ArticleRSC advances2021
Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds.
Article in RSC advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Large-scale comparative virtual screening identifies novel molecular targets underlying the multi-target activity of quercetin in psoriasis.Inflammopharmacology · 2026Article
- Activation of pro-survival autophagy by a small molecule promoting p62 oligomerization.The Journal of biological chemistry · 2026Article
- Molecular docking: a computational approach for the discovery of novel targets against visceral leishmaniasis.Journal of molecular modeling · 2026Review
- Mechanistic insights into the noncovalent inhibition of SARS-CoV-2 PLpro: a multiscale computational study.Journal of computer-aided molecular design · 2026Article
- Solvent-Site Prediction for Fragment Docking and Its Implication on Fragment-Based Drug Discovery.Journal of chemical information and modeling · 2025Article
- Assessing the Robustness and Scalability of Machine Learning Methods to Accelerate Ultralarge High-Throughput Docking Campaigns.ACS omega · 2025Article
- Discovery of novel HBV core protein inhibitors by high throughput virtual screening.Scientific reports · 2025Article
- A dataset for machine learning-based QSAR models establishment to screen beta-lactamase inhibitors using the FARM -BIOMOL chemical library.BMC research notes · 2025Article
- Identification of novel cyclin-dependent kinase 4/6 inhibitors from marine natural products.PloS one · 2025Article
- Utilizing machine learning-based QSAR model to overcome standalone consensus docking limitation in beta-lactamase inhibitors screening: a proof-of-concept study.BMC chemistry · 2024Article
- Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities.Computational and structural biotechnology journal · 2024Review
- Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs.International journal of molecular sciences · 2024Article
- Geometry Optimization Algorithms in Conjunction with the Machine Learning Potential ANI-2x Facilitate the Structure-Based Virtual Screening and Binding Mode Prediction.Biomolecules · 2024Article
- Consensus holistic virtual screening for drug discovery: a novel machine learning model approach.Journal of cheminformatics · 2024Article
- Molecular Dynamics and Other HPC Simulations for Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2024Article
- In Search of Novel SGLT2 Inhibitors by High-throughput Virtual Screening.Current drug discovery technologies · 2024Article
- Towards Effective Consensus Scoring in Structure-Based Virtual Screening.Interdisciplinary sciences, computational life sciences · 2023Article
- Article
- Pyridine Derivatives as Potential Inhibitors for Coronavirus SARS-CoV-2: A Molecular Docking Study.Bioinformatics and biology insights · 2023Article
- dockECR: Open consensus docking and ranking protocol for virtual screening of small molecules.Journal of molecular graphics & modelling · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results.
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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.