mlsanityData to 7 Oct 2026

SparseChem: Fast and accurate machine learning model for small molecules

arXiv 2203.04676 ↗First posted 9 Mar 2022stat.ML cs.LGcited by 6

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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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 2 of its 15 references that are in the map. First posted on arXiv 9 Mar 2022.

Builds on

2 of 15 references.

References · 2

  1. 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 791
  2. 2014Multi-task Neural Networks for QSAR Predictionscited by 17

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 3.

6 papers.

  1. 2022Industry-Scale Orchestrated Federated Learning for Drug Discoverycited by 19
  2. 2024Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labelscited by 5
  3. 2024Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Modelscited by 2
  4. 2025BandiK: Efficient Multi-Task Decomposition Using a Multi-Bandit Frameworkcited by 0
  5. 2025DTI-GP: Bayesian operations for drug-target interactions using deep kernel Gaussian processescited by 0
  6. 2025Characterization of Transfer Using Multi-task Learning Curvescited by 0