# Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review

arXiv: https://arxiv.org/abs/2502.15346 · HTML: https://mlsanity.com/paper/2502.15346/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 419 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 30 of 34)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [Evaluating Protein Transfer Learning with TAPE](https://mlsanity.com/paper/1906.08230/) (2019)
- [Graph Neural Networks with convolutional ARMA filters](https://mlsanity.com/paper/1901.01343/) (2019)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing](https://mlsanity.com/paper/2007.06225/) (2020)
- [MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction](https://mlsanity.com/paper/2004.11424/) (2020)
- [Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity](https://mlsanity.com/paper/2107.10670/) (2021)
- [DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks](https://mlsanity.com/paper/1806.07537/) (2018)
- [Development and evaluation of a deep learning model for protein-ligand binding affinity prediction](https://mlsanity.com/paper/1712.07042/) (2017)
- [Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations](https://mlsanity.com/paper/1906.05017/) (2019)
- [PotentialNet for Molecular Property Prediction](https://mlsanity.com/paper/1803.04465/) (2018)
- [DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences](https://mlsanity.com/paper/1811.02114/) (2018)
- [Interpretable bilinear attention network with domain adaptation improves drug-target prediction](https://mlsanity.com/paper/2208.02194/) (2022)
- [DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science](https://mlsanity.com/paper/2106.14232/) (2021)
- [Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference](https://mlsanity.com/paper/2005.07704/) (2020)
- [Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction](https://mlsanity.com/paper/1908.06760/) (2019)
- [GEFA: Early Fusion Approach in Drug-Target Affinity Prediction](https://mlsanity.com/paper/2009.12146/) (2020)
- [DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/1912.00318/) (2019)
- [DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/2003.13902/) (2020)
- [Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts](https://mlsanity.com/paper/1912.12553/) (2019)
- [Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction](https://mlsanity.com/paper/2012.08194/) (2020)
- [Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinity](https://mlsanity.com/paper/2201.06872/) (2022)
- [Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity](https://mlsanity.com/paper/2203.11458/) (2022)
- [SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction](https://mlsanity.com/paper/2206.07015/) (2022)
- [Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction](https://mlsanity.com/paper/2306.04886/) (2023)

## Built on by (most cited first, 2 of 2)

- [GRAM-DTI: adaptive multimodal representation learning for drug target interaction prediction](https://mlsanity.com/paper/2509.21971/) (2025)
- [Where Black-box Drug-Target Interaction Prediction Models Look: Cross-Method Explainability](https://mlsanity.com/paper/2606.14245/) (2026)

## Abstract (arXiv)

Drug discovery remains a slow and expensive process that involves many steps, from detecting the target structure to obtaining approval from the Food and Drug Administration (FDA), and is often riddled with safety concerns. Accurate prediction of how drugs interact with their targets and the development of new drugs by using better methods and technologies have immense potential to speed up this process, ultimately leading to faster delivery of life-saving medications. Traditional methods used for drug-target interaction prediction show limitations, particularly in capturing complex relationships between drugs and their targets. As an outcome, deep learning models have been presented to overcome the challenges of interaction prediction through their precise and efficient end results. By outlining promising research avenues and models, each with a different solution but similar to the problem, this paper aims to give researchers a better idea of methods for even more accurate and efficient prediction of drug-target interaction, ultimately accelerating the development of more effective drugs. A total of 180 prediction methods for drug-target interactions were analyzed throughout the period spanning 2016 to 2025 using different frameworks based on machine learning, mainly deep learning and graph neural networks. Additionally, this paper discusses the novelty, architecture, and input representation of these models.