ReviewExpert opinion on drug discovery2025
Virtual screening: hope, hype, and the fine line in between.
Review in Expert opinion on drug discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Article
- In vitro enzymatic assays invalidate dihydromyricetin as a potential inhibitor against enterovirus A71 3C protease: beware of fluorescence quenching artifacts.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Article
- Review
- Evaluating generalization in protein-ligand cofolding methods.Nature structural & molecular biology · 2026Article
- Search for Specific Inhibitors Targeting Type IA Topoisomerases.Journal of molecular biology · 2026Review
- Interpretable machine learning rationalizes carbonic anhydrase inhibition via conformal and counterfactual prediction.Scientific reports · 2026Article
- Molecular deep learning at the edge of chemical space.Nature machine intelligence · 2026Article
- SLICE (SMARTS and Logic In ChEmistry): fast generation of molecules using advanced chemical synthesis logic and modern coding style.Journal of cheminformatics · 2025Article
- CHI3L1-targeted small molecules as glioblastoma therapies: Virtual screening-based discovery, biophysical validation, pharmacokinetic profiling, and evaluation in glioblastoma spheroids.European journal of medicinal chemistry · 2025Article
- Undirected Exploration of Binding Pockets with Flexible Topology.Journal of chemical theory and computation · 2025Article
- Inhibition Profiling ofMolecules (Basel, Switzerland) · 2025Article
- Digital Alchemy: The Rise of Machine and Deep Learning in Small-Molecule Drug Discovery.International journal of molecular sciences · 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
introductionTechnological advancements in virtual screening (VS) have rapidly accelerated its application in drug discovery, as reflected by the exponential growth in VS-related publications. However, a significant gap remains between the volume of computational predictions and their experimental validation. This discrepancy has led to a rise in the number of unverified 'claimed' hits which impedes the drug discovery efforts. AREAS COVERED: This perspective examines the current VS landscape, highlighting essential practices and identifying critical challenges, limitations, and common pitfalls. Using case studies and practices, this perspective aims to highlight strategies that can effectively mitigate or overcome these challenges. Furthermore, the perspective explores common approaches for addressing pharmacodynamic and pharmacokinetic issues in optimizing VS hits. EXPERT OPINION: VS has become a tried-and-true technique of drug discovery due to the rapid advances in computational methods and machine learning (ML) over the past two decades. Although each VS workflow varies depending on the chosen approach and methodology, integrated strategies that combine biological and in silico data have consistently yielded higher success rates. Moreover, the widespread adoption of ML has enhanced the integration of VS into the drug discovery pipeline. However, the absence of standardized evaluation criteria hinders the objective assessment of VS studies' success and the identification of optimal adoption methods.
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.