ArticleJournal of cheminformatics2018
HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors.
Article in Journal of cheminformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 34 citations in OpenAlex.
- i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus.Frontiers in pharmacology · 2025Article
- Anti-EBV: Artificial intelligence driven predictive modeling for repurposing drugs as potential antivirals against Epstein-Barr virus.Computational and structural biotechnology journal · 2025Article
- Article
- Drug Reprofiling to Identify Potential HIV-1 Protease Inhibitors.Molecules (Basel, Switzerland) · 2023Article
- FP-MAP: an extensive library of fingerprint-based molecular activity prediction tools.Frontiers in chemistry · 2023Article
- Discovery of Novel HIV Protease Inhibitors Using Modern Computational Techniques.International journal of molecular sciences · 2022Article
- Computational Identification of Potential Multitarget Inhibitors of Nipah Virus by Molecular Docking and Molecular Dynamics.Microorganisms · 2022Article
- Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches.Computational and structural biotechnology journal · 2022Article
- Computational identification of repurposed drugs against viruses causing epidemics and pandemics via drug-target network analysis.Computers in biology and medicine · 2021Article
- Small Molecules of Natural Origin as Potential Anti-HIV Agents: A Computational Approach.Life (Basel, Switzerland) · 2021Article
- LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds.Journal of cheminformatics · 2021Article
- Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning.Computational and structural biotechnology journal · 2021Article
- Phenanthridine derivatives as potential HIV-1 protease inhibitors.Biomedical reports · 2020Article
- AntiHIV-Pred: web-resource for in silico prediction of anti-HIV/AIDS activity.Bioinformatics (Oxford, England) · 2020Article
- Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.Molecular pharmaceutics · 2019Article
- Computational Identification of Inhibitors Using QSAR Approach Against Nipah Virus.Frontiers in pharmacology · 2019Article
- Anti-flavi: A Web Platform to Predict Inhibitors ofFrontiers in microbiology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.