Home
Deutsch
Advanced Search
Browse by ...
Publication overview
Contact persons
Help
DOI
 |
|
Open Access Green as soon as Postprint is submitted to ZB.
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)
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.
Altmetric
Additional Metrics?
Edit extra informations
Login
Publication type
Article: Conference contribution
Keywords
Deep Learning ; Drug Design ; Tox21 ; Toxicity
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
16th International Conference on Hybrid Artificial Intelligent Systems, HAIS 202
Conference Date
22-24 September 2021
Conference Location
Bilbao
Quellenangaben
Volume: 12886 LNAI,
Pages: 149-157
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
CF Monoclonal Antibodies (CF-MAB)