Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· 0 citations· 9 references
TL;DR
This paper proposes UniEdit, a Unified Graph-based Mixture-of-Experts (MoE) Molecular Editing model that offers a robust alternative to LLMs and incorporates a Mixture-of-Experts architecture that dynamically routes tasks to specialized components.
Abstract
Recently, Large Language Models (LLMs) have become the dominant paradigm for molecular editing due to their strong generalization capabilities across diverse tasks. However, treating molecules as 1D text strings (SMILES) introduces significant challenges in controllability and structural validity. In this paper, we question whether sequence generation is truly optimal for this topological task. We propose UniEdit, a Unified Graph-based Mixture-of-Experts (MoE) Molecular Editing model that offers a robust alternative to LLMs. Diverging from the generative approach, UniEdit reformulates molecular editing as a hierarchical node-level classification task. By predicting discrete edit actions (e.g., Add, Remove, Replace) directly on the graph, our model ensures topological precision by design. To handle conflicting multi-objective constraints within a single framework, we incorporate a Mixture-of-Experts architecture that dynamically routes tasks to specialized components. Extensive experiments across 28 diverse tasks demonstrate that UniEdit significantly outperforms sequence-based baselines. Furthermore, a preliminary scaling study reveals that our graph-based approach benefits consistently from increased model capacity, suggesting a scalable path toward general-purpose molecular editing. Our code is available at https://github.com/jiajunyu1999/GraphEditing.
A Molecular Perturbation framework that generates syntax-valid structural variants of training molecules under controlled Graph Edit Distance (GED) to probe the manifold regularity of molecular LLMs and suggests that it can partially expand the local trust region and offer a promising direction for stabilizing molecular LLMs against structural variation.
Jiatong Li, Weida Wang, Changmeng Zheng et al.· 0 citations
This paper introduces Caduceus, a family of MoE-enhanced foundation models built with a hierarchical pre-training paradigm to jointly integrate biological and natural language, and incorporates a multi-task instruction tuning phase, enabling robust protein parsing and natural language question answering.
Mingze Yin, Yiheng Zhu, Jialu Wu et al.· Proceedings of the 32nd ACM...· 0 citations
Automating optimization modeling from natural language with large language models (LLMs) faces two key challenges. First, training corpora lack structural diversity. Second, data generation pipelines remain static and decoupled from model learning. To address these challenges, we propose EvoOptiGraph, a novel framework where data and model co-evolve, driven by model weaknesses. EvoOptiGraph represents each mixed-integer linear program (MILP) as an attributed bipartite graph and applies validity-preserving evolutionary operators to generate structurally diverse instances. The evolved graphs are converted into solver code and natural language via deterministic compilation and verified back-translation. Training proceeds in two stages: supervised fine-tuning (SFT) on an initial dataset, followed by reinforcement learning with verifiable rewards (RLVR), where graph-derived weakness signals guide the generation of new instances targeting the model's failures. This forms a closed loop that continuously updates the training distribution. Empirical results on six public datasets show that EvoOptiGraph significantly outperforms larger generalist models, agentic methods, and specialized baselines in accuracy, executability, and generalization. These results demonstrate that targeted data-model coevolution is an effective strategy for improving LLMs on optimization modeling tasks.
Qingcan Kang, Mingyang Liu, Xiaojin Fu et al.· arXiv.org· 0 citations
This is the first method to expose GNN-derived attributions to an LLM as evidence for property prediction, and achieves the best overall results among generalist models and narrows the gap to specialist models tuned for each task.
Junwoo Park, Minyoung Shin, C. Lee et al.· 0 citations
FragBERTa is introduced, a molecular fragment-aware transformer-based representation language model pretrained using masked language modeling on Sequential Attachment-based Fragment Embedding (SAFE) representations, suggesting that fragment-based string representations offer advantages over atom-level representations for scaffold-sensitive and interaction-driven tasks.