Robert P. Sheridan

57 papers Journal 57

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Enhancing Permeability Prediction of Heterobifunctional Degraders Using Machine Learning and Metadynamics-Informed 3D Molecular Descriptors. J. Chem. Inf. Model. Bryant Kim, Robert P. Sheridan, Ruibo Zhang, Emília P. Barros, Jennifer M. Johnston, Li Xiao
2023	J	`jnl`	Development and Evaluation of Conformal Prediction Methods for QSAR. CoRR Yuting Xu, Andy Liaw, Robert P. Sheridan, Vladimir Svetnik
2022	J	`jnl`	Prediction Accuracy of Production ADMET Models as a Function of Version: Activity Cliffs Rule. J. Chem. Inf. Model. Robert P. Sheridan, J. Christopher Culberson, Elizabeth Joshi, Matthew Tudor, Prabha Karnachi
2022	J	`jnl`	Stability of Prediction in Production ADMET Models as a Function of Version: Why and When Predictions Change. J. Chem. Inf. Model. Robert P. Sheridan
2021	J	`jnl`	Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships. CoRR Robert P. Sheridan, Andy Liaw, Matthew Tudor
2020	J	`jnl`	AlignmentViewer: Sequence Analysis of Large Protein Families. F1000Research Roc Reguant, Yevgeniy Antipin, Robert P. Sheridan, Christian Dallago, Drew Diamantoukos, Augustin Luna, Chris Sander, Nicholas Paul Gauthier
2020	J	`jnl`	Correction to Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. Robert P. Sheridan, Min Wang, Andy Liaw, Junshi Ma, Eric M. Gifford
2020	J	`jnl`	Deep Dive into Machine Learning Models for Protein Engineering. J. Chem. Inf. Model. Yuting Xu, Deeptak Verma, Robert P. Sheridan, Andy Liaw, Junshui Ma, Nicholas M. Marshall, John McIntosh, Edward C. Sherer, Vladimir Svetnik, Jennifer M. Johnston
2020	J	`jnl`	Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure-Activity Relationship Models? J. Chem. Inf. Model. Robert P. Sheridan, Prabha Karnachi, Matthew Tudor, Yuting Xu, Andy Liaw, Falgun Shah, Alan C. Cheng, Elizabeth Joshi, Meir Glick, Juan Alvarez
2020	J	`jnl`	Nearest Neighbor Gaussian Process for Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. Anthony Difranzo, Robert P. Sheridan, Andy Liaw, Matthew Tudor
2019	J	`jnl`	Building Quantitative Structure-Activity Relationship Models Using Bayesian Additive Regression Trees. J. Chem. Inf. Model. Dai Feng, Vladimir Svetnik, Andy Liaw, Matthew T. Pratola, Robert P. Sheridan
2019	J	`jnl`	Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It? J. Chem. Inf. Model. Robert P. Sheridan
2019	J	`jnl`	Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurization. CoRR Evan N. Feinberg, Robert P. Sheridan, Elizabeth Joshi, Vijay S. Pande, Alan C. Cheng
2019	J	`jnl`	The EVcouplings Python framework for coevolutionary sequence analysis. Bioinform. 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, Eli J. Draizen, Christian Dallago, Chris Sander, Debora S. Marks
2017	J	`jnl`	Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. Yuting Xu, Junshui Ma, Andy Liaw, Robert P. Sheridan, Vladimir Svetnik
2017	J	`jnl`	Is Multitask Deep Learning Practical for Pharma? J. Chem. Inf. Model. Bharath Ramsundar, Bowen Liu, Zhenqin Wu, Andreas Verras, Matthew Tudor, Robert P. Sheridan, Vijay S. Pande
2016	J	`jnl`	Debunking the Idea that Ligand Efficiency Indices Are Superior to pIC50 as QSAR Activities. J. Chem. Inf. Model. Robert P. Sheridan
2016	J	`jnl`	Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. Robert P. Sheridan, Wei Min Wang, Andy Liaw, Junshui Ma, Eric M. Gifford
2015	J	`jnl`	Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. Junshui Ma, Robert P. Sheridan, Andy Liaw, George E. Dahl, Vladimir Svetnik
