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.

本文提出了多任务生物测定预训练框架 (MBP)，该模型结合蛋白质 - 配体复合物的三维结构作为输入来预测蛋白质 - 配体结合亲和力，利用 ChEMBL-Dock 数据集对不同亲和力标签的预测进行多任务预训练，并且分类了相对排名是否来自同一生物测定，通过实验证明 MBP 作为通用框架能够改进并针对主流结构基预测任务。