# Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+

arXiv: https://arxiv.org/abs/2303.16982 · HTML: https://mlsanity.com/paper/2303.16982/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 39 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 30 of 30)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [Directional Message Passing for Molecular Graphs](https://mlsanity.com/paper/2003.03123/) (2020)
- [GemNet: Universal Directional Graph Neural Networks for Molecules](https://mlsanity.com/paper/2106.08903/) (2021)
- [OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs](https://mlsanity.com/paper/2103.09430/) (2021)
- [Do Transformers Really Perform Bad for Graph Representation?](https://mlsanity.com/paper/2106.05234/) (2021)
- [Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs](https://mlsanity.com/paper/2206.11990/) (2022)
- [Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules](https://mlsanity.com/paper/2011.14115/) (2020)
- [DeeperGCN: All You Need to Train Deeper GCNs](https://mlsanity.com/paper/2006.07739/) (2020)
- [Pre-training Molecular Graph Representation with 3D Geometry](https://mlsanity.com/paper/2110.07728/) (2021)
- [The Open Catalyst 2020 (OC20) Dataset and Community Challenges](https://mlsanity.com/paper/2010.09990/) (2020)
- [Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics](https://mlsanity.com/paper/1707.09571/) (2017)
- [Large-Scale Chemical Language Representations Capture Molecular Structure and Properties](https://mlsanity.com/paper/2106.09553/) (2021)
- [Pure Transformers are Powerful Graph Learners](https://mlsanity.com/paper/2207.02505/) (2022)
- [TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials](https://mlsanity.com/paper/2202.02541/) (2022)
- [Spherical Message Passing for 3D Graph Networks](https://mlsanity.com/paper/2102.05013/) (2021)
- [3D Infomax improves GNNs for Molecular Property Prediction](https://mlsanity.com/paper/2110.04126/) (2021)
- [Global Self-Attention as a Replacement for Graph Convolution](https://mlsanity.com/paper/2108.03348/) (2021)
- [LieTransformer: Equivariant self-attention for Lie Groups](https://mlsanity.com/paper/2012.10885/) (2020)
- [One Transformer Can Understand Both 2D & 3D Molecular Data](https://mlsanity.com/paper/2210.01765/) (2022)
- [GRPE: Relative Positional Encoding for Graph Transformer](https://mlsanity.com/paper/2201.12787/) (2022)
- [Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets](https://mlsanity.com/paper/2203.04810/) (2022)
- [Your Transformer May Not be as Powerful as You Expect](https://mlsanity.com/paper/2205.13401/) (2022)
- [Graph convolutions that can finally model local structure](https://mlsanity.com/paper/2011.15069/) (2020)
- [GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction](https://mlsanity.com/paper/2212.02229/) (2022)
- [DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction](https://mlsanity.com/paper/2303.02875/) (2023)

## Built on by (most cited first, 22 of 22)

- [Position: Graph Foundation Models are Already Here](https://mlsanity.com/paper/2402.02216/) (2024)
- [Towards 3D Molecule-Text Interpretation in Language Models](https://mlsanity.com/paper/2401.13923/) (2024)
- [textttMiniMol: A Parameter-Efficient Foundation Model for Molecular Learning](https://mlsanity.com/paper/2404.14986/) (2024)
- [Uni-Mol2: Exploring Molecular Pretraining Model at Scale](https://mlsanity.com/paper/2406.14969/) (2024)
- [Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems](https://mlsanity.com/paper/2502.19227/) (2025)
- [Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers](https://mlsanity.com/paper/2402.04538/) (2024)
- [Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step Retrosynthesis](https://mlsanity.com/paper/2309.15798/) (2023)
- [Pre-Training Protein Bi-level Representation Through Span Mask Strategy On 3D Protein Chains](https://mlsanity.com/paper/2402.01481/) (2024)
- [MolVision: Molecular Property Prediction with Vision Language Models](https://mlsanity.com/paper/2507.03283/) (2025)
- [Gradual Optimization Learning for Conformational Energy Minimization](https://mlsanity.com/paper/2311.06295/) (2023)
- [Equivariant Masked Position Prediction for Efficient Molecular Representation](https://mlsanity.com/paper/2502.08209/) (2025)
- [Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design](https://mlsanity.com/paper/2407.06152/) (2024)
- [AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use](https://mlsanity.com/paper/2505.12650/) (2025)
- [Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions](https://mlsanity.com/paper/2603.00568/) (2026)
- [GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning](https://mlsanity.com/paper/2405.16206/) (2024)
- [nabla2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials](https://mlsanity.com/paper/2406.14347/) (2024)
- [Bridging Geometric States via Geometric Diffusion Bridge](https://mlsanity.com/paper/2410.24220/) (2024)
- [Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings](https://mlsanity.com/paper/2505.13087/) (2025)
- [Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials](https://mlsanity.com/paper/2507.00407/) (2025)
- [A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era](https://mlsanity.com/paper/2604.16586/) (2026)
- [Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference](https://mlsanity.com/paper/2608.18982/) (2026)
- [MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis](https://mlsanity.com/paper/2406.17797/) (2024)

## Abstract (arXiv)

Recent developments in deep learning have made remarkable progress in speeding up the prediction of quantum chemical (QC) properties by removing the need for expensive electronic structure calculations like density functional theory. However, previous methods learned from 1D SMILES sequences or 2D molecular graphs failed to achieve high accuracy as QC properties primarily depend on the 3D equilibrium conformations optimized by electronic structure methods, far different from the sequence-type and graph-type data. In this paper, we propose a novel approach called Uni-Mol+ to tackle this challenge. Uni-Mol+ first generates a raw 3D molecule conformation from inexpensive methods such as RDKit. Then, the raw conformation is iteratively updated to its target DFT equilibrium conformation using neural networks, and the learned conformation will be used to predict the QC properties. To effectively learn this update process towards the equilibrium conformation, we introduce a two-track Transformer model backbone and train it with the QC property prediction task. We also design a novel approach to guide the model's training process. Our extensive benchmarking results demonstrate that the proposed Uni-Mol+ significantly improves the accuracy of QC property prediction in various datasets. We have made the code and model publicly available at urlhttps://github.com/dptech-corp/Uni-Mol.