization, a reinforcement learning framework that trains an LLM to progressively optimize molecules using feedback from previous turns. MFG introduces a feedback vector capturing both property improvements and source molecule similarity, alongside a feedback-aligned reward function that promotes progressive improvement across turns. We apply MFG over $\text{GeLLM}^{3}\text{O}$ , a state-of-the-art LLM model for multi-objective molecular optimization, evaluate $\text{GeLLM}^{3}\text{O-MFG}$ on ten multi-objective molecular optimization tasks. The results demonstrate that $\text{GeLLM}^{3}\text{O-MFG}$ achieves the highest average similarity-constrained success rate ( $81.50%$ ), outperforming the base model $\text{GeLLM}^{3}\text{O}$ ( $70.81%$ ).

Figures & tables

Figure 1: Overall figure of Multi-turn Feedback Guided (MFG) $\mathop{\text{MOMO}}\limits$ .

General-purpose LLMs
Model	Setting	BDP			BDQ			BPQ			DPQ			BDPQ
Model	Setting	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$
Qwen3.5-9B	ST	32.80	0.76	0.98	18.80	0.73	0.54	90.40	0.75	0.58	15.60	0.72	0.51	11.00	0.75	0.54
Qwen3.5-9B	MT	36.40	0.72	0.95	21.60	0.70	0.60	90.40	0.75	0.58	19.80	0.69	0.36	12.20	0.76	0.51
Ministral-3-8B	ST	27.60	0.71	0.74	24.00	0.67	0.45	64.80	0.68	0.52	12.80	0.68	0.28	8.60	0.72	0.56
Ministral-3-8B	MT	37.60	0.68	0.70	24.60	0.66	0.69	66.00	0.65	0.65	13.60	0.68	0.36	11.60	0.70	0.47

Table 1: Overall Performance on IND Tasks

General-purpose LLMs
Model	Setting	MPQ			BDMQ			BHMQ			BMPQ			HMPQ
Model	Setting	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$
Qwen3.5-9B	ST	75.80	0.69	0.41	17.00	0.76	0.41	72.03	0.73	1.03	75.39	0.71	0.47	81.25	0.73	0.54
Qwen3.5-9B	MT	75.80	0.69	0.41	23.40	0.71	0.43	77.12	0.69	1.03	75.39	0.71	0.47	81.25	0.69	0.68
Ministral-3-8B	ST	47.80	0.65	0.37	15.40	0.66	0.52	51.69	0.66	1.07	47.12	0.65	0.47	34.38	0.67	0.59
Ministral-3-8B	MT	49.60	0.64	0.39	23.20	0.64	0.76	57.63	0.63	1.03	56.54	0.65	0.49	40.62	0.63	0.69

Table 2: Overall Performance on OOD Tasks

Model	Setting	BDP			BDQ			BPQ			DPQ			BDPQ
Model	Setting	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$
GeLLM 3 O-RL	ST	79.00	0.61	3.67	70.60	0.63	4.94	90.60	0.60	1.31	47.20	0.62	2.00	41.80	0.62	2.28
GeLLM 3 O-RL	MT	79.40	0.58	4.18	80.60	0.59	5.67	90.60	0.60	1.31	47.20	0.62	2.00	49.20	0.59	3.14
GeLLM 3 O-MFG	ST	83.20	0.62	3.62	73.00	0.63	4.30	89.20	0.62	1.25	48.40	0.63	1.97	44.00	0.61	2.50
GeLLM 3 O-MFG	MT	84.20	0.61	4.24	86.20	0.61	5.32	91.20	0.60	1.32	54.60	0.60	2.49	53.40	0.59	3.19
		$\texttt{SR}_{0.6}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.6}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.6}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.6}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.6}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$

Table 3: Ablation Study

Figure 2: An optimization case on BDPQ with $T=2$ . (a) The source molecule. (b) The best optimized molecule at turn $t=1$ . (c) The best optimized molecule at turn $t=2$ . The modified fragments are highlighted.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

General-purpose LLMs
Model	$T$	BDP			BDQ			BPQ			DPQ			BDPQ
Model	$T$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$
Qwen3.5-9B	1	32.80	0.76	0.98	18.80	0.73	0.54	90.40	0.75	0.58	15.60	0.72	0.51	11.00	0.75	0.54
	2	36.40	0.72	0.95	21.60	0.70	0.60	88.60	0.71	0.65	19.80	0.69	0.36	12.20	0.76	0.51
	3	35.40	0.71	0.97	19.80	0.70	0.44	81.80	0.71	0.61	16.60	0.70	0.35	11.60	0.72	0.51
	4	34.60	0.70	1.04	19.80	0.71	0.51	75.80	0.71	0.62	15.40	0.69	0.33	11.80	0.71	0.60

Appendix

Table A.2 : Overall Performance on IND Tasks Across Turn Settings

General-purpose LLMs
Model	$T$	MPQ			BDMQ			BHMQ			BMPQ			HMPQ
Model	$T$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$	$\texttt{SR}_{0.5}^{\uparrow}$	$\texttt{Sim}^{\uparrow}$	$\texttt{RI}^{\uparrow}$
Qwen3.5-9B	1	75.80	0.69	0.41	17.00	0.76	0.41	72.03	0.73	1.03	75.39	0.71	0.47	81.25	0.73	0.54
	2	68.00	0.66	0.46	23.40	0.71	0.43	77.12	0.69	1.03	73.82	0.67	0.54	81.25	0.69	0.68
	3	63.20	0.66	0.43	21.20	0.69	0.41	73.73	0.69	1.03	62.83	0.66	0.55	72.92	0.68	0.73
	4	60.20	0.66	0.45	21.40	0.69	0.42	70.34	0.70	0.98	59.16	0.67	0.52	70.83	0.67	0.74

Appendix

Table A.3 : Overall Performance on OOD Tasks Across Turn Settings

Artifact	Source	License Type	Accessibility
MuMOInstruct	https://huggingface.co/datasets/NingLab/MuMOInstruct	CC BY 4.0	Dataset
Llama-3.1-8B-Instruct	https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct	Llama 3.1 Community License	Checkpoint
GeLLM 3 O-P(6) Llama	https://huggingface.co/NingLab/GeLLMO-P6-Llama	CC BY-NC 4.0	Checkpoint
Qwen3.5-9B	https://huggingface.co/Qwen/Qwen3.5-9B	Apache 2.0	Checkpoint
Ministral-3-8B-Instruct-2512-BF16	https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512-BF16	Apache 2.0	Checkpoint
LlaSMol-Mistral-7B	https://huggingface.co/osunlp/LlaSMol-Mistral-7B	CC BY 4.0	LoRA Adapter

Appendix

Table A.4 : Licenses and sources of artifacts used in our study.

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