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Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/9783527613083openalex-snapshot
Virtual Screening for Bioactive Molecules
inferredcites →https://doi.org/10.1002/9783527678679.dg08217openalex-snapshot
National Center for Biotechnology Information (NCBI)
inferredcites →https://doi.org/10.1007/978-1-4471-0347-9_4openalex-snapshot
Principal Component Analysis
inferredcites →https://doi.org/10.1007/s10822-004-4067-1openalex-snapshot
Measuring CAMD technique performance: A virtual screening case study in the design of validation experiments
inferredcites →https://doi.org/10.1007/s10822-007-9164-5openalex-snapshot
LASSO—ligand activity by surface similarity order: a new tool for ligand based virtual screening
inferredcites →https://doi.org/10.1007/s10822-007-9167-2openalex-snapshot
Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection?
inferredcites →https://doi.org/10.1007/s10822-008-9169-8openalex-snapshot
Evaluating docking programs: keeping the playing field level
inferredcites →https://doi.org/10.1007/s10822-008-9181-zopenalex-snapshot
Managing bias in ROC curves
inferredcites →https://doi.org/10.1007/s10822-008-9189-4openalex-snapshot
Community benchmarks for virtual screening
inferredcites →https://doi.org/10.1007/s10822-008-9199-2openalex-snapshot
Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening
inferredcites →https://doi.org/10.1016/j.ddtec.2004.08.004openalex-snapshot
Scoring functions for protein–ligand interactions: a critical perspective
inferredcites →https://doi.org/10.1016/j.drudis.2006.05.014openalex-snapshot
Screening in a spirit haunted world
inferredcites →https://doi.org/10.1016/j.patrec.2005.10.010openalex-snapshot
An introduction to ROC analysis
inferredcites →https://doi.org/10.1016/s0076-6879(96)66012-1openalex-snapshot
[10] Entrez: Molecular biology database and retrieval system
inferredcites →https://doi.org/10.1021/ci00017a013openalex-snapshot
Computation and management of chemical properties in CACTVS: An extensible networked approach toward modularity and compatibility
inferredcites →https://doi.org/10.1021/ci025589vopenalex-snapshot
Calculation of Intersubstituent Similarity Using R-Group Descriptors
inferredcites →https://doi.org/10.1021/ci034231bopenalex-snapshot
Comparison of Fingerprint-Based Methods for Virtual Screening Using Multiple Bioactive Reference Structures
inferredcites →https://doi.org/10.1021/ci034289qopenalex-snapshot
Virtual Screening Using Protein−Ligand Docking: Avoiding Artificial Enrichment
inferredcites →https://doi.org/10.1021/ci049714+openalex-snapshot
ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
inferredcites →https://doi.org/10.1021/ci049850eopenalex-snapshot
Ensemble Methods for Classification in Cheminformatics
inferredcites →https://doi.org/10.1021/ci0498719openalex-snapshot
Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D): Evaluation of Performance
inferredcites →https://doi.org/10.1021/ci050457yopenalex-snapshot
ErG: 2D Pharmacophore Descriptions for Scaffold Hopping
inferredcites →https://doi.org/10.1021/ci050504mopenalex-snapshot
An Empirical Process for the Design of High-Throughput Screening Deck Filters
inferredcites →https://doi.org/10.1021/ci6003493openalex-snapshot
Measuring CAMD Technique Performance. 2. How “Druglike” Are Drugs? Implications of Random Test Set Selection Exemplified Using Druglikeness Classification Models
inferredcites →https://doi.org/10.1021/ci600418uopenalex-snapshot
Introduction of an Information-Theoretic Method to Predict Recovery Rates of Active Compounds for Bayesian in Silico Screening: Theory and Screening Trials
inferredcites →https://doi.org/10.1021/ci600426eopenalex-snapshot
Evaluating Virtual Screening Methods: Good and Bad Metrics for the “Early Recognition” Problem
inferredcites →https://doi.org/10.1021/ci6005504openalex-snapshot
Understanding False Positives in Reporter Gene Assays: in Silico Chemogenomics Approaches To Prioritize Cell-Based HTS Data
inferredcites →https://doi.org/10.1021/ci700092vopenalex-snapshot
Large-Scale Annotation of Small-Molecule Libraries Using Public Databases
inferredcites →https://doi.org/10.1021/ci700099uopenalex-snapshot
Impact of Benchmark Data Set Topology on the Validation of Virtual Screening Methods: Exploration and Quantification by Spatial Statistics
inferredcites →https://doi.org/10.1021/ci960151eopenalex-snapshot
Clustering of Large Databases of Compounds: Using the MDL “Keys” as Structural Descriptors
inferredcites →https://doi.org/10.1021/ci970282vopenalex-snapshot
OptiSim: An Extended Dissimilarity Selection Method for Finding Diverse Representative Subsets
inferredcites →https://doi.org/10.1021/ci990070topenalex-snapshot
An Alignment-Independent Versatile Structure Descriptor for QSAR and QSPR Based on the Distribution of Molecular Features
inferredcites →https://doi.org/10.1021/jm0003992openalex-snapshot
Detailed Analysis of Scoring Functions for Virtual Screening
inferredcites →https://doi.org/10.1021/jm001044lopenalex-snapshot
Protein-Based Virtual Screening of Chemical Databases. 1. Evaluation of Different Docking/Scoring Combinations
inferredcites →https://doi.org/10.1021/jm010934dopenalex-snapshot
Development of a Virtual Screening Method for Identification of “Frequent Hitters” in Compound Libraries
inferredcites →https://doi.org/10.1021/jm049798dopenalex-snapshot
