# SparseChem: Fast and accurate machine learning model for small molecules

arXiv: https://arxiv.org/abs/2203.04676 · HTML: https://mlsanity.com/paper/2203.04676/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 2 of 2)

- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Multi-task Neural Networks for QSAR Predictions](https://mlsanity.com/paper/1406.1231/) (2014)

## Built on by (most cited first, 6 of 6)

- [Industry-Scale Orchestrated Federated Learning for Drug Discovery](https://mlsanity.com/paper/2210.08871/) (2022)
- [Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels](https://mlsanity.com/paper/2409.04313/) (2024)
- [Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models](https://mlsanity.com/paper/2407.14185/) (2024)
- [Characterization of Transfer Using Multi-task Learning Curves](https://mlsanity.com/paper/2512.24866/) (2025)
- [BandiK: Efficient Multi-Task Decomposition Using a Multi-Bandit Framework](https://mlsanity.com/paper/2512.24708/) (2025)
- [DTI-GP: Bayesian operations for drug-target interactions using deep kernel Gaussian processes](https://mlsanity.com/paper/2512.24810/) (2025)

## Abstract (arXiv)

SparseChem provides fast and accurate machine learning models for biochemical applications. Especially, the package supports very high-dimensional sparse inputs, e.g., millions of features and millions of compounds. It is possible to train classification, regression and censored regression models, or combination of them from command line. Additionally, the library can be accessed directly from Python. Source code and documentation is freely available under MIT License on GitHub.