From Single-Step Edit Response to Multi-Step Molecular Optimization
- URL: http://arxiv.org/abs/2605.10035v1
- Date: Mon, 11 May 2026 06:00:16 GMT
- Title: From Single-Step Edit Response to Multi-Step Molecular Optimization
- Abstract summary: Conditional molecular optimization aims to edit a molecule to realize a specified property shift.<n>In practice, structurally similar molecule data is scarce, while decisions are inherently action-level.<n>We propose a response-oriented discrete edit optimization approach comprising a single-step molecular edit response predictor and a multi-step planner.
- Score: 9.685693410049495
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Conditional molecular optimization aims to edit a molecule to realize a specified property shift. In practice, structurally similar molecule data is scarce, while decisions are inherently action-level: at each step, the system must select one local structural edit from a candidate set that is strictly filtered by chemical feasibility rules. This level mismatch between supervision and decision makes oracle-in-the-loop search unstable in molecular optimization. Regressing on property differences between molecule pairs improves data efficiency but relies on oracle-in-the-loop search, entangling transformation effects with global context and providing limited guidance for selecting the next feasible edit, often resorting to oracle-in-the-loop search. For this reason, we propose a response-oriented discrete edit optimization approach comprising two tightly coupled components: a single-step molecular edit response predictor (SMER) and a multi-step planner that composes local predictions into optimization trajectories via guided tree search (SMER-Opt). The approach learns a directional evaluation model over edit actions to support constraint-aware planning. It mines weakly related molecule pairs and decomposes their structural differences into minimal edit units, turning endpoint property annotations into process-level supervision and yielding reusable, transferable action primitives. A directional edit evaluator then scores feasible candidate edits by their likelihood of moving the molecule toward the desired property change, substantially reducing dependence on external evaluator queries at decision time. Code is available at https://anonymous.4open.science/r/SMER.
Related papers
- On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization [55.27368653669229]
Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures.<n>We study this adaptation problem for discrete diffusion models.<n>We find that acquisition, reward shaping, and model debiasing provide complementary routes to higher reward, especially for small molecules.
arXiv Detail & Related papers (2026-07-03T00:06:31Z) - Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization [85.4863852505905]
We introduce unsupervised reward optimization of protein language models (PLMs)<n>Our key insight is that task-agnostic rewards, which combine intrinsic model uncertainty with semantic consistency informed by protein representation models, exhibit strong correlation with controllability measures across base models and temperature regimes.<n>Our framework provides a scalable pathway toward controllable biomolecular design in settings where labeled preferences or experimental feedback are scarce or unavailable.
arXiv Detail & Related papers (2026-06-17T11:42:01Z) - My Chemical Harness: Evolutionary Molecular Design over Synthetic Pathways with Large Language Model Agents [2.0798270860482964]
My Chemical Harness is a route-native evolutionary framework for goal-directed molecular design.<n>Each route is built from purchasable building blocks and reaction templates, executed by deterministic chemistry tools, and scored through task-specific molecular oracles.<n>Large language models (LLMs) are used only as strategy controllers that select high-level preferences over route length, move type, reaction families, motifs, and exploration pressure.
arXiv Detail & Related papers (2026-06-08T23:52:52Z) - Probe Before You Edit: Probing-Guided Molecular Optimization for LLM Agents in Structure-Based Drug Design [15.697564598284833]
Two diagnostic metrics measure how often a single edit improves both objectives.<n>Applying these metrics to current LLM-agent pipelines exposes a consistent failure mode.<n>We propose textbfPROBE, an optimization framework built around edit-response probing.
arXiv Detail & Related papers (2026-05-30T06:06:51Z) - Agents on a Tree: Pathwise Coordination for Multi-Objective Molecular Optimization [33.33321738135092]
Multi-objective molecular optimization requires searching vast chemical spaces under conflicting objectives.<n>We propose ATOM, a multi-agent framework that formulates molecular optimization as a tree-structured search.
