ArticleJournal of computer-aided molecular design2018
Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges.
Article in Journal of computer-aided molecular design, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Data mining and molecular dynamics analysis to detect HIV-1 reverse transcriptase RNase H activity inhibitor.Molecular diversity · 2024Article
- Worth the Weight: Sub-Pocket EXplorer (SubPEx), a Weighted Ensemble Method to Enhance Binding-Pocket Conformational Sampling.Journal of chemical theory and computation · 2023Article
- Worth the weight: Sub-Pocket EXplorer (SubPEx), a weighted-ensemble method to enhance binding-pocket conformational sampling.bioRxiv : the preprint server for biology · 2023Article
- Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4.Journal of computer-aided molecular design · 2022Article
- D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.Journal of computer-aided molecular design · 2020Article
- Cross-docking benchmark for automated pose and ranking prediction of ligand binding.Protein science : a publication of the Protein Society · 2020Article
- Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3.Journal of computer-aided molecular design · 2019Article
- A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor.Future medicinal chemistry · 2018Article
- Improving small molecule virtual screening strategies for the next generation of therapeutics.Current opinion in chemical biology · 2018Review
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
The goal of virtual screening is to generate a substantially reduced and enriched subset of compounds from a large virtual chemistry space. Critical in these efforts are methods to properly rank the binding affinity of compounds. Prospective evaluations of ranking strategies in the D3R grand challenges show that for targets with deep pockets the best correlations (Spearman ρ ~ 0.5) were obtained by our submissions that docked compounds to the holo-receptors with the most chemically similar ligand. On the other hand, for targets with open pockets using multiple receptor structures is not a good strategy. Instead, docking to a single optimal receptor led to the best correlations (Spearman ρ ~ 0.5), and overall performs better than any other method. Yet, choosing a suboptimal receptor for crossdocking can significantly undermine the affinity rankings. Our submissions that evaluated the free energy of congeneric compounds were also among the best in the community experiment. Error bars of around 1 kcal/mol are still too large to significantly improve the overall rankings. Collectively, our top of the line predictions show that automated virtual screening with rigid receptors perform better than flexible docking and other more complex 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.