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.
@article{arxiv.2304.12239,
title = {Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction},
author = {Zhifeng Gao and Xiaohong Ji and Guojiang Zhao and Hongshuai Wang and Hang Zheng and Guolin Ke and Linfeng Zhang},
journal= {arXiv preprint arXiv:2304.12239},
year = {2023}
}