A Binary Classification Model for Toxicity Prediction in Drug Design

• Génesis Varela-Salinas ^[1] ; Camacho-Cruz, Hugo E. ^[1] ; Alfredo Juárez Saldivar ^[2] ; Martinez-Rodriguez, Jose L. ^[1] ; Josue Rodriguez-Rodriguez ^[1] ; Carlos Garcia-Perez ^[3]
  1. [1] Universidad Autónoma de Tamaulipas

     Universidad Autónoma de Tamaulipas

     México

     
  2. [2] Instituto Politécnico Nacional

     Instituto Politécnico Nacional

     México

     
  3. [3] Helmholtz Zentrum München

     Helmholtz Zentrum München

     Kreisfreie Stadt München, Alemania

     

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• Localización: Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings / coord. por Hugo Sanjurjo González, Iker Pastor López , Pablo García Bringas , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2021, ISBN 978-3-030-86271-8, págs. 149-157
• Idioma: inglés
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• Resumen

  • 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.