ConforFormer: Representation for Molecules through Understanding of Conformers 1. ConforFormer introduces a novel approach to molecular representation by explicitly accounting for the diversity of 3D conformations, addressing a critical gap in existing models that often rely solely on 2D molecular graphs. This method enhances the prediction of molecular properties by capturing the conformational diversity that influences chemical behavior. 2. The model employs a contrastive learning objective to align embeddings across multiple conformations of a molecule, producing task-agnostic and conformation-agnostic vector representations. These embeddings can be generated once and directly applied to downstream tasks such as property prediction and structural similarity analysis without extensive retraining. 3. ConforFormer builds upon the Uni-Mol architecture, leveraging its strengths in 3D structure representation while introducing a new weakly supervised contrastive learning objective. This allows the model to learn more informative and general-purpose representations without direct access to molecular graph information during training. 4. The study demonstrates significant improvements in performance on established chemical benchmarks compared to previous models, especially in tasks requiring an understanding of 3D molecular geometry. The model also shows an emergent capability to distinguish between conformers and isomers, suggesting it can infer molecular graph-like features from 3D geometries alone. 5. ConforFormer’s training on the OpenMolecules dataset further enhances its performance, achieving state-of-the-art results on several quantum-chemical benchmarks. The model’s ability to learn from high-quality molecular geometries highlights its potential for applications in drug discovery and materials science. 6. The authors introduce a new benchmark dataset, PharmIsomer, to evaluate the model’s ability to distinguish between conformers and isomers. ConforFormer demonstrates superior performance in this task, outperforming traditional methods and showing promise for similarity search applications in chemistry. 📜Paper: doi.org/10.26434/chemrxiv-20… #ConforFormer #MolecularRepresentation #ContrastiveLearning #Chemistry #MachineLearning