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Robert P. Sheridan
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2020 – today
- 2023
- [i5]Yuting Xu, Andy Liaw, Robert P. Sheridan, Vladimir Svetnik:
Development and Evaluation of Conformal Prediction Methods for QSAR. CoRR abs/2304.00970 (2023) - 2022
- [j51]Robert P. Sheridan, J. Christopher Culberson, Elizabeth Joshi, Matthew Tudor
, Prabha Karnachi:
Prediction Accuracy of Production ADMET Models as a Function of Version: Activity Cliffs Rule. J. Chem. Inf. Model. 62(14): 3275-3280 (2022) - [j50]Robert P. Sheridan
Stability of Prediction in Production ADMET Models as a Function of Version: Why and When Predictions Change. J. Chem. Inf. Model. 62(15): 3477-3485 (2022) - 2021
- [i4]Robert P. Sheridan, Andy Liaw, Matthew Tudor:
Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships. CoRR abs/2105.08626 (2021) - 2020
- [j49]Robert P. Sheridan
Correction to Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. 60(3): 1910 (2020) - [j48]Robert P. Sheridan
Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure-Activity Relationship Models? J. Chem. Inf. Model. 60(4): 1969-1982 (2020) - [j47]Yuting Xu
Deep Dive into Machine Learning Models for Protein Engineering. J. Chem. Inf. Model. 60(6): 2773-2790 (2020) - [j46]Anthony Difranzo
Nearest Neighbor Gaussian Process for Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. 60(10): 4653-4663 (2020) - [i3]Roc Reguant
AlignmentViewer: Sequence Analysis of Large Protein Families. F1000Research 9: 213 (2020)
2010 – 2019
- 2019
- [j45]Thomas A. Hopf
The EVcouplings Python framework for coevolutionary sequence analysis. Bioinform. 35(9): 1582-1584 (2019) - [j44]Robert P. Sheridan
Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It? J. Chem. Inf. Model. 59(4): 1324-1337 (2019) - [j43]Dai Feng
Building Quantitative Structure-Activity Relationship Models Using Bayesian Additive Regression Trees. J. Chem. Inf. Model. 59(6): 2642-2655 (2019) - [i2]Evan N. Feinberg, Robert P. Sheridan, Elizabeth Joshi, Vijay S. Pande, Alan C. Cheng:
Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurization. CoRR abs/1903.11789 (2019) - 2017
- [j42]Bharath Ramsundar
Is Multitask Deep Learning Practical for Pharma? J. Chem. Inf. Model. 57(8): 2068-2076 (2017) - [j41]Yuting Xu
Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. 57(10): 2490-2504 (2017) - 2016
- [j40]Robert P. Sheridan:
Debunking the Idea that Ligand Efficiency Indices Are Superior to pIC50 as QSAR Activities. J. Chem. Inf. Model. 56(11): 2253-2262 (2016) - [j39]Robert P. Sheridan
Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. 56(12): 2353-2360 (2016) - 2015
- [j38]Robert P. Sheridan, Daniel R. McMasters
eCounterscreening: Using QSAR Predictions to Prioritize Testing for Off-Target Activities and Setting the Balance between Benefit and Risk. J. Chem. Inf. Model. 55(2): 231-238 (2015) - [j37]Junshui Ma, Robert P. Sheridan, Andy Liaw, George E. Dahl, Vladimir Svetnik:
Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. 55(2): 263-274 (2015) - [j36]Robert P. Sheridan:
The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity. J. Chem. Inf. Model. 55(6): 1098-1107 (2015) - 2014
- [j35]Robert P. Sheridan:
Global Quantitative Structure-Activity Relationship Models vs Selected Local Models as Predictors of Off-Target Activities for Project Compounds. J. Chem. Inf. Model. 54(4): 1083-1092 (2014) - [j34]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:
