ArticleMolecular pharmaceutics2019
Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.
Article in Molecular pharmaceutics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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Who cites it
42 citing papers in PubMed, 65 citations in OpenAlex.
- Machine Learning in Tuberculosis Research: A Global Bibliometric Analysis of Diagnostic, Prognostic, and Drug Discovery Trends.Therapeutic innovation & regulatory science · 2026Article
- Machine Learning Application in Enhancing HIV Management and Treatment Outcomes: Revolutionizing HIV Infection.Current HIV research · 2026Review
- Repurposing lapatinib as a triple antagonist of chemokine receptors 3, 4, and 5.Molecular pharmacology · 2025Article
- Discovery of Dual Targeting GSK-3β/HIV-1 Reverse Transcriptase Inhibitors as Neuroprotective Antiviral Agents.ACS chemical neuroscience · 2025Article
- AI applications in HIV research: advances and future directions.Frontiers in microbiology · 2025Review
- The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications.Communications chemistry · 2024Article
- Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus.Tuberculosis (Edinburgh, Scotland) · 2024Article
- Identification of New Modulators and Inhibitors of Palmitoyl-Protein Thioesterase 1 for CLN1 Batten Disease and Cancer.ACS omega · 2024Article
- High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents.Journal of medicinal chemistry · 2023Article
- Reconstructing the cytokine view for the multi-view prediction of COVID-19 mortality.BMC infectious diseases · 2023Article
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- The Commoditization of AI for Molecule Design.Artificial intelligence in the life sciences · 2022Article
- Chalcones from Angelica keiskei (ashitaba) inhibit key Zika virus replication proteins.Bioorganic chemistry · 2022Article
- Machine Learning Models forMolecular pharmaceutics · 2022Article
- A coarse-refine segmentation network for COVID-19 CT images.IET image processing · 2022Article
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- Mycobacterium abscessus drug discovery using machine learning.Tuberculosis (Edinburgh, Scotland) · 2022Article
- Machine Learning for Discovery of New ADORA Modulators.Frontiers in pharmacology · 2022Article
- UV-adVISor: Attention-Based Recurrent Neural Networks to Predict UV-Vis Spectra.Analytical chemistry · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
The human immunodeficiency virus (HIV) causes over a million deaths every year and has a huge economic impact in many countries. The first class of drugs approved were nucleoside reverse transcriptase inhibitors. A newer generation of reverse transcriptase inhibitors have become susceptible to drug resistant strains of HIV, and hence, alternatives are urgently needed. We have recently pioneered the use of Bayesian machine learning to generate models with public data to identify new compounds for testing against different disease targets. The current study has used the NIAID ChemDB HIV, Opportunistic Infection and Tuberculosis Therapeutics Database for machine learning studies. We curated and cleaned data from HIV-1 wild-type cell-based and reverse transcriptase (RT) DNA polymerase inhibition assays. Compounds from this database with ≤1 μM HIV-1 RT DNA polymerase activity inhibition and cell-based HIV-1 inhibition are correlated (Pearson r = 0.44, n = 1137, p < 0.0001). Models were trained using multiple machine learning approaches (Bernoulli Naive Bayes, AdaBoost Decision Tree, Random Forest, support vector classification, k-Nearest Neighbors, and deep neural networks as well as consensus approaches) and then their predictive abilities were compared. Our comparison of different machine learning methods demonstrated that support vector classification, deep learning, and a consensus were generally comparable and not significantly different from each other using 5-fold cross validation and using 24 training and test set combinations. This study demonstrates findings in line with our previous studies for various targets that training and testing with multiple data sets does not demonstrate a significant difference between support vector machine and deep neural networks.
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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.