R. Sheridan

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Protein 3D Structure Computed from Evolutionary Sequence Variation

Thomas A. Hopf, Lucy J. Colwell, C. Sander, 2011, PloS one.

Three-Dimensional Structures of Membrane Proteins from Genomic Sequencing

Thomas A. Hopf, Lucy J. Colwell, B. Rost, 2012, Cell.

Docking flexible ligands to macromolecular receptors by molecular shape.

I. Kuntz, R. Sheridan, R. DesJarlais, 1986, Journal of medicinal chemistry.

Molecular Shape and Medicinal Chemistry: A Perspective

Ajay N. Jain, Scott P. Brown, A. Good, 2010, Journal of medicinal chemistry.

Probing the role of two hydrophobic active site residues in the human dihydrofolate reductase by site-directed mutagenesis.

R. Sheridan, R. Venkataraghavan, B. Schweitzer, 1989, The Journal of biological chemistry.

3DSEARCH: a system for three-dimensional substructure searching

R. Sheridan, R. Venkataraghavan, A. Rusinko, 1989, J. Chem. Inf. Comput. Sci..

Latent semantic structure indexing (LaSSI) for defining chemical similarity.

E. Fluder, R. Sheridan, S. Kearsley, 2001, Journal of medicinal chemistry.

The ensemble approach to distance geometry: application to the nicotinic pharmacophore.

R. Sheridan, J. S. Dixon, R. Venkataraghavan, 1986, Journal of medicinal chemistry.

Empirical regioselectivity models for human cytochromes P450 3A4, 2D6, and 2C9.

R. Sheridan, K. Korzekwa, R. A. Torres, 2007, Journal of medicinal chemistry.

A model for predicting likely sites of CYP3A4-mediated metabolism on drug-like molecules.

R. Sheridan, Lucy Q Shen, M. Walker, 2003, Journal of medicinal chemistry.

AlignmentViewer: Sequence Analysis of Large Protein Families

C. Sander, Yevgeniy Antipin, R. Sheridan, 2020, F1000Research.

New methods in computer-aided drug design

R. Sheridan, R. Venkataraghavan, 1987 .

Using shape complementarity as an initial screen in designing ligands for a receptor binding site of known three-dimensional structure.

I. Kuntz, R. Sheridan, R. DesJarlais, 1988, Journal of medicinal chemistry.

Protocols for Bridging the Peptide to Nonpeptide Gap in Topological Similarity Searches.

E. Fluder, R. Sheridan, S. Kearsley, 2001 .

A systematic search for protein signature sequences

R. Sheridan, R. Venkataraghavan, M. Sharp, 1992, Proteins: Structure, Function, and Bioinformatics.

Sequence analysis MSAViewer : interactive JavaScript visualization of multiple sequence alignments

Ian Sillitoe, Suzanna E. Lewis, Burkhard Rost, 2016 .

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

Robert P. Sheridan, Christopher I. Bayly, Vladimir N. Maiorov, 2007 .

Amino acid composition and hydrophobicity patterns of protein domains correlate with their structures

I D Kuntz, I. Kuntz, R. Sheridan, 1985, Biopolymers.

a-Helix dipole model and electrostatic stabilization of 4-a-helical proteins

R. Sheridan, R. Levy, F. R. SALEMMEt, 2022 .

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

Katrina W. Lexa, Rebecca T. Ruck, R. Sheridan, 2021, Organic Process Research & Development.

Establishment of P‐Glycoprotein Structure–Transport Relationships to Optimize CNS Exposure in Drug Discovery

R. Sheridan, J. Hochman, Sookhee N. Ha, 2015 .

Generating hypotheses about molecular structure–activity relationships (SARs) by solving an optimization problem

Andy Liaw, R. Sheridan, V. Svetnik, 2009 .

Finding Multiactivity Substructures by Mining Databases of Drug-Like Compounds

Robert P. Sheridan, R. Sheridan, 2003, J. Chem. Inf. Comput. Sci..

