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Target-Specific Native/Decoy Pose Classifier Improves the Accuracy of Ligand Ranking in the CSAR 2013 Benchmark

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/minf.201000061openalex-snapshot

Best Practices for QSAR Model Development, Validation, and Exploitation

inferredcites →https://doi.org/10.1007/s10822-008-9189-4openalex-snapshot

Community benchmarks for virtual screening

inferredcites →https://doi.org/10.1016/s1093-3263(01)00123-1openalex-snapshot

Beware of q2!

inferredcites →https://doi.org/10.1021/ci100015jopenalex-snapshot

ConfGen: A Conformational Search Method for Efficient Generation of Bioactive Conformers

inferredcites →https://doi.org/10.1021/ci100176xopenalex-snapshot

Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research

inferredcites →https://doi.org/10.1021/ci200146eopenalex-snapshot

Combined Application of Cheminformatics- and Physical Force Field-Based Scoring Functions Improves Binding Affinity Prediction for CSAR Data Sets

inferredcites →https://doi.org/10.1021/ci2002507openalex-snapshot

Cheminformatics Meets Molecular Mechanics: A Combined Application of Knowledge-Based Pose Scoring and Physical Force Field-Based Hit Scoring Functions Improves the Accuracy of Structure-Based Virtual Screening

inferredcites →https://doi.org/10.1021/ci200528dopenalex-snapshot

Recognizing Pitfalls in Virtual Screening: A Critical Review

inferredcites →https://doi.org/10.1021/ci300200ropenalex-snapshot

Predicting Ligand Binding Modes from Neural Networks Trained on Protein–Ligand Interaction Fingerprints

inferredcites →https://doi.org/10.1021/ci400025fopenalex-snapshot

CSAR Benchmark Exercise 2011–2012: Evaluation of Results from Docking and Relative Ranking of Blinded Congeneric Series

inferredcites →https://doi.org/10.1021/ci4000486openalex-snapshot

CSAR Data Set Release 2012: Ligands, Affinities, Complexes, and Docking Decoys

inferredcites →https://doi.org/10.1021/ci400094hopenalex-snapshot

Application of the Docking Program SOL for CSAR Benchmark

inferredcites →https://doi.org/10.1021/ci400120bopenalex-snapshot

SFCscore RF : A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein–Ligand Complexes

inferredcites →https://doi.org/10.1021/ci400216qopenalex-snapshot

Predicting Binding Affinity of CSAR Ligands Using Both Structure-Based and Ligand-Based Approaches

inferredcites →https://doi.org/10.1021/ci500091ropenalex-snapshot

Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?

inferredcites →https://doi.org/10.1021/ci500319fopenalex-snapshot

Structural Protein?Ligand Interaction Fingerprints (SPLIF) for Structure-Based Virtual Screening: Method and Benchmark Study

inferredcites →https://doi.org/10.1021/ci600342eopenalex-snapshot

Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints

inferredcites →https://doi.org/10.1021/ci8001167openalex-snapshot

MedusaScore: An Accurate Force Field-Based Scoring Function for Virtual Drug Screening

inferredcites →https://doi.org/10.1021/ja983494xopenalex-snapshot

Electrophilicity Index

inferredcites →https://doi.org/10.1021/jm0306430openalex-snapshot

Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy

inferredcites →https://doi.org/10.1021/jm050362nopenalex-snapshot

A Critical Assessment of Docking Programs and Scoring Functions

inferredcites →https://doi.org/10.1021/jm4004285openalex-snapshot

QSAR Modeling: Where Have You Been? Where Are You Going To?

inferredcites →https://doi.org/10.1038/nature03197openalex-snapshot

Virtual screening of chemical libraries

inferredcites →https://doi.org/10.1038/nature12443openalex-snapshot

Computational design of ligand-binding proteins with high affinity and selectivity

inferredcites →https://doi.org/10.1093/bioinformatics/btq112openalex-snapshot

A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

inferredcites →https://doi.org/10.1093/nar/gkq366openalex-snapshot

Dali server: conservation mapping in 3D

inferredcites →https://doi.org/10.2174/138161207782794257openalex-snapshot

Predictive QSAR Modeling Workflow, Model Applicability Domains, and Virtual Screening

inferredcites →https://doi.org/10.2174/157340908785747465openalex-snapshot

ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors

inferredcites →https://doi.org/10.3866/pku.whxb20090332openalex-snapshot

Conceptual Density Functional Theory and Some Recent Developments

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4818919openalex-extracted

Triptonide Ameliorates Diet-Accelerated Atherosclerosis and Fatty Liver Via Upregulating Anti-Atherosclerotic Insig1 and Inhibiting Key Lipogenic Signaling Pathways

inferred← citeshttps://doi.org/10.1002/jcc.26514openalex-snapshot

Developing a variation of 3D‐QSAR / MD method in drug design

inferred← citeshttps://doi.org/10.1002/med.21483openalex-snapshot

Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies

inferred← citeshttps://doi.org/10.1002/minf.201800030openalex-snapshot

Machine Learning Classification Models to Improve the Docking‐based Screening: A Case of PI3K‐Tankyrase Inhibitors

inferred← citeshttps://doi.org/10.1007/s10822-017-0062-1openalex-snapshot

Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2

inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2016.04.005openalex-snapshot

Machine learning optimization of cross docking accuracy

inferred← citeshttps://doi.org/10.1016/j.jmgm.2017.12.002openalex-snapshot

PLHINT: A knowledge-driven computational approach based on the intermolecular H bond interactions at the protein-ligand interface from docking solutions

inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00477openalex-snapshot

Integration of Ligand and Structure Based Approaches for CSAR-2014

inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00523openalex-snapshot

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma

inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00751openalex-snapshot

Docking and Scoring with Target-Specific Pose Classifier Succeeds in Native-Like Pose Identification But Not Binding Affinity Prediction in the CSAR 2014 Benchmark Exercise

inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00749openalex-snapshot

The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening

inferred← citeshttps://doi.org/10.1021/acs.jcim.9b01030openalex-snapshot

Pose Filter-Based Ensemble Learning Enables Discovery of Orally Active, Nonsteroidal Farnesoid X Receptor Agonists

inferred← citeshttps://doi.org/10.1080/17460441.2019.1664467openalex-snapshot

4D- quantitative structure–activity relationship modeling: making a comeback

inferred← citeshttps://doi.org/10.1080/17460441.2021.1929921openalex-snapshot

Computational representations of protein–ligand interfaces for structure-based virtual screening

inferred← citeshttps://doi.org/10.2174/1389557520666200204121129openalex-snapshot

Consensus Analyses in Molecular Docking Studies Applied to Medicinal Chemistry

inferred← citeshttps://doi.org/10.70675/d5fdc018z1d3fz4af2zb794zae806588d521openalex-snapshot

Development of novel algorithms for data-driven prediction of interactions between proteins and small molecules

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