arXiv Detail & Related papers (2026-03-27T14:21:21Z) - Reference-guided Policy Optimization for Molecular Optimization via LLM Reasoning [58.644854860003704]
Large language models (LLMs) benefit from supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) in reasoning tasks.<n>We show that answer-only SFT on the reference molecules collapses reasoning, and RLVR provides sparse feedback under similarity constraints.<n>We introduce Reference-guided Policy Optimization (RePO), an optimization approach that learns from reference molecules without requiring trajectory data.
arXiv Detail & Related papers (2026-03-06T04:39:08Z) - M^4olGen: Multi-Agent, Multi-Stage Molecular Generation under Precise Multi-Property Constraints [35.83366265234892]
textbfM olGen is a fragment-level, retrieval-augmented, two-stage framework for molecule generation under multi-property constraints.<n>A dataset with reasoning chains of fragment edits and measured property deltas underpins both stages.<n>Experiments on generation under two sets of property constraints show consistent gains in validity and precise satisfaction of multi-property targets.
arXiv Detail & Related papers (2026-01-15T07:18:05Z) - Rewiring Experts on the Fly:Continuous Rerouting for Better Online Adaptation in Mixture-of-Expert models [52.502867924372275]
Mixture-of-Experts (MoE) models achieve efficient scaling through sparse expert activation, but often suffer from suboptimal routing decisions due to distribution shifts in deployment.<n>We propose textita data-free, online test-time framework that continuously adapts MoE routing decisions during text generation without external supervision or data.
arXiv Detail & Related papers (2025-10-16T16:24:36Z) - Coder as Editor: Code-driven Interpretable Molecular Optimization [36.84386817559159]
We introduce MECo, a framework that bridges reasoning and execution by translating editing actions into executable code.<n>Our approach achieves over 98% accuracy in reproducing held-out realistic edits from chemical reactions and target-specific compound pairs.
arXiv Detail & Related papers (2025-10-16T08:55:06Z) - MoRA: On-the-fly Molecule-aware Low-Rank Adaptation Framework for LLM-based Multi-Modal Molecular Assistant [23.60380089071549]
We propose an instance-specific parameter space alignment approach for each molecule on-the-fly.<n>MoRA produces a unique set of low-rank adaptation weights for each input molecular graph.<n>Experiments demonstrate that MoRA's instance-specific dynamic adaptation outperforms statically adapted baselines.
arXiv Detail & Related papers (2025-10-14T07:54:43Z) - MolEditRL: Structure-Preserving Molecular Editing via Discrete Diffusion and Reinforcement Learning [4.430115182041077]
MolEditRL is a molecular editing framework that integrates structural constraints with precise property optimization.<n>For comprehensive evaluation, we construct MolEdit-Instruct, the largest and most property-rich molecular editing dataset.
arXiv Detail & Related papers (2025-05-26T15:29:08Z) - Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model [20.250683535089617]
We propose a text-guided multi-property molecular optimization method utilizing transformer-based diffusion language model (TransDLM)<n>By fusing physically and chemically detailed semantics with specialized molecular representations, TransDLM effectively integrates diverse information sources to guide precise optimization.
arXiv Detail & Related papers (2024-10-17T14:30:27Z) - Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states.
This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO)
We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
arXiv Detail & Related papers (2023-12-10T15:22:30Z) - DiffStack: A Differentiable and Modular Control Stack for Autonomous
Vehicles [75.43355868143209]
We present DiffStack, a differentiable and modular stack for prediction, planning, and control.
Our results on the nuScenes dataset indicate that end-to-end training with DiffStack yields substantial improvements in open-loop and closed-loop planning metrics.
arXiv Detail & Related papers (2022-12-13T09:05:21Z) - MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization [51.00815310242277]
generative models and reinforcement learning approaches made initial success, but still face difficulties in simultaneously optimizing multiple drug properties.
We propose the MultI-constraint MOlecule SAmpling (MIMOSA) approach, a sampling framework to use input molecule as an initial guess and sample molecules from the target distribution.
arXiv Detail & Related papers (2020-10-05T20:18:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.