Amino acid substitution of arginine 80 in 17beta-hydroxysteroid dehydrogenase type 3 and its effect on NADPH cofactor binding and oxidation/reduction kinetics.

R. Sheridan, B. McKeever, W. Geissler, 2002, Biochimica et biophysica acta.

Modeling and predicting chiral stationary phase enantioselectivity: An efficient random forest classifier using an optimally balanced training dataset and an aggregation strategy.

Edward C Sherer, Christian Roussel, Patrick Piras, 2018, Journal of separation science.

Mining Chromatographic Enantioseparation Data Using Matched Molecular Pair Analysis

R. Sheridan, C. Welch, E. Sherer, 2016, Molecules.

Advances in automated docking applied to human immunodeficiency virus type 1 protease.

D. Underwood, R. Sheridan, S. Kearsley, 1994, Methods in Enzymology.

Generating plausible protein folds by secondary structure similarity

R. Sheridan, R. Venkataraghavan, J. Dixon, 2009 .

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

R. Sheridan, M. Tudor, E. Joshi, 2022, J. Chem. Inf. Model..

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

Katrina W. Lexa, Rebecca T. Ruck, R. Sheridan, 2021, Organic Process Research & Development.

3DSEARCH: a system for three-dimensional substructure searching

Robert P. Sheridan, Ramaswamy Nilakantan, Norman Bauman, 1989, J. Chem. Inf. Comput. Sci..

Mapping the dark space of chemical reactions with extended nanomole synthesis and MALDI-TOF MS

Robert P. Sheridan, Christopher J. Welch, Zhengwei Peng, 2018, Science.

Using a Genetic Algorithm To Suggest Combinatorial Libraries

Robert P. Sheridan, Simon K. Kearsley, R. Sheridan, 1995, J. Chem. Inf. Comput. Sci..

Inhibition of recombinant cytochrome P450 isoforms 2D6 and 2C9 by diverse drug-like molecules.

R. Sheridan, R. Nachbar, K. Korzekwa, 2007, Journal of medicinal chemistry.

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

R. Sheridan, 2022, J. Chem. Inf. Model..

The Most Common Chemical Replacements in Drug-Like Compounds

Robert P. Sheridan, R. Sheridan, 2002, J. Chem. Inf. Comput. Sci..

A 13C NMR study of the Mn2+ · bleomycin complex

R. Sheridan, Raj K. Gupta, 1981 .

The nature of hydrogen bonding in electron-transport proteins

R. Sheridan, L. C. Allen, 1980 .

Toward structure-based predictive tools for the selection of chiral stationary phases for the chromatographic separation of enantiomers.

Edward C Sherer, Christian Roussel, Patrick Piras, 2016, Journal of chromatography. A.

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

Robert P. Sheridan, Andy Liaw, Vladimir Svetnik, 2003, J. Chem. Inf. Comput. Sci..

Why do we need so many chemical similarity search methods?

Robert P Sheridan, Simon K Kearsley, R. Sheridan, 2002, Drug discovery today.

1H nuclear relaxation study of the aniline‐binding site in human hemoglobin

R. Sheridan, Raj K. Gupta, 1981 .

Combined effect of restricted rotational diffusion plus jumps on nuclear magnetic resonance and fluorescence probes of aromatic ring motions in proteins.

R. Sheridan, R. Levy, 1983, Biophysical journal.

The active site electrostatic potential of human carbonic anhydrase

R. Sheridan, L. C. Allen, 1981 .

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

Andy Liaw, Junshui Ma, Wei Min Wang, 2016, J. Chem. Inf. Model..

Computational Analysis of Mouse piRNA Sequence and Biogenesis

Doron Betel, Robert P. Sheridan, Debora S. Marks, 2007, PLoS Comput. Biol..

Is Multitask Deep Learning Practical for Pharma?

