On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points-Reference-Cited by-同舟云学术

On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points

Published:2023-02-22 Issue:3 Volume:36 Page:446-464
ISSN:0893-228X
Container-title:Chemical Research in Toxicology
language:en
Short-container-title:Chem. Res. Toxicol.

Author:

Banerjee Arkaprava¹^ORCID,Roy Kunal¹^ORCID

Affiliation:

1. Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India

Funder

Science and Engineering Research Board

Life Sciences Research Board

Publisher

American Chemical Society (ACS)

Subject

Toxicology,General Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.2c00374

Reference35 articles.

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3. Sharma, B.; Chenthamarakshan, V.; Dhurandhar, A.; Pereira, S.; Hendler, J. A.; Dordick, J. S.; Das, P. Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. arXiv (Quantitative Methods), 2204.06614, ver. 1, April 2022. https://arxiv.org/abs/2204.06614 (accessed February 22, 2023).

4. DeepTox: Toxicity Prediction using Deep Learning

5. Mind the Gap! A Journey towards Computational Toxicology

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