Work

SparseChem: Fast and accurate machine learning model for small molecules

Year: 2022

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2203.04676

Authors: Arany, Adam, Simm, Jaak, Oldenhof, Martijn, Moreau, Yves

Institution: KU Leuven

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Nvidia, European Federation of Pharmaceutical Industries and Associations, Vlaams Supercomputer Centrum, Fonds Wetenschappelijk Onderzoek, KU Leuven, Vlaamse regering

Awards: Active learning and interpretation of computational model on tissue specific permeability for stock and virtual drug discovery library, G324259913, G3277036021, IMPULS-AI-2021

OpenAlex ID: W4221162707

Cites:

Cited by: 4

Related to: 10