Designing novel nicotinic agonists by searching a database of molecular shapes. - Robert P. Sheridan; R. Venkataraghavan (J. Comput. Aided Mol. Des., 1987)

Using CONCORD to construct a large database of three-dimensional coordinates from connection tables. - Andrew Rusinko III; Robert P. Sheridan; Ramaswamy Nilakantan; Kevin S. Haraki; Norman Bauman; R. Venkataraghavan (J. Chem. Inf. Comput. Sci., 1989)

3DSEARCH: a system for three-dimensional substructure searching. - Robert P. Sheridan; Ramaswamy Nilakantan; Andrew Rusinko III; Norman Bauman; Kevin S. Haraki; R. Venkataraghavan (J. Chem. Inf. Comput. Sci., 1989)

PATTY: A programmable atom type and language for automatic classification of atoms in molecular databases. - Bruce L. Bush; Robert P. Sheridan (J. Chem. Inf. Comput. Sci., 1993)

Flexibases: A way to enhance the use of molecular docking methods. - Simon K. Kearsley; Dennis J. Underwood; Robert P. Sheridan; Michael D. Miller (J. Comput. Aided Mol. Des., 1994)

FLOG: A system to select 'quasi-flexible' ligands complementary to a receptor of known three-dimensional structure. - Michael D. Miller; Simon K. Kearsley; Dennis J. Underwood; Robert P. Sheridan (J. Comput. Aided Mol. Des., 1994)

Extending the trend vector: The trend matrix and sample-based partial least squares. - Robert P. Sheridan; Robert B. Nachbar; Bruce L. Bush (J. Comput. Aided Mol. Des., 1994)

Using a Genetic Algorithm To Suggest Combinatorial Libraries. - Robert P. Sheridan; Simon K. Kearsley (J. Chem. Inf. Comput. Sci., 1995)

Chemical Similarity Using Physiochemical Property Descriptors. - Simon K. Kearsley; Susan Sallamack; Eugene M. Fluder; Joseph D. Andose; Ralph T. Mosley; Robert P. Sheridan (J. Chem. Inf. Comput. Sci., 1996)

Chemical Similarity Using Geometric Atom Pair Descriptors. - Robert P. Sheridan; Michael D. Miller; Dennis J. Underwood; Simon K. Kearsley (J. Chem. Inf. Comput. Sci., 1996)

A Method for Visualizing Recurrent Topological Substructures in Sets of Active Molecules. - Robert P. Sheridan; Michael D. Miller (J. Chem. Inf. Comput. Sci., 1998)

The Centroid Approximation for Mixtures: Calculating Similarity and Deriving Structure-Activity Relationships. - Robert P. Sheridan (J. Chem. Inf. Comput. Sci., 2000)

Protocols for Bridging the Peptide to Nonpeptide Gap in Topological Similarity Searches. - Robert P. Sheridan; Suresh B. Singh; Eugene M. Fluder; Simon K. Kearsley (J. Chem. Inf. Comput. Sci., 2001)

The Most Common Chemical Replacements in Drug-Like Compounds. - Robert P. Sheridan (J. Chem. Inf. Comput. Sci., 2002)

Finding Multiactivity Substructures by Mining Databases of Drug-Like Compounds. - Robert P. Sheridan (J. Chem. Inf. Comput. Sci., 2003)

Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. - Vladimir Svetnik; Andy Liaw; Christopher Tong; J. Christopher Culberson; Robert P. Sheridan; Bradley P. Feuston (J. Chem. Inf. Comput. Sci., 2003)

Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR. - Robert P. Sheridan; Bradley P. Feuston; Vladimir N. Maiorov; Simon K. Kearsley (J. Chem. Inf. Model., 2004)

Calculating Similarities between Biological Activities in the MDL Drug Data Report Database. - Robert P. Sheridan; Joseph Shpungin (J. Chem. Inf. Model., 2004)

Enhanced Virtual Screening by Combined Use of Two Docking Methods: Getting the Most on a Limited Budget. - Vladimir N. Maiorov; Robert P. Sheridan (J. Chem. Inf. Model., 2005)