Andreas Verras, Bharath Ramsundar, Robert P Sheridan, 2017, J. Chem. Inf. Model..

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

Robert P. Sheridan, Andy Liaw, Junshui Ma, 2015, J. Chem. Inf. Model..

Hydrogen‐bond cooperativity in protein secondary structure

R. Sheridan, L. C. Allen, Richard H. Lee, 1979 .

CHEMGENIE: integration of chemogenomics data for applications in chemical biology.

Anne Mai Wassermann, Meir Glick, Dante A. Pertusi, 2018, Drug discovery today.

Robert P. Sheridan, Vladimir N. Maiorov, Simon K. Kearsley, 2004, J. Chem. Inf. Model..

MSAViewer: interactive JavaScript visualization of multiple sequence alignments

Burkhard Rost, Ian Sillitoe, Suzanna Lewis, 2016, Bioinform..

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, 2010, J. Chem. Inf. Model..

The EVcouplings Python framework for coevolutionary sequence analysis

Robert P. Sheridan, Debora S. Marks, Thomas A. Hopf, 2018, bioRxiv.

A Deep Dive into Machine Learning Models for Protein Engineering.

Nicholas Marshall, Junshui Ma, Vladimir Svetnik, 2020, Journal of chemical information and modeling.

QSAR without borders.

Artem Cherkasov, Tudor I. Oprea, Jürgen Bajorath, 2020, Chemical Society reviews.

alpha-Helix dipole model and electrostatic stabilization of 4-alpha-helical proteins.

F R Salemme, R. Sheridan, R. Levy, 1982, Proceedings of the National Academy of Sciences of the United States of America.

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

Robert P. Sheridan, Christopher I. Bayly, Vladimir N. Maiorov, 2007, J. Chem. Inf. Model..

Robert P. Sheridan, Simon K. Kearsley, Joseph D. Andose, 1996, J. Chem. Inf. Comput. Sci..

Robert P. Sheridan, Joseph Shpungin, R. Sheridan, 2004, J. Chem. Inf. Model..

Role of lysine-54 in determining cofactor specificity and binding in human dihydrofolate reductase.

R. Sheridan, R. Venkataraghavan, Raymond L. Blakley, 1990, Biochemistry.

Flexibases: A way to enhance the use of molecular docking methods

Robert P. Sheridan, Michael D. Miller, Simon K. Kearsley, 1994, J. Comput. Aided Mol. Des..

FLOG: A system to select ‘quasi-flexible’ ligands complementary to a receptor of known three-dimensional structure

Robert P. Sheridan, Michael D. Miller, Simon K. Kearsley, 1994, J. Comput. Aided Mol. Des..

A Method for Visualizing Recurrent Topological Substructures in Sets of Active Molecules

Robert P. Sheridan, Michael D. Miller, R. Sheridan, 1998, J. Chem. Inf. Comput. Sci..

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

Robert P. Sheridan, Alejandro J. C. Crespo, Kenneth N. McGuinness, 2018, PloS one.

A simple method for visualizing the differences between related receptor sites.

Robert P Sheridan, M Katharine Holloway, Georgia McGaughey, 2002, Journal of molecular graphics & modelling.

The electrostatic potential of the alpha helix (electrostatic potential/alpha-helix/secondary structure/helix dipole).

R. Sheridan, L. C. Allen, 1980, Biophysical chemistry.

Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships

Andy Liaw, Junshui Ma, Vladimir Svetnik, 2017, J. Chem. Inf. Model..

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

Robert P. Sheridan, R. Sheridan, 2013, J. Chem. Inf. Model..

G2S: a web-service for annotating genomic variants on 3D protein structures

Dong Xu, Juexin Wang, Nikolaus Schultz, 2018, Bioinform..

The effect of deuterium substitution on hydrogen bonds in redox proteins.

