Deliverable D2.1 – Toolbox improved in silico models for identification of PMT properties

This H2020 PROMISCES deliverable presents an enhanced toolbox of in silico models designed to identify PMT (Persistent, Mobile, and Toxic) properties of chemical substances.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-02-04
DOI
https://doi.org/10.5281/zenodo.14800915
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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article

Deliverable D2.1 – Toolbox improved in silico models for identification of PMT properties

Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
article

Deliverable D2.1 – Toolbox improved in silico models for identification of PMT properties

article en

Abstract

This H2020 PROMISCES deliverable presents an enhanced toolbox of in silico models designed to identify PMT (Persistent, Mobile, and Toxic) properties of chemical substances.

Zenodo (CERN European Organization for Nuclear Research)
Leiden University (NL), LabCorp (United States) (US), University of Gdańsk (PL), National Institute for Public Health and the Environment (NL), University of Amsterdam (NL)
Industry, innovation and infrastructure
Openalex Percentile: Top 59%
Computational Drug Discovery Methods
DOIOpen Access PDFOpenAlex
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