Modeling a Crowdsourced Definition of Molecular Complexity. J. Chem. Inf. Model. 54(6): 1604-1616 (2014) - 2013
- [j33]Robert P. Sheridan:
Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction. J. Chem. Inf. Model. 53(4): 783-790 (2013) - [j32]Robert P. Sheridan:
Using Random Forest To Model the Domain Applicability of Another Random Forest Model. J. Chem. Inf. Model. 53(11): 2837-2850 (2013) - 2012
- [j31]Bin Chen, Robert P. Sheridan, Viktor Hornak, Johannes H. Voigt:
Comparison of Random Forest and Pipeline Pilot Naïve Bayes in Prospective QSAR Predictions. J. Chem. Inf. Model. 52(3): 792-803 (2012) - [j30]Robert P. Sheridan:
Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest. J. Chem. Inf. Model. 52(3): 814-823 (2012) - 2011
- [i1]Debora S. Marks, Lucy J. Colwell, Robert P. Sheridan, Thomas A. Hopf, Andrea Pagnani, Riccardo Zecchina, Chris Sander:
3D Protein Structure Predicted from Sequence. CoRR abs/1110.5091 (2011) - 2010
- [j29]Robert P. Sheridan, Vladimir N. Maiorov, M. Katharine Holloway, Wendy D. Cornell, Ying-Duo Gao:
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. 50(11): 2029-2040 (2010)
2000 – 2009
- 2009
- [j28]Robert P. Sheridan, Kiyean Nam, Vladimir N. Maiorov, Daniel R. McMasters
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. 49(8): 1974-1985 (2009) - [j27]Junshui Ma, Christopher Tong
Generating hypotheses about molecular structure-activity relationships (SARs) by solving an optimization problem. Stat. Anal. Data Min. 2(3): 161-174 (2009) - 2008
- [j26]Robert P. Sheridan, Georgia B. McGaughey, Wendy D. Cornell:
Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results. J. Comput. Aided Mol. Des. 22(3-4): 257-265 (2008) - [j25]Robert P. Sheridan:
Alternative Global Goodness Metrics and Sensitivity Analysis: Heuristics to Check the Robustness of Conclusions from Studies Comparing Virtual Screening Methods. J. Chem. Inf. Model. 48(2): 426-433 (2008) - 2007
- [j24]Georgia B. McGaughey, Robert P. Sheridan, Christopher I. Bayly, J. Christopher Culberson, Constantine Kreatsoulas
Comparison of Topological, Shape, and Docking Methods in Virtual Screening. J. Chem. Inf. Model. 47(4): 1504-1519 (2007) - [j23]Doron Betel
Computational Analysis of Mouse piRNA Sequence and Biogenesis. PLoS Comput. Biol. 3(11) (2007) - 2006
- [j22]Robert P. Sheridan, Peter Hunt, J. Christopher Culberson:
Molecular Transformations as a Way of Finding and Exploiting Consistent Local QSAR. J. Chem. Inf. Model. 46(1): 180-192 (2006) - 2005
- [j21]Vladimir Svetnik, Ting Wang, Christopher Tong
Boosting: An Ensemble Learning Tool for Compound Classification and QSAR Modeling. J. Chem. Inf. Model. 45(3): 786-799 (2005) - [j20]Vladimir N. Maiorov, Robert P. Sheridan:
Enhanced Virtual Screening by Combined Use of Two Docking Methods: Getting the Most on a Limited Budget. J. Chem. Inf. Model. 45(4): 1017-1023 (2005) - [j19]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:
Reagent Selector: Using Synthon Analysis to Visualize Reagent Properties and Assist in Combinatorial Library Design. J. Chem. Inf. Model. 45(5): 1439-1446 (2005) - 2004
- [j18]Robert P. Sheridan, Joseph Shpungin:
Calculating Similarities between Biological Activities in the MDL Drug Data Report Database. J. Chem. Inf. Model. 44(2): 727-740 (2004) - [j17]Robert P. Sheridan, Bradley P. Feuston, Vladimir N. Maiorov, Simon K. Kearsley:
Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR. J. Chem. Inf. Model. 44(6): 1912-1928 (2004) - 2003
- [j16]Robert P. Sheridan:
Finding Multiactivity Substructures by Mining Databases of Drug-Like Compounds. J. Chem. Inf. Comput. Sci. 43(3): 1037-1050 (2003) - [j15]Vladimir Svetnik, Andy Liaw, Christopher Tong
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. J. Chem. Inf. Comput. Sci. 43(6): 1947-1958 (2003) - 2002
- [j14]Robert P. Sheridan:
The Most Common Chemical Replacements in Drug-Like Compounds. J. Chem. Inf. Comput. Sci. 42(1): 103-108 (2002) - 2001
- [j13]Robert P. Sheridan, Suresh B. Singh, Eugene M. Fluder, Simon K. Kearsley:
Protocols for Bridging the Peptide to Nonpeptide Gap in Topological Similarity Searches. J. Chem. Inf. Comput. Sci. 41(5): 1395-1406 (2001) - 2000
- [j12]Robert P. Sheridan:
The Centroid Approximation for Mixtures: Calculating Similarity and Deriving Structure-Activity Relationships. J. Chem. Inf. Comput. Sci. 40(6): 1456-1469 (2000)
1990 – 1999
- 1998
- [j11]Robert P. Sheridan, Michael D. Miller:
A Method for Visualizing Recurrent Topological Substructures in Sets of Active Molecules. J. Chem. Inf. Comput. Sci. 38(5): 915-924 (1998) - 1996
- [j10]Simon K. Kearsley, Susan Sallamack, Eugene M. Fluder, Joseph D. Andose, Ralph T. Mosley, Robert P. Sheridan:
Chemical Similarity Using Physiochemical Property Descriptors. J. Chem. Inf. Comput. Sci. 36(1): 118-127 (1996) - [j9]Robert P. Sheridan, Michael D. Miller, Dennis J. Underwood, Simon K. Kearsley:
Chemical Similarity Using Geometric Atom Pair Descriptors. J. Chem. Inf. Comput. Sci. 36(1): 128-136 (1996) - 1995
- [j8]Robert P. Sheridan, Simon K. Kearsley:
Using a Genetic Algorithm To Suggest Combinatorial Libraries. J. Chem. Inf. Comput. Sci. 35(2): 310-320 (1995) - 1994
- [j7]Michael D. Miller, Simon K. Kearsley, Dennis J. Underwood, Robert P. Sheridan:
FLOG: A system to select 'quasi-flexible' ligands complementary to a receptor of known three-dimensional structure. J. Comput. Aided Mol. Des. 8(2): 153-174 (1994) - [j6]Robert P. Sheridan, Robert B. Nachbar, Bruce L. Bush:
Extending the trend vector: The trend matrix and sample-based partial least squares. J. Comput. Aided Mol. Des. 8(3): 323-340 (1994) - [j5]Simon K. Kearsley, Dennis J. Underwood, Robert P. Sheridan, Michael D. Miller:
Flexibases: A way to enhance the use of molecular docking methods. J. Comput. Aided Mol. Des. 8(5): 565-582 (1994) - 1993
- [j4]Bruce L. Bush, Robert P. Sheridan:
PATTY: A programmable atom type and language for automatic classification of atoms in molecular databases. J. Chem. Inf. Comput. Sci. 33(5): 756-762 (1993)
1980 – 1989
- 1989
- [j3]Andrew Rusinko III, Robert P. Sheridan, Ramaswamy Nilakantan, Kevin S. Haraki, Norman Bauman, R. Venkataraghavan:
Using CONCORD to construct a large database of three-dimensional coordinates from connection tables. J. Chem. Inf. Comput. Sci. 29(4): 251-255 (1989) - [j2]Robert P. Sheridan, Ramaswamy Nilakantan, Andrew Rusinko III, Norman Bauman, Kevin S. Haraki, R. Venkataraghavan:
3DSEARCH: a system for three-dimensional substructure searching. J. Chem. Inf. Comput. Sci. 29(4): 255-260 (1989) - 1987
- [j1]Robert P. Sheridan, R. Venkataraghavan:
Designing novel nicotinic agonists by searching a database of molecular shapes. J. Comput. Aided Mol. Des. 1(3): 243-256 (1987)
Coauthor Index
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