R. Sheridan, E. T. Knight, L. C. Allen, 1984, Biopolymers.

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

Robert P. Sheridan, R. Sheridan, 2013, J. Chem. Inf. Model..

Boosting: An Ensemble Learning Tool for Compound Classification and QSAR Modeling

Ting Wang, Robert P. Sheridan, Andy Liaw, 2005, J. Chem. Inf. Model..

Coupling between oxidation state and hydrogen bond conformation in heme proteins.

R. Sheridan, P. Kahn, J. Valentine, 1979, Proceedings of the National Academy of Sciences of the United States of America.

Designing targeted libraries with genetic algorithms.

Robert P. Sheridan, Simon K. Kearsley, Sonia G. SanFeliciano, 2000, Journal of molecular graphics & modelling.

Protocols for Bridging the Peptide to Nonpeptide Gap in Topological Similarity Searches

Robert P. Sheridan, Simon K. Kearsley, Eugene M. Fluder, 2001, J. Chem. Inf. Comput. Sci..

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

Robert P. Sheridan, R. Sheridan, 2012, J. Chem. Inf. Model..

Correction: QSAR without borders.

Tudor I. Oprea, I. Tetko, D. Agrafiotis, 2020, Chemical Society reviews.

MOLECULAR DYNAMICS OF MYOGLOBIN AT 2980K Results from a 300-ps Computer Simulation

M. Karplus, R. Sheridan, R. Levy, 2005 .

Nearest Neighbor Gaussian Process for Quantitative Structure-Activity Relationships

Robert P Sheridan, Matthew Tudor, Andy Liaw, 2020, J. Chem. Inf. Model..

Experimental error, kurtosis, activity cliffs, and methodology: What limits the predictivity of QSAR models?

Meir Glick, Robert P Sheridan, Yuting Xu, 2020, Journal of chemical information and modeling.

Modeling assisted rational design of novel, potent, and selective pyrrolopyrimidine DPP-4 inhibitors.

R. Sheridan, G. Scapin, N. Thornberry, 2007, Bioorganic & medicinal chemistry letters.

Looking for pharmacophores in 3-D databases: Does conformational searching improve the yield of actives?

Robert P. Sheridan, R. Venkataraghavan, Richard McCulloch, 1990 .

Robert P. Sheridan, Michael D. Miller, Simon K. Kearsley, 1996, J. Chem. Inf. Comput. Sci..

Informing the Selection of Screening Hit Series with in Silico Absorption, Distribution, Metabolism, Excretion, and Toxicity Profiles.

Andreas Verras, Robert P Sheridan, J Christopher Culberson, 2017, Journal of medicinal chemistry.

Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships pdf

Robert P. Sheridan, Andy Liaw, Matthew Tudor, 2021, ArXiv.

Molecular Transformations as a Way of Finding and Exploiting Consistent Local QSAR

Robert P. Sheridan, Peter Hunt, J. Christopher Culberson, 2006, J. Chem. Inf. Model..

Electron spin resonance detection of free radicals in the mercaptan-activation and UV-inactivation of neocarzinostatin.

R. Sheridan, R. Gupta, 1981, Biochemical and biophysical research communications.

Mini review on molecular modeling of P-glycoprotein (Pgp).

R. Sheridan, J. Hochman, Sookhee N. Ha, 2007, Current topics in medicinal chemistry.

Searching for pharmacophores in large coordinate data bases and its use in drug design.

R. Sheridan, R. Venkataraghavan, A. Rusinko, 1989, Proceedings of the National Academy of Sciences of the United States of America.

Acetaldehyde hydrate and carbonic anhydrase: possible roles in the inhibition of brain aldehyde dehydrogenase.

R. Sheridan, C. Deakyne, L. C. Allen, 1980, Advances in experimental medicine and biology.

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

Robert P Sheridan, Derek T. Ahneman, Jesús G Estrada, 2018, Science.

