update reference information.

Author: HongxinXiang

Diff for: README.md

@@ -15,7 +15,7 @@ The code is an official PyTorch-based implementation in the paper [Accurate pred
 
 ## Abstract
 
-The clinical efficacy and safety of a drug is determined by its molecular targets in the human proteome. However, proteome-wide evaluation of all compounds in human, or even animal models, is challenging. In this study, we present an unsupervised pre-training deep learning framework, termed ImageMol, from 10 million unlabeled drug-like molecules to predict molecular targets of candidate compounds. The ImageMol framework is designed to pretrain chemical representations from unlabeled molecular images based on local- and global-structural characteristics of molecules from pixels. We demonstrate high performance of ImageMol in evaluation of molecular properties (i.e., drug’s metabolism, brain penetration and toxicity) and molecular target profiles (i.e., human immunodeficiency virus) across 10 benchmark datasets. ImageMol shows high accuracy in identifying anti-SARS-CoV-2 molecules across 13 high-throughput experimental datasets from the National Center for Advancing Translational Sciences (NCATS) and we re-prioritized candidate clinical 3CL inhibitors for potential treatment of COVID-19. In summary, ImageMol is an active self-supervised image processing-based strategy that offers a powerful toolbox for computational drug discovery in a variety of human diseases, including COVID-19.
+The clinical efficacy and safety of a drug is determined by its molecular properties and targets in humans. However, proteome-wide evaluation of all compounds in humans, or even animal models, is challenging. In this study, we present an unsupervised pretraining deep learning framework, named ImageMol, pretrained on 10 million unlabelled drug-like, bioactive molecules, to predict molecular targets of candidate compounds. The ImageMol framework is designed to pretrain chemical representations from unlabelled molecular images on the basis of local and global structural characteristics of molecules from pixels. We demonstrate high performance of ImageMol in evaluation of molecular properties (that is, the drug’s metabolism, brain penetration and toxicity) and molecular target profiles (that is, beta-secretase enzyme and kinases) across 51 benchmark datasets. ImageMol shows high accuracy in identifying anti-SARS-CoV-2 molecules across 13 high-throughput experimental datasets from the National Center for Advancing Translational Sciences. Via ImageMol, we identified candidate clinical 3C-like protease inhibitors for potential treatment of COVID-19.
 
 ![framework](./imgs/framework.png)
 
@@ -286,12 +286,14 @@ If you want to process your own dataset and obtain molecular images, use the fol
 If you use ImageMol in scholary publications, presentations or to communicate with your satellite, please cite the following work that presents the algorithms used:
 ```bib
 @article{zeng2022accurate,
-  title={Accurate prediction of molecular targets using a self-supervised image representation learning framework},
+  title={Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework},
   author={Zeng, Xiangxiang and Xiang, Hongxin and Yu, Linhui and Wang, Jianmin and Li, Kenli and Nussinov, Ruth and Cheng, Feixiong},
-  journal={Research Square},
-  pages={rs--3},
+  journal={Nature Machine Intelligence},
+  volume={4},
+  number={11},
+  pages={1004--1016},
   year={2022},
-  publisher={American Journal Experts}
+  publisher={Nature Publishing Group}
 }
 ```