# Multi-task Neural Networks for QSAR Predictions

arXiv: https://arxiv.org/abs/1406.1231 · HTML: https://mlsanity.com/paper/1406.1231/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2014.

## Built on by (most cited first, 17 of 17)

- [DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations](https://mlsanity.com/paper/2201.09637/) (2022)
- [Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language](https://mlsanity.com/paper/2303.03363/) (2023)
- [Property-Aware Relation Networks for Few-Shot Molecular Property Prediction](https://mlsanity.com/paper/2107.07994/) (2021)
- [Context-enriched molecule representations improve few-shot drug discovery](https://mlsanity.com/paper/2305.09481/) (2023)
- [Industry-Scale Orchestrated Federated Learning for Drug Discovery](https://mlsanity.com/paper/2210.08871/) (2022)
- [BARTSmiles: Generative Masked Language Models for Molecular Representations](https://mlsanity.com/paper/2211.16349/) (2022)
- [Structured Multi-task Learning for Molecular Property Prediction](https://mlsanity.com/paper/2203.04695/) (2022)
- [SparseChem: Fast and accurate machine learning model for small molecules](https://mlsanity.com/paper/2203.04676/) (2022)
- [PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction](https://mlsanity.com/paper/2211.01978/) (2022)
- [Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships](https://mlsanity.com/paper/2105.08626/) (2021)
- [Probabilistic Contrastive Pretraining for Multi-task ADME Property Prediction](https://mlsanity.com/paper/2606.11508/) (2026)
- [Improving Compound Activity Classification via Deep Transfer and Representation Learning](https://mlsanity.com/paper/2111.07439/) (2021)
- [Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization](https://mlsanity.com/paper/2306.01095/) (2023)
- [FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction](https://mlsanity.com/paper/2401.09840/) (2024)
- [MLT-LE: predicting drug-target binding affinity with multi-task residual neural networks](https://mlsanity.com/paper/2209.06274/) (2022)
- [QComp: A QSAR-Based Data Completion Framework for Drug Discovery](https://mlsanity.com/paper/2405.11703/) (2024)
- [Comparison of Optimised Geometric Deep Learning Architectures, over Varying Toxicological Assay Data Environments](https://mlsanity.com/paper/2507.17775/) (2025)

## Abstract (arXiv)

Although artificial neural networks have occasionally been used for Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) studies in the past, the literature has of late been dominated by other machine learning techniques such as random forests. However, a variety of new neural net techniques along with successful applications in other domains have renewed interest in network approaches. In this work, inspired by the winning team's use of neural networks in a recent QSAR competition, we used an artificial neural network to learn a function that predicts activities of compounds for multiple assays at the same time. We conducted experiments leveraging recent methods for dealing with overfitting in neural networks as well as other tricks from the neural networks literature. We compared our methods to alternative methods reported to perform well on these tasks and found that our neural net methods provided superior performance.