Using CONCORD to construct a large database of three-dimensional coordinates from connection tables

Robert P. Sheridan, Ramaswamy Nilakantan, Norman Bauman, 1989, J. Chem. Inf. Comput. Sci..

Coupling between oxidation state and hydrogen bond conformation in high potential iron-sulfur protein.

R. Sheridan, C. Carter, L. C. Allen, 1981, The Journal of biological chemistry.

Modeling a Crowdsourced Definition of Molecular Complexity

Robert P. Sheridan, Edward C. Sherer, Charlie Chang, 2014, J. Chem. Inf. Model..

SQ: a program for rapidly producing pharmacophorically relevent molecular superpositions.

R. Sheridan, S. Kearsley, R P Sheridan, 1999, Journal of medicinal chemistry.

Development and Evaluation of Conformal Prediction Methods for QSAR

Andy Liaw, R. Sheridan, V. Svetnik, 2023, ArXiv.

Comparison of Random Forest and Pipeline Pilot Naïve Bayes in Prospective QSAR Predictions

Bin Chen, Robert P. Sheridan, Johannes H. Voigt, 2012, J. Chem. Inf. Model..

Robert P Sheridan, R. Sheridan, 2007, Expert opinion on drug discovery.

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

Robert P Sheridan, R. Sheridan, 2019, J. Chem. Inf. Model..

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

Robert P. Sheridan, R. Sheridan, 2014, J. Chem. Inf. Model..

Molecular dynamics of myoglobin at 298 degrees K. Results from a 300-ps computer simulation.

M Karplus, M. Karplus, S. Swaminathan, 1985, Biophysical journal.

Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results

Robert P. Sheridan, Wendy D. Cornell, Georgia B. McGaughey, 2008, J. Comput. Aided Mol. Des..

3D Protein Structure Predicted from Sequence pdf

Robert P. Sheridan, Debora S. Marks, Chris Sander, 2011, ArXiv.

Mining the chemical quarry with joint chemical probes: an application of latent semantic structure indexing (LaSSI) and TOPOSIM (Dice) to chemical database mining.

R D Hull, E. Fluder, R. Sheridan, 2001, Journal of medicinal chemistry.

The Centroid Approximation for Mixtures: Calculating Similarity and Deriving Structure-Activity Relationships

Robert P. Sheridan, R. Sheridan, 2000, J. Chem. Inf. Comput. Sci..

Comparison of Random Forest and Pipeline Pilot Naïve Bayes in Prospective QSAR Predictions

Bin Chen, R. Sheridan, V. Hornak, 2012, J. Chem. Inf. Model..

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

Robert P. Sheridan, R. Sheridan, 2015, J. Chem. Inf. Model..

Alternative Global Goodness Metrics and Sensitivity Analysis: Heuristics to Check the Robustness of Conclusions from Studies Comparing Virtual Screening Methods

R. Sheridan, 2008, J. Chem. Inf. Model..

Correction to Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships

Eric Gifford, Robert P Sheridan, Andy Liaw, 2020, J. Chem. Inf. Model..

Designing novel nicotinic agonists by searching a database of molecular shapes

Robert P. Sheridan, R. Venkataraghavan, R. Sheridan, 1987, J. Comput. Aided Mol. Des..

PATTY: A programmable atom type and language for automatic classification of atoms in molecular databases

Bruce L. Bush, Robert P. Sheridan, R. Sheridan, 1993, J. Chem. Inf. Comput. Sci..

QSAR Prediction of Passive Permeability in the LLC‐PK1 Cell Line: Trends in Molecular Properties and Cross‐Prediction of Caco‐2 Permeabilities

Andreas Verras, Robert P Sheridan, Edward C Sherer, 2012, Molecular informatics.

Building Quantitative Structure-Activity Relationship Models Using Bayesian Additive Regression Trees

Andy Liaw, Vladimir Svetnik, Robert P Sheridan, 2019, J. Chem. Inf. Model..