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Varela-Salinas, G.* ; Camacho-Cruz, H.E.* ; Saldivar, A.J.* ; Martinez-Rodriguez, J.L.* ; Rodriguez-Rodriguez, J.* ; Garcia-Perez, C.

A binary classification model for toxicity prediction in drug design.

In: (16th International Conference on Hybrid Artificial Intelligent Systems, HAIS 202, 22-24 September 2021, Bilbao). Berlin [u.a.]: Springer, 2021. 149-157 (Lect. Notes Comput. Sc. ; 12886 LNAI)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Toxicity in drug design is a very important step prior to human or animal evaluation phases. Establishing drug toxicity involves the modification or redesign of the drug into an analog to suppress or reduce the toxicity. In this work, two different deep neural networks architectures and a proposed model to classify drug toxicity were evaluated. Three datasets of molecular descriptors were build based on SMILES from the Tox21 database and the AhR protein to test the accuracy prediction of the models. All models were tested with different sets of hyperparameters. The proposed model showed higher accuracy and lower loss compared to the other architectures. The number of descriptors played a key roll in the accuracy of the proposed model along with the Adam optimizer.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Deep Learning ; Drug Design ; Tox21 ; Toxicity
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel 16th International Conference on Hybrid Artificial Intelligent Systems, HAIS 202
Konferzenzdatum 22-24 September 2021
Konferenzort Bilbao
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 12886 LNAI, Heft: , Seiten: 149-157 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) CF Monoclonal Antibodies (CF-MAB)