mlsanityData to 7 Oct 2026

Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction

arXiv 2306.04886 ↗First posted 8 Jun 2023q-bio.BM cs.LGcited by 7

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Abstract (arXiv)

Protein-ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery. Recently, various deep learning-based models predict binding affinity by incorporating the three-dimensional structure of protein-ligand complexes as input and achieving astounding progress. However, due to the scarcity of high-quality training data, the generalization ability of current models is still limited. In addition, different bioassays use varying affinity measurement labels (i.e., IC50, Ki, Kd), and different experimental conditions inevitably introduce systematic noise, which poses a significant challenge to constructing high-precision affinity prediction models. To address these issues, we (1) propose Multi-task Bioassay Pre-training (MBP), a pre-training framework for structure-based PLBA prediction; (2) construct a pre-training dataset called ChEMBL-Dock with more than 300k experimentally measured affinity labels and about 2.8M docked three-dimensional structures. By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels. Experiments substantiate the capability of MBP as a general framework that can improve and be tailored to mainstream structure-based PLBA prediction tasks. To the best of our knowledge, MBP is the first affinity pre-training model and shows great potential for future development.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 32 of its 108 references that are in the map. First posted on arXiv 8 Jun 2023.

Builds on

32 of 108 references.

References · 32

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20 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 3; 2026: 2.

7 papers.

  1. 2023Geometric Deep Learning for Structure-Based Drug Design: A Surveycited by 2
  2. 2025Fast and Accurate Blind Flexible Dockingcited by 2
  3. 2025Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Reviewcited by 2
  4. 2023Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictionscited by 1
  5. 2025A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discoverycited by 0
  6. 2026Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensemblescited by 0
  7. 2026HBGSA: Hydrogen Bond Graph with Self-Attention for Drug-Target Binding Affinity Predictioncited by 0