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Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction

Journal of chemical information and modeling, 2013-04, Vol.53 (4), p.783-790

Sheridan, Robert P

2013

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Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

Journal of chemical information and modeling, 2015-02, Vol.55 (2), p.263-274

2015

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Using Random Forest To Model the Domain Applicability of Another Random Forest Model

Journal of chemical information and modeling, 2013-11, Vol.53 (11), p.2837-2850

Sheridan, Robert P

2013

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Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships

Journal of chemical information and modeling, 2016-12, Vol.56 (12), p.2353-2360

2016

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QSAR without borders

Chemical Society reviews, 2020-06, Vol.49 (11), p.3525-3564

2020

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Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest

Journal of chemical information and modeling, 2012-03, Vol.52 (3), p.814-823

Sheridan, Robert P

2012

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Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning"

Science (American Association for the Advancement of Science), 2018-11, Vol.362 (6416)

2018

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Global Quantitative Structure–Activity Relationship Models vs Selected Local Models as Predictors of Off-Target Activities for Project Compounds

Journal of chemical information and modeling, 2014-04, Vol.54 (4), p.1083-1092

Sheridan, Robert P

2014

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Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure–Activity Relationship Models?

Journal of chemical information and modeling, 2020-04, Vol.60 (4), p.1969-1982

2020

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Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?

Journal of chemical information and modeling, 2019-04, Vol.59 (4), p.1324-1337

Sheridan, Robert P

2019

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Application of Machine Learning and Reaction Optimization for the Iterative Improvement of Enantioselectivity of Cinchona-Derived Phase Transfer Catalysts

Organic process research & development, 2022-03, Vol.26 (3), p.670-682

2022

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Role of simple descriptors and applicability domain in predicting change in protein thermostability

PloS one, 2018-09, Vol.13 (9), p.e0203819-e0203819

2018

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Comparison of Topological, Shape, and Docking Methods in Virtual Screening

Journal of chemical information and modeling, 2007-07, Vol.47 (4), p.1504-1519

2007

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Stability of Prediction in Production ADMET Models as a Function of Version: Why and When Predictions Change

Journal of chemical information and modeling, 2022-08, Vol.62 (15), p.3477-3485

Sheridan, Robert P.

2022

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Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships

Journal of chemical information and modeling, 2017-10, Vol.57 (10), p.2490-2504

2017

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Development and Evaluation of Conformal Prediction Methods for Quantitative Structure–Activity Relationship

ACS omega, 2024-07, Vol.9 (27), p.29478-29490

2024

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Is Multitask Deep Learning Practical for Pharma?

Journal of chemical information and modeling, 2017-08, Vol.57 (8), p.2068-2076

2017

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The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity

Journal of chemical information and modeling, 2015-06, Vol.55 (6), p.1098-1107

Sheridan, Robert P

2015

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Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR

Journal of Chemical Information and Computer Sciences, 2004-11, Vol.44 (6), p.1912-1928

2004

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Deep Dive into Machine Learning Models for Protein Engineering

Journal of chemical information and modeling, 2020-06, Vol.60 (6), p.2773-2790

2020

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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

Journal of Chemical Information and Computer Sciences, 2003-11, Vol.43 (6), p.1947-1958

2003

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eCounterscreening: Using QSAR Predictions to Prioritize Testing for Off-Target Activities and Setting the Balance between Benefit and Risk

Journal of chemical information and modeling, 2015-02, Vol.55 (2), p.231-238

2015

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Debunking the Idea that Ligand Efficiency Indices Are Superior to pIC50 as QSAR Activities

Journal of chemical information and modeling, 2016-11, Vol.56 (11), p.2253-2262

Sheridan, Robert P

2016

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Mining Chromatographic Enantioseparation Data Using Matched Molecular Pair Analysis

Molecules (Basel, Switzerland), 2016-10, Vol.21 (10), p.1297-1297

2016

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Prediction Accuracy of Production ADMET Models as a Function of Version: Activity Cliffs Rule

Journal of chemical information and modeling, 2022-07, Vol.62 (14), p.3275-3280

2022

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Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction

Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

Using Random Forest To Model the Domain Applicability of Another Random Forest Model

Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships

QSAR without borders

Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest

Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning"

Global Quantitative Structure–Activity Relationship Models vs Selected Local Models as Predictors of Off-Target Activities for Project Compounds

Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure–Activity Relationship Models?

Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?

Application of Machine Learning and Reaction Optimization for the Iterative Improvement of Enantioselectivity of Cinchona-Derived Phase Transfer Catalysts

Role of simple descriptors and applicability domain in predicting change in protein thermostability

Comparison of Topological, Shape, and Docking Methods in Virtual Screening

Stability of Prediction in Production ADMET Models as a Function of Version: Why and When Predictions Change

Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships

Development and Evaluation of Conformal Prediction Methods for Quantitative Structure–Activity Relationship

Is Multitask Deep Learning Practical for Pharma?

The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity

Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR

Deep Dive into Machine Learning Models for Protein Engineering

Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

eCounterscreening: Using QSAR Predictions to Prioritize Testing for Off-Target Activities and Setting the Balance between Benefit and Risk

Debunking the Idea that Ligand Efficiency Indices Are Superior to pIC50 as QSAR Activities

Mining Chromatographic Enantioseparation Data Using Matched Molecular Pair Analysis

Prediction Accuracy of Production ADMET Models as a Function of Version: Activity Cliffs Rule

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