3DCS: Datasets and Benchmark for Evaluating Conformational Sensitivity in Molecular Representations

Xi Wang, Yang Zhang, Yingjia Zhang, Yejia Cai, Shenji Wan

Keywords: Molecule Benchmark, AI for Science

TL;DR: 3DCS: The first benchmark to rigorously evaluate how well molecular representations capture intra-molecular conformational sensitivity across geometry, chirality, and energy.

Abstract:

Molecular representations (MRs) that capture 3D conformations are critical for applications such as reaction prediction, drug design, and material discovery. Yet despite the rapid development of molecular representation models, there is no comprehensive benchmark to evaluate their treatment of 3D conformational information.
We introduce 3DCS, the first benchmark for 3D Conformational Sensitivity in MRs. 3DCS evaluates whether representations within the same molecule (i) preserve geometric variation, (ii) capture chirality, and (iii) reflect the energy landscape. To enable this, we curate three large-scale datasets ($>$1M molecules, $\sim$10M conformers) spanning relaxed torsional scans, chiral drug candidates, and AIMD trajectories, and propose a unified Geometry–Chirality–Energy (GCE) evaluation framework.
Empirical analysis reveals that while modern data-driven MRs are highly geometry-sensitive, they inconsistently handle chirality and poorly align with energy, which is often overlooked. 3DCS thus provides the first rigorous benchmark for developing physically grounded, functionally reliable 3D molecular representations. GitHub repository: https://github.com/ComDec/3DCS.

Primary Area: datasets and benchmarks

Submission Number: 11768