arXiv cs.AI· Tianyou Bai, Huan Wang, Mingchen Gao, Tongrui Yang, Fangyue Lin, Pinze Ren, Zhenlin Zhao, Siming Dong·· 3 小时前AI 评分34
Chem World：面向可信分子性质预测的大规模基准与物理感知框架
Chem World: A Large-Scale Benchmark and Physics-Aware Framework for Trustworthy Molecular Property Prediction
AI 导读
研究者发布 Chem World 大规模基准，整合 17 个公开数据集、超过 800,000 个分子和化学系统实例，覆盖 10 条性质赛道，包含单分子与多组分系统如药物溶解度。
正文 · 原文
Abstract:Molecular property prediction is fundamental to computational chemistry and drug formulation research, yet existing benchmarks remain fragmented across datasets, properties, and evaluation protocols, limiting systematic evaluation of model generalization. We introduce Chem World, a large-scale benchmark integrating 17 public datasets with over 800,000 molecular and chemical-system instances across 10 property tracks, covering single-molecule and multi-component systems, including drug solubility. Chem World provides a unified testbed for evaluating molecular representations and compositional modeling across diverse chemical properties. Building on the benchmark, we propose PACM (Physics-Aware Compositional Modeling), which combines molecular representations with composition-aware modeling and domain-specific physical constraints. Extensive experiments show that molecular language models provide strong and transferable representations, while PACM consistently improves prediction across diverse chemical systems. Chem World and our PACM-powered drug-property prediction Agent support generalizable chemical modeling and offer a foundation for AI-assisted biomedical research and drug formulation development.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.13945 [cs.LG] |
| (or arXiv:2611.13945v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13945 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tianyou Bai [view email]
[v1]
Tue, 10 Nov 2026 11:30:39 UTC (1,549 KB)
来源：arXiv cs.AI · arxiv.org