Reagent Selector: Using Synthon Analysis to Visualize Reagent Properties and Assist in Combinatorial Library Design. - Ralph T. Mosley; J. Christopher Culberson; Bryan Kraker; Bradley P. Feuston; Robert P. Sheridan; John F. Conway; Joseph K. Forbes; Subhas J. Chakravorty; Simon K. Kearsley (J. Chem. Inf. Model., 2005)

Boosting: An Ensemble Learning Tool for Compound Classification and QSAR Modeling. - Vladimir Svetnik; Ting Wang; Christopher Tong; Andy Liaw; Robert P. Sheridan; Qinghua Song (J. Chem. Inf. Model., 2005)

Molecular Transformations as a Way of Finding and Exploiting Consistent Local QSAR. - Robert P. Sheridan; Peter Hunt; J. Christopher Culberson (J. Chem. Inf. Model., 2006)

Comparison of Topological, Shape, and Docking Methods in Virtual Screening. - Georgia B. McGaughey; Robert P. Sheridan; Christopher I. Bayly; J. Christopher Culberson; Constantine Kreatsoulas; Stacey Lindsley; Vladimir N. Maiorov; Jean-François Truchon; Wendy D. Cornell (J. Chem. Inf. Model., 2007)

Computational Analysis of Mouse piRNA Sequence and Biogenesis. - Doron Betel; Robert P. Sheridan; Debora S. Marks; Chris Sander (PLoS Comput. Biol., 2007)

Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results. - Robert P. Sheridan; Georgia B. McGaughey; Wendy D. Cornell (J. Comput. Aided Mol. Des., 2008)

Alternative Global Goodness Metrics and Sensitivity Analysis: Heuristics to Check the Robustness of Conclusions from Studies Comparing Virtual Screening Methods. - Robert P. Sheridan (J. Chem. Inf. Model., 2008)

QSAR Models for Predicting the Similarity in Binding Profiles for Pairs of Protein Kinases and the Variation of Models between Experimental Data Sets. - Robert P. Sheridan; Kiyean Nam; Vladimir N. Maiorov; Daniel R. McMasters; Wendy D. Cornell (J. Chem. Inf. Model., 2009)

Generating hypotheses about molecular structure-activity relationships (SARs) by solving an optimization problem. - Junshui Ma; Christopher Tong; Andy Liaw; Robert P. Sheridan; John Szumiloski; Vladimir Svetnik (Stat. Anal. Data Min., 2009)

Drug-like Density: A Method of Quantifying the "Bindability" of a Protein Target Based on a Very Large Set of Pockets and Drug-like Ligands from the Protein Data Bank. - Robert P. Sheridan; Vladimir N. Maiorov; M. Katharine Holloway; Wendy D. Cornell; Ying-Duo Gao (J. Chem. Inf. Model., 2010)

3D Protein Structure Predicted from Sequence - Debora S. Marks; Lucy J. Colwell; Robert P. Sheridan; Thomas A. Hopf; Andrea Pagnani; Riccardo Zecchina; Chris Sander (CoRR, 2011)

Comparison of Random Forest and Pipeline Pilot Naïve Bayes in Prospective QSAR Predictions. - Bin Chen; Robert P. Sheridan; Viktor Hornak; Johannes H. Voigt (J. Chem. Inf. Model., 2012)

Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest. - Robert P. Sheridan (J. Chem. Inf. Model., 2012)

Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction. - Robert P. Sheridan (J. Chem. Inf. Model., 2013)

Using Random Forest To Model the Domain Applicability of Another Random Forest Model. - Robert P. Sheridan (J. Chem. Inf. Model., 2013)

Global Quantitative Structure-Activity Relationship Models vs Selected Local Models as Predictors of Off-Target Activities for Project Compounds. - Robert P. Sheridan (J. Chem. Inf. Model., 2014)

Modeling a Crowdsourced Definition of Molecular Complexity. - Robert P. Sheridan; Nicolas Zorn; Edward C. Sherer; Louis-Charles Campeau; Charlie (Zhenyu) Chang; Jared Cumming; Matthew L. Maddess; Philippe G. Nantermet; Christopher J. Sinz; Paul D. O'Shea (J. Chem. Inf. Model., 2014)

Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships. - Junshui Ma; Robert P. Sheridan; Andy Liaw; George E. Dahl; Vladimir Svetnik (J. Chem. Inf. Model., 2015)

