AI 中文总结
Rem3Di是一种表征学习框架，利用原子基础模型潜在特征生成可转移分子描述符，用于性质预测和虚拟筛选。它能捕捉分子手性，在药物性质基准测试中表现出色，还能区分过渡金属配合物，为化学机器学习提供新途径。
AI 中文摘要
基础机器学习原子间势（MLIPs）在大量量子力学数据集上训练，能在化学和构型空间广泛区域泛化。其内部表征编码丰富化学局部原子环境。本文介绍Rem3Di，一种将原子基础模型的潜在特征重新用作可转移分子描述符进行性质预测和虚拟筛选的表征学习框架。它将势能的每个原子特征组合成整个分子的单个固定长度描述符，随三维结构平滑变化且对原子排序不变。该描述符可直接使用或针对特定预测任务微调。为捕捉分子手性，构建伪标量特征。通过在大分子数据集上预训练，不依赖实验标签。在公共药物性质基准测试中，Rem3Di匹配或超过已发表基线，且能对过渡金属配合物进行有化学意义的区分。Rem3Di为化学机器学习提供了从模拟训练的原子表征到可转移、手性感知分子表征的途径。
英文摘要
Foundation machine-learned interatomic potentials (MLIPs) are trained on large quantum-mechanical datasets and generalise across broad regions of chemical and configurational space. Beyond their usual role in accelerating sampling-based simulations, their internal representations encode chemically rich local atomic environments. Here, we introduce Rem3Di, a representation-learning framework that repurposes latent features from atomistic foundation models as transferable molecular descriptors for property prediction and virtual screening. Rem3Di combines a potential's per-atom features into a single fixed-length descriptor of the whole molecule that varies smoothly with three-dimensional structure and is invariant to the ordering of the atoms. The descriptor can be used directly or fine-tuned for specific prediction tasks. To capture molecular handedness, Rem3Di constructs pseudoscalar features, which are unchanged by rotation but reverse sign under mirror reflection. This lets the descriptor distinguish enantiomers, which can differ in activity and toxicity. The transformer is pretrained on large molecular datasets by reconstructing corrupted atom features, so no experimental labels are required. Across public drug-property benchmarks, Rem3Di matches or exceeds published baselines without relying on classical 2D fingerprints. Additionally, the same descriptor yields chemically meaningful differentiation of transition-metal complexes without predefined bonding rules or handcrafted representations. Rem3Di therefore provides a route from simulation-trained atomistic representations to transferable, chirality-aware molecular representations for chemical machine learning.
CommentsAn earlier version of this work appeared at the NeurIPS 2025 Workshop on Symmetry and Geometry in Neural Representations (NeurReps). Workshop version: https://openreview.net/forum?id=jOmZsvXoK5
