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The development of a predictive model to identify potential HIV-1 attachment inhibitors

McGarry, Kenneth, Ashton, Mark, Gong, Yu and Hosny, Amer (2020) The development of a predictive model to identify potential HIV-1 attachment inhibitors. Computers in Biology and Medicine. ISSN 0010-4825

Item Type: Article


Despite the significant progress in managing patients infected with HIV through the development of Highly Active Anti-Retroviral Therapy (HAART), major challenges and opportunities remain to be explored. Of particular interest, is the binding of glycoprotein 120 (gp120) to the primary cellular receptor Cluster of Differentiation 4 (CD4). In this work we describe our two phased computational process to identify useful compounds capable of binding to the gp120 protein for therapeutic purposes. We identified 187 compounds from the literature that conform to active binding sites on these proteins and use these as training/test sets. The data in the form of quantitative structure-activity relationships (QSAR) is downloaded from the ZINC database and transformed using principal components analysis. In the first phase we developed a Radial Basis Function neural network model that identifies potential inhibitors from a virtual screen of a subset of the ZINC database. In the second phase we modelled the top performing compounds using the Discovery Studio docking and screening software. By employing this approach, we identified that those compounds with a LogP value of approx 2-4 performed well in the binding simulations while the lower scoring compounds do not bind well.

QSAR_Hosny.pdf - Accepted Version
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Uncontrolled Keywords: HIV, gp120, CD4, QSAR, RBF, neural network, PCA
Depositing User: Kenneth McGarry


Item ID: 11904
Identification Number:
ISSN: 0010-4825
Official URL:

Users with ORCIDS

ORCID for Kenneth McGarry: ORCID iD

Catalogue record

Date Deposited: 06 Apr 2020 06:49
Last Modified: 01 Apr 2021 02:38


Author: Kenneth McGarry ORCID iD
Author: Mark Ashton
Author: Yu Gong
Author: Amer Hosny
Author: Amer Hosny
Author: Mark Ashton
Author: Yu Gong
Author: Kenneth McGarry

University Divisions

Faculty of Technology > School of Computer Science


Computing > Data Science
Computing > Artificial Intelligence
Sciences > Biomedical Sciences
Computing > Databases

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