ReviewFundamental research2024
A review of deep learning methods for ligand based drug virtual screening.
Review in Fundamental research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors.Molecular diversity · 2026Article
- An Integrated Consensus Machine Learning and Structure-Based Workflow for the Discovery of Novel Tankyrase 1 Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2026Article
- GPCR-GO: Relation-aware graph learning for predicting Gene Ontology terms of G protein-coupled receptors.PLoS computational biology · 2026Article
- Venom-Derived Enzyme Inhibitors as Anticancer Agents: Structure-Activity Relationships, Molecular Targets and Mechanistic Insights.Molecules (Basel, Switzerland) · 2026Review
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- Review
- Amphibian-derived peptides as novel therapeutics for skin wound healing: Mechanisms, applications, and challenges.Zoological research · 2026Review
- Integrated computational and experimental analysis explores FOLH1 expression patterns across cancers and nominates melatonin as a potential modulator in prostate cancer models.PLoS computational biology · 2026Article
- Ligand-based graph neural network, molecular dynamics and biological evaluation for identification of potential FGFR1 kinase inhibitors.Molecular diversity · 2026Article
- GNN-MA: Soft Molecular Alignment with Cross-Graph Attention for Ligand-Based Virtual Screening.Molecules (Basel, Switzerland) · 2026Article
- Discovery of Novel Molecular Scaffolds to OvercomeInternational journal of molecular sciences · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Learning physical interactions to compose biological large language models.Communications chemistry · 2026Review
- Discovery of novel GluN1/GluN3A NMDA receptor inhibitors using a deep learning-based method.Acta pharmacologica Sinica · 2025Article
- Computational Discovery of Potent Nucleoprotein Inhibitors for Influenza A Virus: Validation Through QM/MM Analysis and Experimental Binding Assays.Molecules (Basel, Switzerland) · 2025Article
- DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction.Journal of pharmaceutical analysis · 2025Article
- Recent advances in molecular representation methods and their applications in scaffold hopping.npj drug discovery · 2025Review
- Advances and challenges in drug design against dental caries: Application ofJournal of pharmaceutical analysis · 2025Review
- Spectra-descriptor-based machine learning for predicting protein-ligand interactions.Chemical science · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent. It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening, select different models for different drug screening problems, exploit the advantages of deep learning models, and further improve the capability of deep learning in drug virtual screening. This review first introduces the basic concepts of drug virtual screening, common datasets, and data representation methods. Then, large numbers of common deep learning methods for drug virtual screening are compared and analyzed. In addition, a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening. Finally, the existing challenges and future directions in the field of virtual screening are presented.
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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.