Comparison of Automated Docking Programs as Virtual Screening Tools
inferredcites →https://doi.org/10.1021/jm050040jopenalex-snapshot
Parameter Estimation for Scoring Protein−Ligand Interactions Using Negative Training Data
inferredcites →https://doi.org/10.1021/jm0603365openalex-snapshot
Comparison of Shape-Matching and Docking as Virtual Screening Tools
inferredcites →https://doi.org/10.1021/jm0608356openalex-snapshot
Benchmarking Sets for Molecular Docking
inferredcites →https://doi.org/10.1021/jm061317yopenalex-snapshot
A High-Throughput Screen for Aggregation-Based Inhibition in a Large Compound Library
inferredcites →https://doi.org/10.1021/jm701301mopenalex-snapshot
Fluorescence Spectroscopic Profiling of Compound Libraries
inferredcites →https://doi.org/10.1021/jm9602928openalex-snapshot
The Properties of Known Drugs. 1. Molecular Frameworks
inferredcites →https://doi.org/10.1038/nbt1186openalex-snapshot
Statistical practice in high-throughput screening data analysis
inferredcites →https://doi.org/10.1038/nchembio718openalex-snapshot
High-throughput assays for promiscuous inhibitors
inferredcites →https://doi.org/10.1038/nrd1063openalex-snapshot
Designing screens: how to make your hits a hit
inferredcites →https://doi.org/10.1038/nrd941openalex-snapshot
Integration of virtual and high-throughput screening
inferredcites →https://doi.org/10.1039/b409865jopenalex-snapshot
Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures
inferredcites →https://doi.org/10.1073/pnas.0604348103openalex-snapshot
Quantitative high-throughput screening: A titration-based approach that efficiently identifies biological activities in large chemical libraries
inferredcites →https://doi.org/10.1073/pnas.0705683104openalex-snapshot
Processing and classification of chemical data inspired by insect olfaction
inferredcites →https://doi.org/10.1080/00031305.1982.10482771openalex-snapshot
Use of the Singular Value Decomposition in Regression Analysis
inferredcites →https://doi.org/10.1080/00401706.1969.10490666openalex-snapshot
Computer Aided Design of Experiments
inferredcites →https://doi.org/10.1080/00401706.1991.10484792openalex-snapshot
Spatial Data Analysis by Example
inferredcites →https://doi.org/10.1093/bioinformatics/btm404openalex-snapshot
Clustal W and Clustal X version 2.0
inferredcites →https://doi.org/10.1093/nar/22.22.4673openalex-snapshot
CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice
inferredcites →https://doi.org/10.1093/nar/gkm1000openalex-snapshot
Database resources of the National Center for Biotechnology Information
inferredcites →https://doi.org/10.1093/nar/gkm958openalex-snapshot
DrugBank: a knowledgebase for drugs, drug actions and drug targets
inferredcites →https://doi.org/10.1093/oso/9780198522546.001.0001openalex-snapshot
Optimum Experimental Designs
inferredcites →https://doi.org/10.1111/j.2517-6161.1995.tb02034.xopenalex-snapshot
Data-Driven Bandwidth Selection in Local Polynomial Fitting: Variable Bandwidth and Spatial Adaptation
inferredcites →https://doi.org/10.2174/1381612053507549openalex-snapshot
New Methodologies for Ligand-Based Virtual Screening
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Pretraining graph transformer for molecular representation with fusion of multimodal information
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Knowledge-enhanced Relation Graph and Task Sampling for few-shot molecular property prediction
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Virtual screening filters for the design of type II p38 MAP kinase inhibitors: A fragment based library generation approach
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Fragment oriented molecular shapes
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GenSMILES: An enhanced validity conscious representation for inverse design of molecules
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LLM-enhanced multi-level knowledge distillation for molecular property prediction
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Indefinite kernel ridge regression and its application on QSAR modelling
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Few-shot Molecular Property Prediction via Hierarchically Structured Learning on Relation Graphs
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A comprehensive review of chemoinformatics: From molecular representation to data-driven design
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HMRLBA: A hierarchical multi-scale representation learning model for predicting protein-ligand binding affinity
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Statistical and machine learning approaches to predicting protein–ligand interactions
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Graph neural network approaches for drug-target interactions
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Explainable machine learning models enhance prediction of PFAS bioactivity using quantitative molecular surface analysis-derived representation
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Benchmarking methods and data sets for ligand enrichment assessment in virtual screening
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Saagar–A New, Extensible Set of Molecular Substructures for QSAR/QSPR and Read-Across Predictions
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Graph Neural Networks in Modern AI-Aided Drug Discovery
