ReviewInternational journal of molecular sciences2021
Deep Learning in Virtual Screening: Recent Applications and Developments.
Review in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
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
60 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation.International journal of molecular sciences · 2023Pooled it
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Artificial Intelligence-Driven Nanomedicine: From Drug Formulation and Nanocarrier Design to Clinical Translation.Pharmaceutics · 2026Review
- Two faces of drug repurposing data in machine learning-driven structure-based virtual screening supported by interaction fingerprints.Journal of computer-aided molecular design · 2026Article
- Towards selecting the virtual series of molecules using the central fragment via R-FBDD at the early stages of drug discovery.Molecular diversity · 2026Article
- Machine learning-assisted screening of natural product database for the identification of novel chalcone-based derivative as a potent DHODH inhibitor in cancer therapy.Discover oncology · 2026Article
- Discovery of potent and selective LRRK2 inhibitors with preserved activity against the G2019S mutant via multi-stage virtual screening.Journal of computer-aided molecular design · 2026Article
- A Leakage-Aware Drug Discovery Workflow for PKM2 and MAPK1 Integrating Scaffold Validation, Molecular Docking and Structural Triage.International journal of molecular sciences · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- AI-drivenPeerJ · 2026Article
- Computer-aided drug discovery: historical foundations, practical AI tools, and emerging ethical considerations.Frontiers in pharmacology · 2026Review
- Benchmarking Molecular Mutation Operators for Evolutionary Drug Design.International journal of molecular sciences · 2025Article
- Unveiling molecular moieties through hierarchical Grad-CAM graph explainability.BMC bioinformatics · 2025Article
- Unraveling Nature's Pharmacy: Transforming Medicinal Plants into Modern Therapeutic Agents.Pharmaceutics · 2025Review
- AI-based Assessment of Risk Factors for Coronary Heart Disease in Patients With Diabetes Mellitus and Construction of a Prediction Model for a Treatment Regimen.Reviews in cardiovascular medicine · 2025Article
- SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity.BMC biology · 2025Article
- PyComp: A Versatile Tool for Efficient Data Extraction, Conversion, and Management in High-throughput Virtual Drug Screening.Current computer-aided drug design · 2025Article
- Innovative Approaches in Molecular Docking for the Discovery of Novel Inhibitors Against Alzheimer's Disease.Current Alzheimer research · 2025Review
- Predicting Inhibition of CDK2 with SAnDReS: The Application of Machine Learning to Navigate the Scoring Function Space.Current medicinal chemistry · 2025Review
- Machine learning approaches for predicting protein-ligand binding sites from sequence data.Frontiers in bioinformatics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.
Indexed as
Identifiers
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.