# Molecular machine learning with conformer ensembles

arXiv: https://arxiv.org/abs/2012.08452 · HTML: https://mlsanity.com/paper/2012.08452/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 27 of 27)

- [Pre-training Molecular Graph Representation with 3D Geometry](https://mlsanity.com/paper/2110.07728/) (2021)
- [3D Infomax improves GNNs for Molecular Property Prediction](https://mlsanity.com/paper/2110.04126/) (2021)
- [Geometric Deep Learning on Molecular Representations](https://mlsanity.com/paper/2107.12375/) (2021)
- [GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles](https://mlsanity.com/paper/2106.07802/) (2021)
- [ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs](https://mlsanity.com/paper/2206.08515/) (2022)
- [A Closer Look at AUROC and AUPRC under Class Imbalance](https://mlsanity.com/paper/2401.06091/) (2024)
- [ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation](https://mlsanity.com/paper/2410.22388/) (2024)
- [Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations](https://mlsanity.com/paper/2110.04383/) (2021)
- [Automated 3D Pre-Training for Molecular Property Prediction](https://mlsanity.com/paper/2306.07812/) (2023)
- [Generative Coarse-Graining of Molecular Conformations](https://mlsanity.com/paper/2201.12176/) (2022)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [Assigning Confidence to Molecular Property Prediction](https://mlsanity.com/paper/2102.11439/) (2021)
- [Graph Neural Networks in Modern AI-aided Drug Discovery](https://mlsanity.com/paper/2506.06915/) (2025)
- [Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks](https://mlsanity.com/paper/2310.00115/) (2023)
- [Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion](https://mlsanity.com/paper/2502.09890/) (2025)
- [Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks](https://mlsanity.com/paper/2402.01975/) (2024)
- [SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers](https://mlsanity.com/paper/2302.07754/) (2023)
- [MolMix: A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning](https://mlsanity.com/paper/2410.07981/) (2024)
- [Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential](https://mlsanity.com/paper/2207.11592/) (2022)
- [A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning](https://mlsanity.com/paper/2211.02130/) (2022)
- [Multiparameter Persistent Homology for Molecular Property Prediction](https://mlsanity.com/paper/2311.10808/) (2023)
- [Structure-Aware Fusion with Progressive Injection for Multimodal Molecular Representation Learning](https://mlsanity.com/paper/2510.23640/) (2025)
- [SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction](https://mlsanity.com/paper/2312.07633/) (2023)
- [The impact of conformer quality on learned representations of molecular conformer ensembles](https://mlsanity.com/paper/2502.13220/) (2025)
- [Flow-Matching Based Refiner for Molecular Conformer Generation](https://mlsanity.com/paper/2510.04878/) (2025)
- [MoleCLUEs: Molecular Conformers Maximally In-Distribution for Predictive Models](https://mlsanity.com/paper/2306.11681/) (2023)
- [Pre-training of Molecular GNNs via Conditional Boltzmann Generator](https://mlsanity.com/paper/2312.13110/) (2023)

## Abstract (arXiv)

Virtual screening can accelerate drug discovery by identifying promising candidates for experimental evaluation. Machine learning is a powerful method for screening, as it can learn complex structure-property relationships from experimental data and make rapid predictions over virtual libraries. Molecules inherently exist as a three-dimensional ensemble and their biological action typically occurs through supramolecular recognition. However, most deep learning approaches to molecular property prediction use a 2D graph representation as input, and in some cases a single 3D conformation. Here we investigate how the 3D information of multiple conformers, traditionally known as 4D information in the cheminformatics community, can improve molecular property prediction in deep learning models. We introduce multiple deep learning models that expand upon key architectures such as ChemProp and Schnet, adding elements such as multiple-conformer inputs and conformer attention. We then benchmark the performance trade-offs of these models on 2D, 3D and 4D representations in the prediction of drug activity using a large training set of geometrically resolved molecules. The new architectures perform significantly better than 2D models, but their performance is often just as strong with a single conformer as with many. We also find that 4D deep learning models learn interpretable attention weights for each conformer.