Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization (2025)

Abstract: In real-world drug design, molecule optimization requires selectively improving multiple molecular properties up to pharmaceutically relevant levels, while maintaining others that already meet such criteria. However, existing computational approaches and instruction-tuned LLMs fail to capture such nuanced property-specific objectives, limiting their practical applicability. To address this, we introduce C-MuMOInstruct, the first instruction-tuning dataset focused on multi-property optimization with explicit, property-specific objectives. Leveraging C-MuMOInstruct, we develop \mathtt{GeLLM^4O\text{-}C} s, a series of instruction-tuned LLMs that can perform targeted property-specific optimization. Our experiments across 5 in-distribution and 5 out-of-distribution tasks show that \mathtt{GeLLM^4O\text{-}C} s consistently outperform strong baselines, achieving up to 126% higher success rate. Notably, \mathtt{GeLLM^4O\text{-}C} s exhibit impressive 0-shot generalization to novel optimization tasks and unseen instructions. This offers a step toward a foundational LLM to support realistic, diverse optimizations with property-specific objectives. C-MuMOInstruct and code are accessible through https://github.com/ninglab/GeLLMO-C.

## introduction developing a new drug is a time-consuming and expensive process, requiring over a decade and $2 billions @xcite . a key stage in this process is lead optimization @xcite , where "hit" molecules -exhibiting promising early-stage bioactivity against drug targets -are optimized for multiple molecular properties @xcite critical for pharmaceutical success. in practice, this stage often requires improving specific properties up to a pharmaceutically significant level, while maintaining already desirable ones within acceptable bounds. we refer to this setting as controllable multi-property, multi-objective optimization (c-mumo), allowing for property-specific objectives, and thus greater control over the optimization. such controllable optimization requires navigating complex trade-offs among multiple properties that are often competing or even conflicting @xcite . for instance, optimizing an oral antipsychotic drug requires sufficiently high bloodbrain barrier permeability (bbbp) @xcite and dopamine receptor d2 (drd2) inhibition @xcite to access the central nervous system (cns) and block dopamine receptors in the cns @xcite . meanwhile, properties related to toxicity, such as potassium (k + ) channel inhibition must be lowered, since excessive inhibition of k + channels in the brain @xcite can cause fatal cardiac arrythmias @xcite . additionally, properties supporting oral bioavailability, such as intestinal absorption, must be maintained if they already meet desirable levels. these trade-offs highlight the need for property-specific objectives to mimic realistic optimization tasks. most existing computational approaches @xcite @xcite cannot handle tasks with multiple objectives. furthermore, existing approaches for multi-objective optimization @xcite @xcite rely on manually curated reward functions and careful task-specific tuning -limiting their scalability and applicability to diverse tasks in practice. we refer readers to appendix a for a detailed review of existing approaches. recently, instruction-tuned llms @xcite , demonstrated strong performance on diverse multi-property optimization tasks. however, they only tackle tasks where all properties should be improved simultaneously. this setting fails to capture the nuanced property-specific objectives prevalent in realistic lead optimization. to address these critical limitations, we introduce c-mumoinstruct, the first high-quality instruction-tuning dataset designed for c-mumo tasks involving up to 10 molecular properties. unlike prior datasets that require all properties to improve, c-mumoinstruct explicitly incorporates controllable property-specific objectives -specifying which properties must be improved up to a user-defined property-specific threshold, and which must be maintained within acceptable bounds. this design better reflects real-world lead optimization, where some properties reach pharmaceutically significant levels in early stages, while others require multiple iterations for further improvement. built on c-mumoinstruct, we introduce a family of generalizable large language models for multi-property, multi-objective controllable optimization, gellm 4 o-c, by instruction-tuning general-purpose llms. gellm 4 o-c is trained to handle tasks requiring selective improvement of specific properties while maintaining already desirable ones. we develop both specialist and generalist variants. each specialist gellm 4 o-c is trained on a single property combination with multiple controllable multi-objective tasks. generalist gellm 4 o-c is trained across diverse multiproperty combinations and multiple controllable objectives within each combination, enabling crosstask knowledge transfer. this enables a single foundational model to handle novel and diverse c-mumo tasks without task-specific fine-tuning. we evaluate our gellm 4 o-c models with strong general-purpose llms and foundational llms for chemistry across 5 in-distribution (ind) and 5 outof-distribution (ood) tasks. our results reveal several key findings: (1) all gellm 4 o-cs substantially outperform state-of-the-art baselines on all ind and ood tasks, with gains of up to 126% over the best baselines. (2) generalist gellm 4 o-cs outperform specialist ones on 4 out of 5 ind tasks, with impressive gains of up to 26% on challenging tasks. ## c-mumoinstruct in this paper, we introduce c-mumoinstruct, which provides control over each property objective in multi-property optimization tasks, unlike existing datasets such as mumoinstruct. this enables models tuned on c-mumoinstruct to im-prove specific properties up to a user-defined level, while maintaining others at already desirable levels -a crucial capability that distinguishes c-mumoinstruct from existing datasets. these key differences are highlighted in problem definition: a c-mumo task is to modify a hit molecule m x into an improved lead molecule m y , via structural modifications on m x , guided by property-specific objectives -controlling which properties to be improved and the extent of such improvement. given p molecular properties, we define a pharmaceutically relevant level, θ p , for each property @xmath0, accordingly, p is considered near-optimal if its score in m x -denoted as p(m x ) -is more desirable than θ p (represented as p(m x ) ≺ θ p ), and sub-optimal, otherwise (represented as p(m x ) ⪰ θ p ). the desirability of each property is determined by the intended pharmaceutical goal, where either higher or lower property scores increase the molecule's likelihood to be a successful drug candidate. for example, a higher bbbp is desired for drugs targeting the cns to ensure their access to the brain, whereas a lower bbbp is desired for peripheral targets to prevent damage to the cns. formally, a c-mumo task optimizing m x to m y aims to improve all sub-optimal properties p @xmath1: (1) m y remains structurally similar to m x (similarity constraint); (2) m y improves upon m x in each sub-optimal property @xmath2, ∆ p , represented as (m x ≺ ∆p m y ) ∀@xmath3; and (3) the absolute change from m x to m y in each near-optimal property @xmath4, represented as (m x ∼ = ∆p m y ) ∀@xmath5. ## additional analyses we conduct ablation studies to examine the effects of (1) the number of properties included during instruction tuning of generalist models, and (2) instruction template diversity. we find that models trained on a moderate number of properties and diverse instruction templates outperform others across multiple ind tasks (appendix g.3). addi-tionally, we perform a failure analysis of the most difficult objectives, revealing that drd2 improvement consistently has the highest constraint violation rates, and that improving properties is more challenging than stabilizing them (appendix g.4). ## conclusion in this paper, we introduced c-mumoinstruct, the first instruction-tuning dataset enabling controllable molecule optimization with property-specific objectives. leveraging c-mumoinstruct, we developed gellm 4 o-cs, that consistently and largely outperform strong general-purpose llms and foundational llms for chemistry across all ind and ood tasks. moreover, generalist gellm 4 o-cs exhibit strong generalization to unseen tasks, outperforming baselines by 27% on average. this indicates the potential of gellm 4 o-c as a foundational model to tackle diverse tasks with realistic, controllable objectives reflecting real-world scenarios.