Chemical representation learning for toxicity prediction-Reference-Cited by-同舟云学术

Chemical representation learning for toxicity prediction

Published:2023 Issue:3 Volume:2 Page:674-691
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Born Jannis¹²^ORCID,Markert Greta¹³^ORCID,Janakarajan Nikita¹⁴^ORCID,Kimber Talia B.⁵^ORCID,Volkamer Andrea⁵⁶^ORCID,Martínez María Rodríguez¹,Manica Matteo¹^ORCID

Affiliation:

1. IBM Research Europe, Zurich, Switzerland

2. Department of Biosystems Science and Engineering, ETH Zürich, Switzerland

3. Department of Chemistry and Applied Biosciences, ETH Zürich, Switzerland

4. Department of Computer Science, ETH Zürich, Switzerland

5. In silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany

6. Data Driven Drug Design, Saarland University, 66123 Saarbrücken, Germany

Abstract

A chemical language model for molecular property prediction: it outperforms prior art, is validated on a large, proprietary toxicity dataset, reveals cytotoxic motifs through attention & uses two uncertainty techniques to improve model reliability.

Funder

Horizon 2020 Framework Programme

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D2DD00099G

Reference123 articles.

1. Diagnosing the decline in pharmaceutical R&D efficiency

2. Can the pharmaceutical industry reduce attrition rates?

3. Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations

4. Large-scale prediction and testing of drug activity on side-effect targets

5. Estimation of clinical trial success rates and related parameters

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