MolRep: A Deep Representation Learning Library for Molecular Property Prediction
Jiahua Rao, Shuangjia Zheng, Ying Song, Jianwen Chen, Chengtao Li, Jiancong Xie, Hui Yu Yang, Hongming Chen, Yuedong Yang · bioRxiv (Cold Spring Harbor Laboratory) · 2021
Abstract Summary Recently, novel representation learning algorithms have shown potential for predicting molecular properties. However, unified frameworks have not yet emerged for fairly measuring algorithmic progress, and experimental procedures of different representation models often lack rigorousness and are hardly reproducible. Herein, we have developed MolRep by unifying 16 state-of-the-art models across 4 popular molecular representations for application and comparison. Furthermore, we ran more than 12.5 million experiments to optimize hyperparameters for each method on 12 common benchmark data sets. As a result, CMPNN achieves the best results ranked the 1st in 5 out of 12 tasks with an average rank of 1.75. Relatively, ECC has good performance in classification tasks and MAT good for regression (both ranked 1st for 3 tasks) with an average rank of 2.71 and 2.6, respectively. Availability The source code is available at: https://github.com/biomed-AI/MolRep Supplementary information Supplementary data are available online.