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Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Predictivity of Simulated ADME AutoQSAR Models over Time
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A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression Predictions
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Target‐Driven Subspace Mapping Methods and Their Applicability Domain Estimation
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Automated QSAR with a Hierarchy of Global and Local Models
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On Selection of Training and Test Sets for the Development of Predictive QSAR models
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Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules
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QSAR with experimental and predictive distributions: an information theoretic approach for assessing model quality
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Can we estimate the accuracy of ADME–Tox predictions?
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Assessing the reliability of a QSAR model's predictions
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The importance of the domain of applicability in QSAR modeling
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Fingerprint-based clustering applied to define a QSAR model use radius
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Atom pairs as molecular features in structure-activity studies: definition and applications
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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
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Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR
inferredcites →https://doi.org/10.1021/ci0500381openalex-snapshot
A Stepwise Approach for Defining the Applicability Domain of SAR and QSAR Models
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Assessment of Methods To Define the Applicability Domain of Structural Alert Models
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Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set
inferredcites →https://doi.org/10.1021/ci200615hopenalex-snapshot
Comparison of Random Forest and Pipeline Pilot Naïve Bayes in Prospective QSAR Predictions
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Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest
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Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?
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Structure−Activity Landscape Index: Identifying and Quantifying Activity Cliffs
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Predicting the Predictability: A Unified Approach to the Applicability Domain Problem of QSAR Models
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Chemical Domain of QSAR Models from Atom-Centered Fragments
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Chemical Similarity Using Physiochemical Property Descriptors
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Prediction of Human Volume of Distribution Values for Neutral and Basic Drugs. 2. Extended Data Set and Leave-Class-Out Statistics
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Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection
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QComp: A QSAR-Based Imputation Framework for Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00538openalex-snapshot
Data Scaling and Generalization Insights for Medicinal Chemistry Deep Learning Models
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01609openalex-snapshot
Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c02106openalex-snapshot
A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c02381openalex-snapshot
Uncertainty Quantification in Molecular Machine Learning for Property Predictions under Data Shifts
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c02465openalex-snapshot
Toward More Trustworthy QSAR: A Systematic Discussion on Data Set Partitioning
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c02953openalex-snapshot
Machine Learning-Based Soft Voting Ensemble Model for the Prediction of Oral Drug-Likeness of Chemical Structures
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00044openalex-snapshot
In Silico Prediction of Volume of Distribution in Humans. Extensive Data Set and the Exploration of Linear and Nonlinear Methods Coupled with Molecular Interaction Fields Descriptors
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00431openalex-snapshot
Debunking the Idea that Ligand Efficiency Indices Are Superior to pIC50 as QSAR Activities
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00591openalex-snapshot
Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00601openalex-snapshot
Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c00514openalex-snapshot
Defining the Data set Defines the QSAR Claim
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01018openalex-snapshot
Balancing Data Quantity and Quality: Evaluating Curation Strategies for Bioactivity Prediction in Lead Optimization
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01094openalex-snapshot
Machine learning for RNA-targeting drug design
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01237openalex-snapshot
Systematic Multi-Target QSAR Benchmarking: Machine Learning Algorithms, Molecular Descriptors, and Validation
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c02160openalex-snapshot
Systematic Evaluation of Graph Neural Networks for Ligand-Based Virtual Screening on ChEMBL Datasets
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c02407openalex-snapshot
Artificial Intelligence in the Discovery and Design of Ionic Liquids for Thermal Energy Applications
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c02796openalex-snapshot
Validation Strategy Dominates Model Choice in Chronological QSAR Evaluation
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00087openalex-snapshot
Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00146openalex-snapshot
Is Multitask Deep Learning Practical for Pharma?
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00166openalex-snapshot
Profile-QSAR 2.0: Kinase Virtual Screening Accuracy Comparable to Four-Concentration IC50s for Realistically Novel Compounds
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00348openalex-snapshot
Dissecting Machine-Learning Prediction of Molecular Activity: Is an Applicability Domain Needed for Quantitative Structure–Activity Relationship Models Based on Deep Neural Networks?
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00553openalex-snapshot
Stacked Generalization with Applicability Domain Outperforms Simple QSAR on in Vitro Toxicological Data
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00663openalex-snapshot
Evaluation of Cross-Validation Strategies in Sequence-Based Binding Prediction Using Deep Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00711openalex-snapshot
PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00785openalex-snapshot
Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00825openalex-snapshot
Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00832openalex-snapshot
Advances and Challenges in Computational Target Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00237openalex-snapshot
Analyzing Learned Molecular Representations for Property Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00415openalex-snapshot
The Effect of Debiasing Protein–Ligand Binding Data on Generalization
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00498openalex-snapshot
Prediction of pKa Using Machine Learning Methods with Rooted Topological Torsion Fingerprints: Application to Aliphatic Amines
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00689openalex-snapshot
Development of New Methods Needs Proper Evaluation—Benchmarking Sets for Machine Learning Experiments for Class A GPCRs
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b01067openalex-snapshot
Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure–Activity Relationship Models?
