arXiv Machine Learning October 12, 2026 By Yuhang Xie, Zhiyi Shi, Zeyi Zhang, Zhisheng Qi, Yushun Dong, Yu Wang, Xiongye Xiao

MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?

Read the original on arXiv Machine Learning →

MolP2SBench is a new benchmark that tests whether large language models can reason from molecular properties to structure. Each task presents a masked scaffold and a set of physical properties, asking the model to pick the correct structure from plausible candidates. By shuffling or replacing properties, the benchmark evaluates whether models truly rely on correct property‑structure pairings and how they respond to counterfactual changes, using a Property‑Grounded Recovery metric to compare performance across models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Sep 22

MolSC: Leveraging Substituent Contributions to Enhance Fine-grained Molecular Understanding in LLMs

MolSC is a new dataset of 181,000 substituent-level examples that captures how attaching specific substituents to molecular scaffolds changes properties such as bioactivity and physicochemical descriptors. The authors also provide MolSC-Bench, a held‑out benchmark of 1,541 examples that are disjoint from MolSC at scaffold, substituent, and molecule levels. Experiments show that training molecular large language models on MolSC markedly improves their ability to predict substituent contributions, outperforming existing models on a range of downstream chemistry tasks.

By Hyuntae Park, Sooyeon Kim, Jiwon Park, SangKeun Lee

llms nlp benchmarks

arXiv AI

Aug 12

Multi-Granular Rationale-Guided Molecular LLM for Property Prediction

arXiv:2608. 10480v1 Announce Type: new Abstract: Large language models (LLMs) are widely applied across chemical tasks, such as molecular property prediction, which underpins drug discovery.

By Junwoo Park, Minyoung Shin, Cheol Soon Lee, Sujee Lee

llms fine-tuning

Hugging Face Trending Papers

Aug 11

Multi-Granular Rationale-Guided Molecular LLM for Property Prediction

Large language models (LLMs) are widely applied across chemical tasks, such as molecular property prediction, which underpins drug discovery. Molecular LLMs represent a molecule through several modalities, notably a 1D SMILES sequence or a 2D molecular graph.

llms fine-tuning

arXiv Machine Learning

Jun 5

MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry

arXiv:2606. 05693v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representations such as SMILES differ substantially from the natural language on which LLMs are primarily trained.

By Joey Chan, Wonbin Kweon, Ashley Shin, Niharika Bhattacharjee, Pengcheng Jiang, Yue Guo, Jiawei Han

llms rag fine-tuning

arXiv Machine Learning

Sep 1

S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation

arXiv:2608.30910v1 Announce Type: new Abstract: Spectroscopic structure elucidation is central to molecular analysis, but recent Large Language Model (LLM)-based methods mostly formulate it as direct...

By Xuanle Zhao, Xinyuan Cai, Xiang Cheng, Bo Xu

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 3

Rethinking Molecular Text Representations for LLMs: An Empirical Study

arXiv:2606. 03057v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use.

By Arun Raja, Garrett M. Morris, Kian Ming A. Chai

llms nlp benchmarks

One email a morning, machine-written

Related stories

MolSC: Leveraging Substituent Contributions to Enhance Fine-grained Molecular Understanding in LLMs

Multi-Granular Rationale-Guided Molecular LLM for Property Prediction

Multi-Granular Rationale-Guided Molecular LLM for Property Prediction

MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry

S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation

Rethinking Molecular Text Representations for LLMs: An Empirical Study