The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity. - Robert P. Sheridan (J. Chem. Inf. Model., 2015)

eCounterscreening: Using QSAR Predictions to Prioritize Testing for Off-Target Activities and Setting the Balance between Benefit and Risk. - Robert P. Sheridan; Daniel R. McMasters; Johannes H. Voigt; Mary Jo Wildey (J. Chem. Inf. Model., 2015)

Debunking the Idea that Ligand Efficiency Indices Are Superior to pIC50 as QSAR Activities. - Robert P. Sheridan (J. Chem. Inf. Model., 2016)

Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships. - Robert P. Sheridan; Wei Min Wang; Andy Liaw; Junshui Ma; Eric M. Gifford (J. Chem. Inf. Model., 2016)

Is Multitask Deep Learning Practical for Pharma? - Bharath Ramsundar; Bowen Liu 014; Zhenqin Wu; Andreas Verras; Matthew Tudor; Robert P. Sheridan; Vijay S. Pande (J. Chem. Inf. Model., 2017)

Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships. - Yuting Xu; Junshui Ma; Andy Liaw; Robert P. Sheridan; Vladimir Svetnik (J. Chem. Inf. Model., 2017)

The EVcouplings Python framework for coevolutionary sequence analysis. - Thomas A. Hopf; Anna G. Green; Benjamin Schubert; Sophia Mersmann; Charlotta Schärfe; John Ingraham; Ágnes Tóth-Petróczy; Kelly Brock; Adam J. Riesselman; Perry Palmedo; Chan Kang; Robert P. Sheridan; et al. (Bioinform., 2019)

Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurization. - Evan N. Feinberg; Robert P. Sheridan; Elizabeth Joshi; Vijay S. Pande; Alan C. Cheng (CoRR, 2019)

Building Quantitative Structure-Activity Relationship Models Using Bayesian Additive Regression Trees. - Dai Feng; Vladimir Svetnik; Andy Liaw; Matthew T. Pratola; Robert P. Sheridan (J. Chem. Inf. Model., 2019)

Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It? - Robert P. Sheridan (J. Chem. Inf. Model., 2019)

AlignmentViewer: Sequence Analysis of Large Protein Families. - Roc Reguant; Yevgeniy Antipin; Robert P. Sheridan; Christian Dallago; Drew Diamantoukos; Augustin Luna; Chris Sander; Nicholas Paul Gauthier (F1000Research, 2020)

Nearest Neighbor Gaussian Process for Quantitative Structure-Activity Relationships. - Anthony Difranzo; Robert P. Sheridan; Andy Liaw; Matthew Tudor (J. Chem. Inf. Model., 2020)

Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure-Activity Relationship Models? - Robert P. Sheridan; Prabha Karnachi; Matthew Tudor; Yuting Xu; Andy Liaw; Falgun Shah; Alan C. Cheng; Elizabeth Joshi; Meir Glick; Juan Alvarez (J. Chem. Inf. Model., 2020)

Correction to Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships. - Robert P. Sheridan; Min Wang; Andy Liaw; Junshi Ma; Eric M. Gifford (J. Chem. Inf. Model., 2020)

Deep Dive into Machine Learning Models for Protein Engineering. - Yuting Xu; Deeptak Verma; Robert P. Sheridan; Andy Liaw; Junshui Ma; Nicholas M. Marshall; John McIntosh; Edward C. Sherer; Vladimir Svetnik; Jennifer M. Johnston (J. Chem. Inf. Model., 2020)

Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships. - Robert P. Sheridan; Andy Liaw; Matthew Tudor (CoRR, 2021)

Stability of Prediction in Production ADMET Models as a Function of Version: Why and When Predictions Change. - Robert P. Sheridan (J. Chem. Inf. Model., 2022)

Prediction Accuracy of Production ADMET Models as a Function of Version: Activity Cliffs Rule. - Robert P. Sheridan; J. Christopher Culberson; Elizabeth Joshi; Matthew Tudor; Prabha Karnachi (J. Chem. Inf. Model., 2022)

Development and Evaluation of Conformal Prediction Methods for QSAR. - Yuting Xu; Andy Liaw; Robert P. Sheridan; Vladimir Svetnik (CoRR, 2023)