Papers › Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+

Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+

16 Mar 2023arXiv:2303.16982archive 2025-07-28

Shuqi Lu, Zhifeng Gao, Di He, Linfeng Zhang, Guolin Ke

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 \url{https://github.com/dptech-corp/Uni-Mol}.

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dptech-corp/Uni-Mol officialmentioned in papermentioned on GitHubpytorchMIT report
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get_info deepmodeling/Uni-Mol/unimol_plus/scripts/get_3d_lmdb.py community (archive-listed) unverified MIT (permissive) · 92cdc968d05099c4 · report

Tasks

BenchmarkingGraph RegressionInitial Structure to Relaxed Energy (IS2RE), DirectPredictionProperty Prediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Graph Regression PCQM4Mv2-LSC Uni-Mol+ Test MAE 0.0705 #4 of 20 Archive leaderboard report
Graph Regression PCQM4Mv2-LSC Uni-Mol+ Validation MAE 0.0693 #4 of 20 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionDense ConnectionsDropoutGraph TransformerLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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