Deep Learning Based-Virtual Screening Using 2D Pharmacophore Fingerprint in Drug Discovery

• Published in: Neural processing letters 2022-12, Vol.54 (6), p.5669-5691
• Main Authors: Hadiby, Seloua, Mohamed Ben Ali, Yamina
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Artificial neural networks; Bioinformatics; Biological activity; Biological properties; Cell cycle; Chemical compounds; Complex Systems; Computational Intelligence; Computer Science; Deep learning; Drug development; Fingerprints; Graphs; Ligands; Machine learning; Molecular properties; Pharmaceutical industry; Proteins; R&D; Receptors; Research & development; Screening; Support vector machines; Toxicity
• ISSN: 1370-4621; 1573-773X
• DOI: 10.1007/s11063-022-10879-6

Predicting biological activity and molecular properties is one of the most important goals in the pharmaceutical and bioinformatics field in order to discover potential new drugs. Although machine learning methods have been used in drug discovery for a long time with good efficacy, the use of deep learning has proven its superiority in most cases. In this paper, we present a virtual screening procedure based on deep learning that aims to classify a set of chemical compounds as regards their biological activity on a particular receptor. The molecules are described with 2D pharmacophore fingerprints, which use the coordinates of atoms in 2D space to calculate them. Two deep learning models are proposed, the first is a deep neural network that uses as input data the fingerprint represented as a 1D vector. The second model is a convolutional neural network that uses the same input data after reshaping it into a 2D vector. Our models were trained on a dataset of active and inactive chemical compounds on cyclin A kinase1 receptor a very important protein family. The results have proven that the proposed models are efficient and comparable with some widely used machine learning methods in drug discovery.