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Beware of Machine Learning-Based Scoring Functions—On the Danger of Developing Black Boxes
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Outgoing claims: the papers this work cites, builds on, or argues with.
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3D-Quantitative Structure Activity Relationships of Biphenyl Carboxylic Acid MMP-3 Inhibitors: Exploring Automated Docking as Alignment Method
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Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database
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SFCscore: Scoring functions for affinity prediction of protein–ligand complexes
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The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure
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Advances in virtual screening
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Structure of a non-peptide inhibitor complexed with HIV-1 protease. Developing a cycle of structure-based drug design
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Support Vector Regression Scoring of Receptor–Ligand Complexes for Rank-Ordering and Virtual Screening of Chemical Libraries
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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
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Multiple Structures for Virtual Ligand Screening: Defining Binding Site Properties-Based Criteria to Optimize the Selection of the Query
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Encoding Protein–Ligand Interaction Patterns in Fingerprints and Graphs
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CSAR Benchmark Exercise 2011–2012: Evaluation of Results from Docking and Relative Ranking of Blinded Congeneric Series
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CSAR Data Set Release 2012: Ligands, Affinities, Complexes, and Docking Decoys
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/ci500080qopenalex-snapshot
Comparative Assessment of Scoring Functions on an Updated Benchmark: 1. Compilation of the Test Set
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Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?
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Evaluating Virtual Screening Methods: Good and Bad Metrics for the “Early Recognition” Problem
inferredcites →https://doi.org/10.1021/ci9000053openalex-snapshot
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/jm020406hopenalex-snapshot
Surflex: Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine
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The PDBbind Database: Methodologies and Updates
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Virtual Screening Workflow Development Guided by the “Receiver Operating Characteristic” Curve Approach. Application to High-Throughput Docking on Metabotropic Glutamate Receptor Subtype 4
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Diverse, High-Quality Test Set for the Validation of Protein−Ligand Docking Performance
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Quo Vadis, Virtual Screening? A Comprehensive Survey of Prospective Applications
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Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking
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Structure-based design of nonpeptide inhibitors specific for the human immunodeficiency virus 1 protease.
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A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
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A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval
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R: A Language and Environment for Statistical Computing
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges
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The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction
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A Practical Guide to Computational Tools for Engineering Biocatalytic Properties
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PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors
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In Silico Structure-Based Approach for Group Efficiency Estimation in Fragment-Based Drug Design Using Evaluation of Fragment Contributions
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Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-Generated Protein–Ligand Structures: Towards Per-Target Scoring Functions
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Combining the Fragment Molecular Orbital and GRID Approaches for the Prediction of Ligand–Metalloenzyme Binding Affinity: The Case Study of hCA II Inhibitors
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Scoring Functions of Protein-Ligand Interactions
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Scoring Functions of Protein-Ligand Interactions
inferred← citeshttps://doi.org/10.4310/cis.241021221101openalex-snapshot
Docking strategies for predicting protein-ligand interactions and their application to structure-based drug design
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AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
inferred← citeshttps://doi.org/10.48550/arxiv.1710.07400openalex-snapshot
Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks
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Review on the Evolving Landscape of Molecular Docking in Drug Discovery
inferred← citeshttps://doi.org/10.6084/m9.figshare.16822543openalex-snapshot
Additional file 1 of The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction
inferred← citeshttps://doi.org/10.6084/m9.figshare.26660296openalex-snapshot
Additional file 1 of Structure-based, deep-learning models for protein-ligand binding affinity prediction
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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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Design and application of deep learning methods to structure-based drug design
inferred← citeshttps://doi.org/10.70675/e211ffb4z6d6fz476ez9441z34c4cdf8d79aopenalex-snapshot
Fragment-based modelling of protein-RNA complexes for protein design
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