ReviewCurrent pharmaceutical design2018
Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review.
Review in Current pharmaceutical design, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 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.
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Who cites it
79 citing papers in PubMed.
- Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer's Disease.ACS chemical neuroscience · 2026Review
- Integrated RNA Sequencing and Multi-Bioinformatics Analyses Reveal the Molecular Mechanisms Underlying the Anti-Breast Cancer Effects of the Fritillariae Thunbergii Bulbus-Bolbostemmatis Rhizoma Herb Pair.Cell biochemistry and function · 2026Article
- AI-Driven Discovery of Prototype CLEC4M Inhibitors Targeting Marburg Virus Entry via Integrated Machine Learning and Molecular Modeling.International journal of molecular sciences · 2026Article
- Quantum computing applications in drug discovery.Briefings in bioinformatics · 2026Review
- Assessment of the rat acute oral toxicity of quinoline-based pharmaceutical scaffold molecules using QSTR, q-RASTR and machine learning methods.Molecular diversity · 2026Article
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- Drug-Target Interaction Prediction with PIGLET.bioRxiv : the preprint server for biology · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Benchmarking Molecular Mutation Operators for Evolutionary Drug Design.International journal of molecular sciences · 2025Article
- Machine learning and cheminformatics-based Identification of lichen-derived compounds targeting mutant PBP4Molecular diversity · 2025Article
- Machine learning models to identify lead compound and substitution optimization to have derived energetics and conformational stability through docking and MD simulations for sphingosine kinase 1.Molecular diversity · 2025Article
- Detecting alpha-synuclein aggregates with small molecules on single-molecule array.Chemical science · 2025Article
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- Clinical and Operational Applications of Artificial Intelligence and Machine Learning in Pharmacy: A Narrative Review of Real-World Applications.Pharmacy (Basel, Switzerland) · 2025Article
- Virtual screening: hope, hype, and the fine line in between.Expert opinion on drug discovery · 2025Review
- High-Throughput Virtual Screening of Small Molecule Modulators Against Viral Proteins.Methods in molecular biology (Clifton, N.J.) · 2025Article
- In Silico Validation of AI-Assisted Drugs in Healthcare.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Mechanisms to medicines: navigating drug repurposing strategies in Alzheimer's disease.Frontiers in aging neuroscience · 2025Review
- Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.Current medicinal chemistry · 2025Review
- Databases of ligand-binding pockets and protein-ligand interactions.Computational and structural biotechnology journal · 2024Review
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundVirtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increasing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered training set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity.
objectiveThe study aims to review ML-based methods used for VS and applications to Alzheimer's Disease (AD) drug discovery.
methodsTo update the current knowledge on ML for VS, we review thorough backgrounds, explanations, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN).
resultsAll techniques have found success in VS, but the future of VS is likely to lean more largely toward the use of neural networks - and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for conducting ML-based VS for potential therapeutics for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation.
conclusionDifferent ML techniques are powerful tools for VS, and they have advantages and disadvantages albeit. ML-based VS can be applied to AD drug development.
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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.