mlsanityData to 7 Oct 2026

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction

arXiv 2304.12239 ↗First posted 24 Apr 2023q-bio.BM cs.LGcited by 4

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Abstract (arXiv)

Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in drug discovery. However, most DL based QSAR models are restricted to limited labeled data to achieve better performance, and also are sensitive to model scale and hyper-parameters. In this paper, we propose Uni-QSAR, a powerful Auto-ML tool for molecule property prediction tasks. Uni-QSAR combines molecular representation learning (MRL) of 1D sequential tokens, 2D topology graphs, and 3D conformers with pretraining models to leverage rich representation from large-scale unlabeled data. Without any manual fine-tuning or model selection, Uni-QSAR outperforms SOTA in 21/22 tasks of the Therapeutic Data Commons (TDC) benchmark under designed parallel workflow, with an average performance improvement of 6.09%. Furthermore, we demonstrate the practical usefulness of Uni-QSAR in drug discovery domains.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 73 references that are in the map. First posted on arXiv 24 Apr 2023.

Builds on

22 of 73 references.

References · 22

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10 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2025Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scalecited by 8
  2. 2024An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learningcited by 5
  3. 2026Suiren-1.0 Technical Report: A Family of Molecular Foundation Modelscited by 1
  4. 2026A Systematic Evaluation of Co-folding Model Representations for Small-Molecule Learningcited by 0