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

https://doi.org/10.1021/ci200146e

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What this work says

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

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Effective energy function for proteins in solution

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CHARMM : A program for macromolecular energy, minimization, and dynamics calculations

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Automated docking with grid‐based energy evaluation

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Best Practices for QSAR Model Development, Validation, and Exploitation

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Knowledge-based scoring function to predict protein-ligand interactions

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Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures

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Structure-based virtual screening: an overview

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/ci9000053openalex-snapshot

Comparative Assessment of Scoring Functions on a Diverse Test Set

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

Electrophilicity Index

inferredcites →https://doi.org/10.1021/jm050260xopenalex-snapshot

Development of Quantitative Structure−Binding Affinity Relationship Models Based on Novel Geometrical Chemical Descriptors of the Protein−Ligand Interfaces

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

A Critical Assessment of Docking Programs and Scoring Functions

inferredcites →https://doi.org/10.1023/a:1007996124545openalex-snapshot

Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes

inferredcites →https://doi.org/10.1023/a:1020869118689openalex-snapshot

Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection

inferredcites →https://doi.org/10.1023/a:1025386326946openalex-snapshot

Rational selection of training and test sets for the development of validated QSAR models

inferredcites →https://doi.org/10.1073/pnas.202485799openalex-snapshot

A simple physical model for binding energy hot spots in protein–protein complexes

inferredcites →https://doi.org/10.1126/science.1089427openalex-snapshot

Design of a Novel Globular Protein Fold with Atomic-Level Accuracy

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Emergence of Protein Fold Families through Rational Design

What points at this work

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

inferred← citeshttps://doi.org/10.1002/0471266949.bmc301openalex-snapshot

Docking, Scoring, and Virtual Screening in Drug Discovery

inferred← citeshttps://doi.org/10.1002/jmr.2266openalex-snapshot

Latest developments in molecular docking: 2010–2011 in review

inferred← citeshttps://doi.org/10.1002/prot.24110openalex-snapshot

Scoring protein interaction decoys using exposed residues (SPIDER): A novel multibody interaction scoring function based on frequent geometric patterns of interfacial residues

inferred← citeshttps://doi.org/10.1002/prot.24928openalex-snapshot

Different combinations of atomic interactions predict protein‐small molecule and protein‐ DNA/RNA affinities with similar accuracy

inferred← citeshttps://doi.org/10.1007/s10822-016-9960-xopenalex-snapshot

A D3R prospective evaluation of machine learning for protein-ligand scoring

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Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization

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Improving the accuracy of pose prediction in molecular docking via structural filtering and conformational clustering

inferred← citeshttps://doi.org/10.1016/j.chemolab.2021.104384openalex-snapshot

Quantum chemical descriptors in quantitative structure–activity relationship models and their applications

inferred← citeshttps://doi.org/10.1021/acs.jcim.2c00127openalex-snapshot

Predicting Protein–Ligand Docking Structure with Graph Neural Network

inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00339openalex-snapshot

Predicting Binding Poses and Affinities in the CSAR 2013–2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential

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.6b00277openalex-snapshot

Beware of Unreliable Q2! A Comparative Study of Regression Metrics for Predictivity Assessment of QSAR Models

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

inferred← citeshttps://doi.org/10.1021/acs.jpcb.0c09051openalex-snapshot

GPU-Accelerated Flexible Molecular Docking

inferred← citeshttps://doi.org/10.1021/ci3002638openalex-snapshot

Integrating Ligand-Based and Protein-Centric Virtual Screening of Kinase Inhibitors Using Ensembles of Multiple Protein Kinase Genes and Conformations

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Discovery of Novel Tubulin Inhibitors via Structure-Based Hierarchical Virtual Screening

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Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

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

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Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General Results

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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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Structural features of falcipain-3 inhibitors: an in silico study

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Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data

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Multipose Binding in Molecular Docking

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