2015	J	`jnl`	The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity. J. Chem. Inf. Model. Robert P. Sheridan
2015	J	`jnl`	eCounterscreening: Using QSAR Predictions to Prioritize Testing for Off-Target Activities and Setting the Balance between Benefit and Risk. J. Chem. Inf. Model. Robert P. Sheridan, Daniel R. McMasters, Johannes H. Voigt, Mary Jo Wildey
2014	J	`jnl`	Global Quantitative Structure-Activity Relationship Models vs Selected Local Models as Predictors of Off-Target Activities for Project Compounds. J. Chem. Inf. Model. Robert P. Sheridan
2014	J	`jnl`	Modeling a Crowdsourced Definition of Molecular Complexity. J. Chem. Inf. Model. 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
2013	J	`jnl`	Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction. J. Chem. Inf. Model. Robert P. Sheridan
2013	J	`jnl`	Using Random Forest To Model the Domain Applicability of Another Random Forest Model. J. Chem. Inf. Model. Robert P. Sheridan
2012	J	`jnl`	Comparison of Random Forest and Pipeline Pilot Naïve Bayes in Prospective QSAR Predictions. J. Chem. Inf. Model. Bin Chen, Robert P. Sheridan, Viktor Hornak, Johannes H. Voigt
2012	J	`jnl`	Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest. J. Chem. Inf. Model. Robert P. Sheridan
2011	J	`jnl`	3D Protein Structure Predicted from Sequence CoRR Debora S. Marks, Lucy J. Colwell, Robert P. Sheridan, Thomas A. Hopf, Andrea Pagnani, Riccardo Zecchina, Chris Sander
2010	J	`jnl`	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. J. Chem. Inf. Model. Robert P. Sheridan, Vladimir N. Maiorov, M. Katharine Holloway, Wendy D. Cornell, Ying-Duo Gao
2009	J	`jnl`	Generating hypotheses about molecular structure-activity relationships (SARs) by solving an optimization problem. Stat. Anal. Data Min. Junshui Ma, Christopher Tong, Andy Liaw, Robert P. Sheridan, John Szumiloski, Vladimir Svetnik
2009	J	`jnl`	QSAR Models for Predicting the Similarity in Binding Profiles for Pairs of Protein Kinases and the Variation of Models between Experimental Data Sets. J. Chem. Inf. Model. Robert P. Sheridan, Kiyean Nam, Vladimir N. Maiorov, Daniel R. McMasters, Wendy D. Cornell
2008	J	`jnl`	Alternative Global Goodness Metrics and Sensitivity Analysis: Heuristics to Check the Robustness of Conclusions from Studies Comparing Virtual Screening Methods. J. Chem. Inf. Model. Robert P. Sheridan
2008	J	`jnl`	Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results. J. Comput. Aided Mol. Des. Robert P. Sheridan, Georgia B. McGaughey, Wendy D. Cornell
2007	J	`jnl`	Comparison of Topological, Shape, and Docking Methods in Virtual Screening. J. Chem. Inf. Model. 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
2007	J	`jnl`	Computational Analysis of Mouse piRNA Sequence and Biogenesis. PLoS Comput. Biol. Doron Betel, Robert P. Sheridan, Debora S. Marks, Chris Sander
2006	J	`jnl`	Molecular Transformations as a Way of Finding and Exploiting Consistent Local QSAR. J. Chem. Inf. Model. Robert P. Sheridan, Peter Hunt, J. Christopher Culberson
2005	J	`jnl`	Boosting: An Ensemble Learning Tool for Compound Classification and QSAR Modeling. J. Chem. Inf. Model. Vladimir Svetnik, Ting Wang, Christopher Tong, Andy Liaw, Robert P. Sheridan, Qinghua Song
2005	J	`jnl`	Enhanced Virtual Screening by Combined Use of Two Docking Methods: Getting the Most on a Limited Budget. J. Chem. Inf. Model. Vladimir N. Maiorov, Robert P. Sheridan
