180 results on '"Robert P. Sheridan"'
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2. Mining Chromatographic Enantioseparation Data Using Matched Molecular Pair Analysis
3. Prediction Accuracy of Production ADMET Models as a Function of Version: Activity Cliffs Rule.
4. Stability of Prediction in Production ADMET Models as a Function of Version: Why and When Predictions Change.
5. Development and Evaluation of Conformal Prediction Methods for QSAR.
6. Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure-Activity Relationship Models?
7. Nearest Neighbor Gaussian Process for Quantitative Structure-Activity Relationships.
8. Deep Dive into Machine Learning Models for Protein Engineering.
9. Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?
10. Building Quantitative Structure-Activity Relationship Models Using Bayesian Additive Regression Trees.
11. Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships.
12. The EVcouplings Python framework for coevolutionary sequence analysis.
13. Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships.
14. Is Multitask Deep Learning Practical for Pharma?
15. Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurization.
16. Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships.
17. Debunking the Idea that Ligand Efficiency Indices Are Superior to pIC50 as QSAR Activities.
18. Driving Aspirational Process Mass Intensity Using Simple Structure-Based Prediction
19. AlignmentViewer: Sequence Analysis of Large Protein Families.
20. Correction to Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships.
21. Application of Machine Learning and Reaction Optimization for the Iterative Improvement of Enantioselectivity of Cinchona-Derived Phase Transfer Catalysts
22. eCounterscreening: Using QSAR Predictions to Prioritize Testing for Off-Target Activities and Setting the Balance between Benefit and Risk.
23. Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships.
24. The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity.
25. Modeling a Crowdsourced Definition of Molecular Complexity.
26. Global Quantitative Structure-Activity Relationship Models vs Selected Local Models as Predictors of Off-Target Activities for Project Compounds.
27. Using Random Forest To Model the Domain Applicability of Another Random Forest Model.
28. Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.
29. Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest.
30. Comparison of Random Forest and Pipeline Pilot Naïve Bayes in Prospective QSAR Predictions.
31. 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.
32. Generating hypotheses about molecular structure-activity relationships (SARs) by solving an optimization problem.
33. QSAR Models for Predicting the Similarity in Binding Profiles for Pairs of Protein Kinases and the Variation of Models between Experimental Data Sets.
34. Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure–Activity Relationship Models?
35. Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results.
36. Alternative Global Goodness Metrics and Sensitivity Analysis: Heuristics to Check the Robustness of Conclusions from Studies Comparing Virtual Screening Methods.
37. Comparison of Topological, Shape, and Docking Methods in Virtual Screening.
38. Molecular Transformations as a Way of Finding and Exploiting Consistent Local QSAR.
39. Enhanced Virtual Screening by Combined Use of Two Docking Methods: Getting the Most on a Limited Budget.
40. Boosting: An Ensemble Learning Tool for Compound Classification and QSAR Modeling.
41. Reagent Selector: Using Synthon Analysis to Visualize Reagent Properties and Assist in Combinatorial Library Design.
42. Driving Aspirational Process Mass Intensity Using SMART-PMI and Innovative Chemistry
43. Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR.
44. Calculating Similarities between Biological Activities in the MDL Drug Data Report Database.
45. Finding Multiactivity Substructures by Mining Databases of Drug-Like Compounds.
46. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling.
47. The Most Common Chemical Replacements in Drug-Like Compounds.
48. Protocols for Bridging the Peptide to Nonpeptide Gap in Topological Similarity Searches.
49. The Centroid Approximation for Mixtures: Calculating Similarity and Deriving Structure-Activity Relationships.
50. A Method for Visualizing Recurrent Topological Substructures in Sets of Active Molecules.
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