Classification of some chemical drugs by genetic algorithm and deep neural network hybrid method


Karakaplan M. , Avcu F. M.

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2021 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume:
  • Publication Date: 2021
  • Doi Number: 10.1002/cpe.6242
  • Title of Journal : CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
  • Keywords: deep learning, drug design, genetic algorithm, machine learning, molecular docking, molecular modeling, structure optimization

Abstract

Deep neural networks (DNN) and genetic algorithm (GA) are gaining importance quickly with many successful applications in the field of science and technology. They are indispensable tool for the numerical solution of difficult problems. It is possible to optimize DNNs using the GA and this combination can be used to classify data. In this article, some drugs are classified by Monte Carlo sampling with combination of GA and DNN due to stochastic nature of the domain, exponential number of variables and small number of chemical species. In addition to the values obtained from the databases of selected drugs, molecular dynamic and ab initio molecular mechanical calculation results were also used. The aim of this study is to generalize the molecular classification with the data obtained from chemical databases as well as molecular docking results by using the combination of deep learning and GA and its usability in drug design. The selected drugs are some agonist and antagonist drugs that bind to dopamine receptors, which are widely studied and well known in the literature. To train the DNN, input datasets were chosen by the GA framework written in pure Python named PyEvolve. Classification of drugs has been analyzed with the focus on orbital energies and docking results. It is possible to use this algorithm in many in silico calculations such as affinity and separation processes. The reliability of the algorithm was tested with the results given in the literature and the expected values were estimated at 93.8%.