inferred← citeshttps://doi.org/10.1021/acs.jctc.5c02081openalex-snapshot
A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.1c01789openalex-snapshot
Explainable Machine Learning for Property Predictions in Compound Optimization
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.7b00487openalex-snapshot
In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.9b02187openalex-snapshot
Improvement in ADMET Prediction with Multitask Deep Featurization
inferred← citeshttps://doi.org/10.1021/acs.jpca.5c03510openalex-snapshot
Analysis of Asymmetric Reduction of Ketones Using Three-Dimensional Electronic States
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.0c00637openalex-snapshot
Structure–Property Relationships and Machine Learning Models for Addressing CYP3A4-Mediated Victim Drug–Drug Interaction Risk in Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.0c01009openalex-snapshot
Prediction of Oral Pharmacokinetics Using a Combination of In Silico Descriptors and In Vitro ADME Properties
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.2c00962openalex-snapshot
Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.3c01124openalex-snapshot
Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.4c00562openalex-snapshot
DeepCt: Predicting Pharmacokinetic Concentration–Time Curves and Compartmental Models from Chemical Structure Using Deep Learning
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.4c01086openalex-snapshot
Multitask Deep Learning Models of Combined Industrial Absorption, Distribution, Metabolism, and Excretion Datasets to Improve Generalization
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.5b00762openalex-snapshot
QSAR Modeling and Prediction of Drug–Drug Interactions
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.8b00083openalex-snapshot
Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.8b00816openalex-snapshot
An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.9b01294openalex-snapshot
Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay
inferred← citeshttps://doi.org/10.1021/acs.oprd.4c00384openalex-snapshot
Enabling Data-Driven Solubility Modeling at GSK: Enhancing Purge Predictions for Mutagenic Impurities
inferred← citeshttps://doi.org/10.1021/acscentsci.2c01177openalex-snapshot
An Ecosystem for Digital Reticular Chemistry
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Challenging Reaction Prediction Models to Generalize to Novel Chemistry
inferred← citeshttps://doi.org/10.1021/acsmedchemlett.4c00290openalex-snapshot
Machine Learning ADME Models in Practice: Four Guidelines from a Successful Lead Optimization Case Study
inferred← citeshttps://doi.org/10.1021/acsmedchemlett.5c00724openalex-snapshot
Benchmarking Classical and Quantum Machine Learning for Potency Prediction across Ten Therapeutically Relevant Targets
inferred← citeshttps://doi.org/10.1021/acsomega.1c04996openalex-snapshot
Prediction of Binding Free Energy of Protein–Ligand Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface Area and Machine Learning Method
inferred← citeshttps://doi.org/10.1021/acsomega.2c01723openalex-snapshot
XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein–Ligand Scoring and Ranking
inferred← citeshttps://doi.org/10.1021/acsomega.2c05607openalex-snapshot
Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity
inferred← citeshttps://doi.org/10.1021/acsomega.3c01218openalex-snapshot
Molecular Representations in Machine-Learning-Based Prediction of PK Parameters for Insulin Analogs
inferred← citeshttps://doi.org/10.1021/acsomega.4c05433openalex-snapshot
Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint
inferred← citeshttps://doi.org/10.1021/acsomega.6b00247openalex-snapshot
Using the Variable-Nearest Neighbor Method To Identify P-Glycoprotein Substrates and Inhibitors
inferred← citeshttps://doi.org/10.1021/acsomega.6c02607openalex-snapshot
Trust Beyond Accuracy: Conformal Uncertainty Quantification Reveals the Generalization Gap in Polymer Glass Transition Temperature Prediction
inferred← citeshttps://doi.org/10.1021/acsomega.6c07482openalex-snapshot
Objective-Dependent Active Learning and Calibrated Uncertainty for Sample-Efficient Discovery of Rare-Earth Extractants: A Benchmark on 1,202 Experimental Distribution Coefficients with Prospective Validation
inferred← citeshttps://doi.org/10.1021/bk-2021-1397.ch008openalex-snapshot
Impacting Drug Discovery Projects with Large-Scale Enumerations, Machine Learning Strategies, and Free-Energy Predictions
inferred← citeshttps://doi.org/10.1021/ci4003766openalex-snapshot
Criterion for Evaluating the Predictive Ability of Nonlinear Regression Models without Cross-Validation
inferred← citeshttps://doi.org/10.1021/ci400482eopenalex-snapshot
Using Random Forest To Model the Domain Applicability of Another Random Forest Model
inferred← citeshttps://doi.org/10.1021/ci5001778openalex-snapshot
Modeling a Crowdsourced Definition of Molecular Complexity
inferred← citeshttps://doi.org/10.1021/ci500747nopenalex-snapshot
Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1021/jm4004285openalex-snapshot
QSAR Modeling: Where Have You Been? Where Are You Going To?
inferred← citeshttps://doi.org/10.1038/d41586-019-02307-yopenalex-snapshot
Three pitfalls to avoid in machine learning
inferred← citeshttps://doi.org/10.1038/nature25978openalex-snapshot
Planning chemical syntheses with deep neural networks and symbolic AI
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Application of machine learning models for property prediction to targeted protein degraders
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