# MLT-LE: predicting drug-target binding affinity with multi-task residual neural networks

arXiv: https://arxiv.org/abs/2209.06274 · HTML: https://mlsanity.com/paper/2209.06274/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Molecular De Novo Design through Deep Reinforcement Learning](https://mlsanity.com/paper/1704.07555/) (2017)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Massively Multitask Networks for Drug Discovery](https://mlsanity.com/paper/1502.02072/) (2015)
- [Multi-task Neural Networks for QSAR Predictions](https://mlsanity.com/paper/1406.1231/) (2014)
- [Docking-based Virtual Screening with Multi-Task Learning](https://mlsanity.com/paper/2111.09502/) (2021)

## Abstract (arXiv)

Assessing drug-target affinity is a critical step in the drug discovery and development process, but to obtain such data experimentally is both time consuming and expensive. For this reason, computational methods for predicting binding strength are being widely developed. However, these methods typically use a single-task approach for prediction, thus ignoring the additional information that can be extracted from the data and used to drive the learning process. Thereafter in this work, we present a multi-task approach for binding strength prediction. Our results suggest that these prediction can indeed benefit from a multi-task learning approach, by utilizing added information from related tasks and multi-task induced regularization.