SparseChem: Fast and accurate machine learning model for small molecules

TL;DR

SparseChem is a software package that enables fast, accurate machine learning on high-dimensional sparse biochemical data, supporting various models and accessible via command line and Python.

Contribution

It introduces a versatile, efficient library for machine learning on large-scale sparse biochemical datasets, with easy integration and broad functionality.

Findings

Supports millions of features and compounds

Allows training of classification, regression, and censored regression models

Accessible via command line and Python

Abstract

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.