TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity-Reference-Cited by-同舟云学术

TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity

Published:2022-12-15 Issue:1 Volume:63 Page:56-66
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Togo Maria Vittoria¹,Mastrolorito Fabrizio¹,Ciriaco Fulvio²,Trisciuzzi Daniela¹,Tondo Anna Rita¹,Gambacorta Nicola¹,Bellantuono Loredana³⁴,Monaco Alfonso⁴⁵,Leonetti Francesco¹,Bellotti Roberto⁴⁵,Altomare Cosimo Damiano¹,Amoroso Nicola¹⁴,Nicolotti Orazio¹^ORCID

Affiliation:

1. Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy

2. Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy

3. Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124Bari, Italy

4. Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125Bari, Italy

5. Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy

Funder

Universita degli Studi di Bari Aldo Moro

European Social Fund

Programma Operativo Nazionale Ricerca e Innovazione

Casa Sollievo della Sofferenza

Comune di Bovino

PIANO STRALCIO

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.2c01126

Reference72 articles.

1. In silico prediction of drug-induced developmental toxicity by using machine learning approaches

2. Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction

3. Quantitative Prediction of Hemolytic Toxicity for Small Molecules and Their Potential Hemolytic Fragments by Machine Learning and Recursive Fragmentation Methods

4. Machine-learning approaches in drug discovery: methods and applications

5. Deep Learning in Drug Discovery

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