ArticleFrontiers in pharmacology2018
Improving Docking Performance Using Negative Image-Based Rescoring.
Article in Frontiers in pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 22 citations in OpenAlex.
- 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
- Building shape-focused pharmacophore models for effective docking screening.Journal of cheminformatics · 2024Article
- Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators.Molecules (Basel, Switzerland) · 2023Article
- Ligand-Enhanced Negative Images Optimized for Docking Rescoring.International journal of molecular sciences · 2022Article
- Identification of bio-active food compounds as potential SARS-CoV-2 PLpro inhibitors-modulators via negative image-based screening and computational simulations.Computers in biology and medicine · 2022Article
- Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening.Journal of chemical information and modeling · 2022Article
- A Perspective: Active Role of Lipids in Neurotransmitter Dynamics.Molecular neurobiology · 2020Review
- Screening of Natural Products Targeting SARS-CoV-2-ACE2 Receptor Interface - A MixMD Based HTVS Pipeline.Frontiers in chemistry · 2020Article
- Getting Docking into Shape Using Negative Image-Based Rescoring.Journal of chemical information and modeling · 2019Article
- A Practical Perspective: The Effect of Ligand Conformers on the Negative Image-Based Screening.International journal of molecular sciences · 2019Article
- PeptoGrid-Rescoring Function for AutoDock Vina to Identify New Bioactive Molecules from Short Peptide Libraries.Molecules (Basel, Switzerland) · 2019Article
- Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods.Frontiers in pharmacology · 2019Article
- Discovery of Retinoic Acid-Related Orphan Receptor γt Inverse Agonists via Docking and Negative Image-Based Screening.ACS omega · 2018Article
- Detection of Binding Sites on SARS-CoV-2 Spike Protein Receptor-Binding Domain by Molecular Dynamics Simulations in Mixed Solvents.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the large computational costs of molecular docking, the default scoring functions are often unable to recognize the active hits from the inactive molecules in large-scale virtual screening experiments. Thus, even though a correct binding pose might be sampled during the docking, the active compound or its biologically relevant pose is not necessarily given high enough score to arouse the attention. Various rescoring and post-processing approaches have emerged for improving the docking performance. Here, it is shown that the very early enrichment (number of actives scored higher than 1% of the highest ranked decoys) can be improved on average 2.5-fold or even 8.7-fold by comparing the docking-based ligand conformers directly against the target protein's cavity shape and electrostatics. The similarity comparison of the conformers is performed without geometry optimization against the negative image of the target protein's ligand-binding cavity using the negative image-based (NIB) screening protocol. The viability of the NIB rescoring or the R-NiB, pioneered in this study, was tested with 11 target proteins using benchmark libraries. By focusing on the shape/electrostatics complementarity of the ligand-receptor association, the R-NiB is able to improve the early enrichment of docking essentially without adding to the computing cost. By implementing consensus scoring, in which the R-NiB and the original docking scoring are weighted for optimal outcome, the early enrichment is improved to a level that facilitates effective drug discovery. Moreover, the use of equal weight from the original docking scoring and the R-NiB scoring improves the yield in most cases.
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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.