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

https://doi.org/10.1021/ci400120b
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What this work says

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

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The Challenge of Affinity Prediction: Scoring Functions for Structure‐Based Virtual Screening
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Scoring Functions for Protein–Ligand Interactions
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A general approach for developing system‐specific functions to score protein–ligand docked complexes using support vector inductive logic programming
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SFCscore: Scoring functions for affinity prediction of protein–ligand complexes
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PLS-regression: a basic tool of chemometrics
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Virtual Screening of Molecular Databases Using a Support Vector Machine
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Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets
inferredcites →https://doi.org/10.1021/ci200057eopenalex-snapshot
Comments on “Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets”: Significance for the Validation of Scoring Functions
inferredcites →https://doi.org/10.1021/ci200078fopenalex-snapshot
Support Vector Regression Scoring of Receptor–Ligand Complexes for Rank-Ordering and Virtual Screening of Chemical Libraries
inferredcites →https://doi.org/10.1021/ci200082topenalex-snapshot
CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes
inferredcites →https://doi.org/10.1021/ci200269qopenalex-snapshot
CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions
inferredcites →https://doi.org/10.1021/ci200274qopenalex-snapshot
DSX: A Knowledge-Based Scoring Function for the Assessment of Protein–Ligand Complexes
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Comparative Assessment of Scoring Functions on a Diverse Test Set
inferredcites →https://doi.org/10.1021/ci900056copenalex-snapshot
Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy
inferredcites →https://doi.org/10.1021/jm030580lopenalex-snapshot
The PDBbind Database: Collection of Binding Affinities for Protein−Ligand Complexes with Known Three-Dimensional Structures
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/jm030644sopenalex-snapshot
Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening
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The PDBbind Database: Methodologies and Updates
inferredcites →https://doi.org/10.1021/jm050362nopenalex-snapshot
A Critical Assessment of Docking Programs and Scoring Functions
inferredcites →https://doi.org/10.1021/jm051256oopenalex-snapshot
Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein−Ligand Complexes
inferredcites →https://doi.org/10.1021/jm900630qopenalex-snapshot
Structure-Guided Design of Potent and Selective Pyrimidylpyrrole Inhibitors of Extracellular Signal-Regulated Kinase (ERK) Using Conformational Control
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Random Forests
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Targeting the mitogen-activated protein kinase cascade to treat cancer
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Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go
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A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
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The Protein Data Bank
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Mechanism and Inhibition of LpxC: An Essential Zinc-Dependent Deacetylase of Bacterial Lipid A Synthesis
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What points at this work

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

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Deep Learning is Competing with Random Forest in Computational Docking
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TransScore: A Graph Model for Pose Scoring and Affinity Prediction Based on Transformer Convolution Network
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A Deep Learning Model for Protein-Ligand Affinity Prediction
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Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery
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Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study
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Correcting the impact of docking pose generation error on binding affinity prediction
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Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field
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GNINA 1.0: molecular docking with deep learning
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Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency
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An overview of recent advances and challenges in predicting compound-protein interaction (CPI)
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Computational methods for calculation of protein-ligand binding affinities in structure-based drug design
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Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function
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Bam: A Deep Learning Model for Protein-Ligand Affinity Prediction
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Developing Generalizable Scoring Functions for Molecular Docking: Challenges and Perspectives
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Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery
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Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances
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Insights into the Development of Chemotherapeutics Targeting PFKFB Enzymes
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Key Topics in Molecular Docking for Drug Design
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Protein–Ligand Docking in the Machine-Learning Era
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Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations
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Scoring Functions in Docking Experiments
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Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks
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libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications
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Molecular modelling and machine learning for the investigation of 2-oxazolidinone ribosomal antibacterials
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Review on the Evolving Landscape of Molecular Docking in Drug Discovery
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Additional file 1 of GNINA 1.0: molecular docking with deep learning
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Additional file 1 of Structure-based, deep-learning models for protein-ligand binding affinity prediction
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Analyse et exploitation des données associées aux variants d’IgG décrits dans les brevets : paysage brevet des variants d'anticorps IgG4 à activité antagoniste et mise au point d'un modèle de prédiction d'affinité pour FcRn par apprentissage machine
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Développement et application d’une approche de docking par fragments pour modéliser les interactions entre protéines et ARN simple-brin
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Development of novel algorithms for data-driven prediction of interactions between proteins and small molecules
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