# Toward Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning

arXiv: https://arxiv.org/abs/2107.00719 · HTML: https://mlsanity.com/paper/2107.00719/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 2 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation](https://mlsanity.com/paper/1711.01468/) (2017)
- [Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction](https://mlsanity.com/paper/1908.06760/) (2019)
- [Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction](https://mlsanity.com/paper/1807.07716/) (2018)

## Built on by (most cited first, 4 of 4)

- [Interpretable bilinear attention network with domain adaptation improves drug-target prediction](https://mlsanity.com/paper/2208.02194/) (2022)
- [ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling](https://mlsanity.com/paper/2403.12995/) (2024)
- [Associative Learning Mechanism for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2205.15364/) (2022)
- [Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review](https://mlsanity.com/paper/2502.15346/) (2025)

## Abstract (arXiv)

Drug-target interaction (DTI) prediction plays a crucial role in drug discovery, and deep learning approaches have achieved state-of-the-art performance in this field. We introduce an ensemble of deep learning models (EnsembleDLM) for DTI prediction. EnsembleDLM only uses the sequence information of chemical compounds and proteins, and it aggregates the predictions from multiple deep neural networks. This approach not only achieves state-of-the-art performance in Davis and KIBA datasets but also reaches cutting-edge performance in the cross-domain applications across different bio-activity types and different protein classes. We also demonstrate that EnsembleDLM achieves a good performance (Pearson correlation coefficient and concordance index > 0.8) in the new domain with approximately 50% transfer learning data, i.e., the training set has twice as much data as the test set.