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General-Purpose Models for the Chemical Sciences: LLMs and Beyond
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Using Machine Learning to Classify Bioactivity for 3486 Per- and Polyfluoroalkyl Substances (PFASs) from the OECD List
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Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs)
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LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening
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Comparing Fingerprints for Ligand-Based Virtual Screening: A Fast and Scalable Approach for Unbiased Evaluation
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Improved Scaffold Hopping in Ligand-Based Virtual Screening Using Neural Representation Learning
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Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump
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GHOST: Adjusting the Decision Threshold to Handle Imbalanced Data in Machine Learning
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Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening
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Differential Multimolecule Fingerprint for Similarity Search─Making Use of Active and Inactive Compound Sets in Virtual Screening
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MILCDock: Machine Learning Enhanced Consensus Docking for Virtual Screening in Drug Discovery
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Few-Shot Learning for Low-Data Drug Discovery
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DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features
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Multisource Attention-Mechanism-Based Encoder–Decoder Model for Predicting Drug–Drug Interaction Events
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BEAR: A Novel Virtual Screening Method Based on Large-Scale Bioactivity Data
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Predicting the Binding of Small Molecules to Proteins through Invariant Representation of the Molecular Structure
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The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks
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Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives
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pROC-Chemotype Plots Enhance the Interpretability of Benchmarking Results in Structure-Based Virtual Screening
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Effect of Binding Pose and Modeled Structures on SVMGen and GlideScore Enrichment of Chemical Libraries
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3D Spatial Learning for Adsorption Energy Prediction in Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-Based Network
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Functional Groups Are All You Need for Chemically Interpretable Molecular Property Prediction
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Protein–Ligand Scoring with Convolutional Neural Networks
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Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets
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The Last Mile Problem: A Critical Assessment of Physics-Based and AI Tools for Small Molecule Binding Prediction in Virtual Screening
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Measuring AI Progress in Drug Discovery: A Reproducible Leaderboard for the Tox21 Challenge
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In Silico Isomerization Produces Apt Negative Data for VHTS Validation
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Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction
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Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization
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xMaP—An Interpretable Alignment-Free Four-Dimensional Quantitative Structure–Activity Relationship Technique Based on Molecular Surface Properties and Conformer Ensembles
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Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis
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Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data
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Evaluation of Cross-Validation Strategies in Sequence-Based Binding Prediction Using Deep Learning
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In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening
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Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure
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Predicting Drug–Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation
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The Effect of Debiasing Protein–Ligand Binding Data on Generalization
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Connected Subgraph Fingerprints: Representing Molecules Using Exhaustive Subgraph Enumeration
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Graph Convolutional Neural Networks for Predicting Drug-Target Interactions
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Development of New Methods Needs Proper Evaluation—Benchmarking Sets for Machine Learning Experiments for Class A GPCRs
