# DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction

arXiv: https://arxiv.org/abs/2004.08919 · HTML: https://mlsanity.com/paper/2004.08919/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction](https://mlsanity.com/paper/2106.06130/) (2021)
- [SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations](https://mlsanity.com/paper/2105.02711/) (2021)
- [De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning](https://mlsanity.com/paper/2205.10473/) (2022)
- [Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19](https://mlsanity.com/paper/2102.04977/) (2021)
- [Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates](https://mlsanity.com/paper/2105.10489/) (2021)

## Abstract (arXiv)

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 deep learning 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.