2005	J	`jnl`	Reagent Selector: Using Synthon Analysis to Visualize Reagent Properties and Assist in Combinatorial Library Design. J. Chem. Inf. Model. 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
2004	J	`jnl`	Calculating Similarities between Biological Activities in the MDL Drug Data Report Database. J. Chem. Inf. Model. Robert P. Sheridan, Joseph Shpungin
2004	J	`jnl`	Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR. J. Chem. Inf. Model. Robert P. Sheridan, Bradley P. Feuston, Vladimir N. Maiorov, Simon K. Kearsley
2003	J	`jnl`	Finding Multiactivity Substructures by Mining Databases of Drug-Like Compounds. J. Chem. Inf. Comput. Sci. Robert P. Sheridan
2003	J	`jnl`	Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. J. Chem. Inf. Comput. Sci. Vladimir Svetnik, Andy Liaw, Christopher Tong, J. Christopher Culberson, Robert P. Sheridan, Bradley P. Feuston
2002	J	`jnl`	The Most Common Chemical Replacements in Drug-Like Compounds. J. Chem. Inf. Comput. Sci. Robert P. Sheridan
2001	J	`jnl`	Protocols for Bridging the Peptide to Nonpeptide Gap in Topological Similarity Searches. J. Chem. Inf. Comput. Sci. Robert P. Sheridan, Suresh B. Singh, Eugene M. Fluder, Simon K. Kearsley
2000	J	`jnl`	The Centroid Approximation for Mixtures: Calculating Similarity and Deriving Structure-Activity Relationships. J. Chem. Inf. Comput. Sci. Robert P. Sheridan
1998	J	`jnl`	A Method for Visualizing Recurrent Topological Substructures in Sets of Active Molecules. J. Chem. Inf. Comput. Sci. Robert P. Sheridan, Michael D. Miller
1996	J	`jnl`	Chemical Similarity Using Geometric Atom Pair Descriptors. J. Chem. Inf. Comput. Sci. Robert P. Sheridan, Michael D. Miller, Dennis J. Underwood, Simon K. Kearsley
1996	J	`jnl`	Chemical Similarity Using Physiochemical Property Descriptors. J. Chem. Inf. Comput. Sci. Simon K. Kearsley, Susan Sallamack, Eugene M. Fluder, Joseph D. Andose, Ralph T. Mosley, Robert P. Sheridan
1995	J	`jnl`	Using a Genetic Algorithm To Suggest Combinatorial Libraries. J. Chem. Inf. Comput. Sci. Robert P. Sheridan, Simon K. Kearsley
1994	J	`jnl`	Extending the trend vector: The trend matrix and sample-based partial least squares. J. Comput. Aided Mol. Des. Robert P. Sheridan, Robert B. Nachbar, Bruce L. Bush
1994	J	`jnl`	FLOG: A system to select 'quasi-flexible' ligands complementary to a receptor of known three-dimensional structure. J. Comput. Aided Mol. Des. Michael D. Miller, Simon K. Kearsley, Dennis J. Underwood, Robert P. Sheridan
1994	J	`jnl`	Flexibases: A way to enhance the use of molecular docking methods. J. Comput. Aided Mol. Des. Simon K. Kearsley, Dennis J. Underwood, Robert P. Sheridan, Michael D. Miller
1993	J	`jnl`	PATTY: A programmable atom type and language for automatic classification of atoms in molecular databases. J. Chem. Inf. Comput. Sci. Bruce L. Bush, Robert P. Sheridan
1989	J	`jnl`	3DSEARCH: a system for three-dimensional substructure searching. J. Chem. Inf. Comput. Sci. Robert P. Sheridan, Ramaswamy Nilakantan, Andrew Rusinko III, Norman Bauman, Kevin S. Haraki, R. Venkataraghavan
1989	J	`jnl`	Using CONCORD to construct a large database of three-dimensional coordinates from connection tables. J. Chem. Inf. Comput. Sci. Andrew Rusinko III, Robert P. Sheridan, Ramaswamy Nilakantan, Kevin S. Haraki, Norman Bauman, R. Venkataraghavan
1987	J	`jnl`	Designing novel nicotinic agonists by searching a database of molecular shapes. J. Comput. Aided Mol. Des. Robert P. Sheridan, R. Venkataraghavan