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Combining Docking Pose Rank and Structure with Deep Learning Improves Protein–Ligand Binding Mode Prediction over a Baseline Docking Approach
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Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular Docking Scoring
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LigMate: A Multifeature Integration Algorithm for Ligand-Similarity-Based Virtual Screening
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Discovery of Novel Inhibitors of a Critical Brain Enzyme Using a Homology Model and a Deep Convolutional Neural Network
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TocoDecoy: A New Approach to Design Unbiased Datasets for Training and Benchmarking Machine-Learning Scoring Functions
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Prediction of Small-Molecule Developability Using Large-Scale In Silico ADMET Models
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In Silico ADMET: From Current Practices to Novel Profilers
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Transfer Learning for Drug Discovery
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Predicting Drug–Target Interactions with Deep-Embedding Learning of Graphs and Sequences
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Utility of the Morgan Fingerprint in Structure-Based Virtual Ligand Screening
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Extracting Predictive Representations from Hundreds of Millions of Molecules
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Low Data Drug Discovery with One-Shot Learning
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Deep Scoring Neural Network Replacing the Scoring Function Components to Improve the Performance of Structure-Based Molecular Docking
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Similarity-Based Virtual Screen Using Enhanced Siamese Deep Learning Methods
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Latent Biases in Machine Learning Models for Predicting Binding Affinities Using Popular Data Sets
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BioAct-Het: A Heterogeneous Siamese Neural Network for Bioactivity Prediction Using Novel Bioactivity Representation
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Prediction of Compound Profiling Matrices Using Machine Learning
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How Many Fingers Does a Compound Have? Molecular Similarity beyond Chemical Space
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Large-Scale Learning of Structure−Activity Relationships Using a Linear Support Vector Machine and Problem-Specific Metrics
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Ligand-Based Virtual Screening Using Bayesian Networks
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One Concept, Three Implementations of 3D Pharmacophore-Based Virtual Screening: Distinct Coverage of Chemical Search Space
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New Fragment Weighting Scheme for the Bayesian Inference Network in Ligand-Based Virtual Screening
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Comprehensive Comparison of Ligand-Based Virtual Screening Tools Against the DUD Data set Reveals Limitations of Current 3D Methods
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Improving VEGFR-2 Docking-Based Screening by Pharmacophore Postfiltering and Similarity Search Postprocessing
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Pharmer: Efficient and Exact Pharmacophore Search
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DEKOIS: Demanding Evaluation Kits for Objective in Silico Screening — A Versatile Tool for Benchmarking Docking Programs and Scoring Functions
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Large-Scale Similarity Search Profiling of ChEMBL Compound Data Sets
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Comparative Analysis of Pharmacophore Screening Tools
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Recognizing Pitfalls in Virtual Screening: A Critical Review
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Similarity Coefficients for Binary Chemoinformatics Data: Overview and Extended Comparison Using Simulated and Real Data Sets
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Consensus Methods for Combining Multiple Clusterings of Chemical Structures
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Evaluation and Optimization of Virtual Screening Workflows with DEKOIS 2.0 – A Public Library of Challenging Docking Benchmark Sets
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Identification of New Fyn Kinase Inhibitors Using a FLAP-Based Approach
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Heterogeneous Classifier Fusion for Ligand-Based Virtual Screening: Or, How Decision Making by Committee Can Be a Good Thing
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An Unbiased Method To Build Benchmarking Sets for Ligand-Based Virtual Screening and its Application To GPCRs
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ColBioS-FlavRC: A Collection of Bioselective Flavonoids and Related Compounds Filtered from High-Throughput Screening Outcomes
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