ArticleFuture medicinal chemistry2018
Deep learning and virtual drug screening.
Article in Future medicinal chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
50 citing papers in PubMed.
- Image-guided activation of drugs with electromagnetic radiation.Nature chemical biology · 2026Review
- Advancing Antiviral Design: Integrating Natural Products, Computation and Targeted Delivery.Chemical biology & drug design · 2026Review
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- Deep Learning Pipeline for Accelerating Virtual Screening in Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Identification of potent inhibitors of potential VEGFR2: a graph neural network-based virtual screening andJournal of enzyme inhibition and medicinal chemistry · 2025Article
- An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor.Acta pharmacologica Sinica · 2025Article
- Unveiling molecular moieties through hierarchical Grad-CAM graph explainability.BMC bioinformatics · 2025Article
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- A beginner's approach to deep learning applied to VS and MD techniques.Journal of cheminformatics · 2025Review
- Virtual Screening and Bioassay of Novel Protoporphyrinogen Oxidase andMolecules (Basel, Switzerland) · 2025Article
- A machine learning method for predicting molecular antimicrobial activity.Scientific reports · 2025Article
- Natural Compounds Targeting Thymic Stromal Lymphopoietin (TSLP): A Promising Therapeutic Strategy for Atopic Dermatitis.Biomolecules · 2024Article
- ACE inhibitors and their interaction with systems and molecules involved in metabolism.Heliyon · 2024Review
- Computational Drug Design Strategies for Fighting the COVID-19 Pandemic.Advances in experimental medicine and biology · 2024Article
- Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Mutational analysis of SARS-CoV-2 ORF6-KPNA2 binding interface and identification of potent small molecule inhibitors to recuse the host immune system.Frontiers in immunology · 2023Article
- Exploring the natural products chemical space to abrogate the F3L-dsRNA interface of monkeypox virus to enhance the immune responses using molecular screening and free energy calculations.Frontiers in pharmacology · 2023Article
- LeMeDISCO is a computational method for large-scale prediction & molecular interpretation of disease comorbidity.Communications biology · 2022Article
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.
Funding
Abstract
Current drug development is still costly and slow given tremendous technological advancements in drug discovery and medicinal chemistry. Using machine learning (ML) to virtually screen compound libraries promises to fix this for generating drug leads more efficiently and accurately. Herein, we explain the broad basics and integration of both virtual screening (VS) and ML. We then discuss artificial neural networks (ANNs) and their usage for VS. The ANN is emerging as the dominant classifier for ML in general, and has proven its utility for both structure-based and ligand-based VS. Techniques such as dropout, multitask learning and convolution improve the performance of ANNs and enable them to take on chemical meaning when learning about the drug-target-binding activity of compounds.
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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.