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de Souza Neto, L.R., Moreira-Filho, J.T., Neves, B.J., Maidana, R.L.B.R., Guimarães, A.C.R., Furnham, N., et al. (2020) In Silico Strategies to Support Fragment-To-Lead Optimization in Drug Discovery. Frontiers in Chemistry, 8, Article 93.
https://doi.org/10.3389/fchem.2020.00093

has been cited by the following article:

• TITLE: QSAR Studies on HIV-1 Protease—A Battle against HIV Using Computational Chemistry

  AUTHORS: Pokala Sai Kovela Sanjana, Radhika Vangala, Sree Kanth Sivan

  KEYWORDS: HIV, HIV-1 Protease, ADME, MLR, 2D QSAR, Molecular Docking

  JOURNAL NAME: Computational Chemistry, Vol.13 No.1, January 27, 2025

  ABSTRACT: HIV/AIDS is a major public health problem. Despite the advances in HIV treatment, the cure for all HIV patients still possesses a major challenge, which needs to be surpassed in coming years. One attractive target is HIV-1 Protease. Herein we report a new series of HIV-1 Protease Inhibitors incorporating stereo chemically defined tetrahydrofuran-tertiary amine-acetamide P2 ligand. Structure activity relationship studies like 2D QSAR, ADME studies and Molecular Docking were performed to design a chemical entity. A total of 37 compounds were chosen for this study divided into Training and Test set. A total of 27 training set molecules were taken for the SAR analysis (2D QSAR). The model was seen to have an incredible Correlation coefficient (R = 0.942) and also exhibited good predictive power confirmed by the high value of cross validated correlation coefficient (Q2 = 0.701). The outcome of this study gives us an insight for designing novel and peculiar HIV-1 Protease Inhibitors and provides us a guideline for designing compounds with improved novel HIV virus inhibitory potential.