DeepPurpose: a deep learning library for drug–target interaction prediction

By Kexin Huang, Tianfan Fu, Lucas Glass, Marinka Zitnik, Cao Xiao, Jimeng Sun. Bioinform., 2020

447 citations recorded by the source corpus.

Abstract Summary Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets. Availability and implementation https://github.com/kexinhuang12345/DeepPurpose. Supplementary information Supplementary data are available at Bioinformatics online.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2319df931b3267ec11fa3bd70f424113771dfcc6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Tianfan Fu
2. Haifeng Wang
3. Jieqiong Lei
4. Shengchao Liu
5. Jimeng Sun
6. Xiaomin Fang
7. Donglong He
8. Jingbo Zhou

In Molecular Biology

1. Tianfan Fu
2. Haifeng Wang
3. Shengchao Liu
4. Jimeng Sun
5. Ying-Cheng Lu
6. Huazheng Wang
7. Hua Wu
8. Wenqi Wei

Citations in the corpus

• MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design (2022, methodology): “For molecular string representation, existing work considers advanced representation methods from other domains, including convolutional neural network (CNN) [19], recurrent neural network (RNN) [20], [21], [22], and Transformer [23], [24], [25].”
• Machine Learning for Synthetic Data Generation: a Review (2023, background): “The whole molecule space is around 10 60 [25], [74], [75].”
• Geometry-enhanced molecular representation learning for property prediction (2021, methodology): “To further boost the generalization ability of GeoGNN, we propose three geometry-level self-supervised learning tasks to pre-train GeoGNN: (1) the bond lengths prediction; (2) the bond angles prediction; (3) the atomic distance matrices prediction.”