Pages that link to "Q61999169"

The following pages link to Predicting the Predictability: A Unified Approach to the Applicability Domain Problem of QSAR Models (Q61999169):

Displaying 39 items.

• Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening (Q26750642) (← links)
• Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling (Q27902278) (← links)
• Which compound to select in lead optimization? Prospectively validated proteochemometric models guide preclinical development (Q28478130) (← links)
• A two-step target binding and selectivity support vector machines approach for virtual screening of dopamine receptor subtype-selective ligands (Q28480436) (← links)
• A Workflow to Investigate Exposure and Pharmacokinetic Influences on High-Throughput in Vitro Chemical Screening Based on Adverse Outcome Pathways (Q30044590) (← links)
• Binding affinity prediction with property-encoded shape distribution signatures (Q33757755) (← links)
• Discovery of potent, selective multidrug and toxin extrusion transporter 1 (MATE1, SLC47A1) inhibitors through prescription drug profiling and computational modeling (Q33799288) (← links)
• A quantitative structure- property relationship of gas chromatographic/mass spectrometric retention data of 85 volatile organic compounds as air pollutant materials by multivariate methods. (Q34272342) (← links)
• No longer confidential: estimating the confidence of individual regression predictions (Q34481989) (← links)
• Prediction of Cytochrome P450 Profiles of Environmental Chemicals with QSAR Models Built from Drug-like Molecules (Q34608018) (← links)
• In-silico approaches to multi-target drug discovery : computer aided multi-target drug design, multi-target virtual screening (Q37707220) (← links)
• Fragment Descriptors in Structure–Property Modeling and Virtual Screening (Q37788604) (← links)
• Pharmacophore-Based Virtual Screening (Q37788606) (← links)
• Chemoinformatic Classification Methods and their Applicability Domain (Q38918853) (← links)
• Introducing conformal prediction in predictive modeling for regulatory purposes. A transparent and flexible alternative to applicability domain determination (Q39064441) (← links)
• GTM-Based QSAR Models and Their Applicability Domains. (Q39525618) (← links)
• A Combinational Strategy of Model Disturbance and Outlier Comparison to Define Applicability Domain in Quantitative Structural Activity Relationship (Q39530796) (← links)
• QSAR Modelling of CYP3A4 Inhibition as a Screening Tool in the Context of DrugDrug Interaction Studies (Q39539730) (← links)
• A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression Predictions (Q39551017) (← links)
• In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids. (Q39560559) (← links)
• Estimation of the applicability domain of kernel-based machine learning models for virtual screening (Q39951742) (← links)
• Mixed learning algorithms and features ensemble in hepatotoxicity prediction. (Q45372748) (← links)
• QSPR models for predicting log P(liver) values for volatile organic compounds combining statistical methods and domain knowledge. (Q45959899) (← links)
• Redox Polypharmacology as an Emerging Strategy to Combat Malarial Parasites (Q46584366) (← links)
• Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology (Q47639565) (← links)
• A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: reliability-density neighbourhood. (Q49170393) (← links)
• Simultaneous Prediction of four ATP-binding Cassette Transporters' Substrates Using Multi-label QSAR. (Q50588937) (← links)
• Applicability Domains and Consistent Structure Generation (Q51028424) (← links)
• 4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling (Q51486138) (← links)
• Assessment of Methods To Define the Applicability Domain of Structural Alert Models (Q51579381) (← links)
• Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set (Q54376695) (← links)
• OPERA models for predicting physicochemical properties and environmental fate endpoints. (Q54974457) (← links)
• Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes (Q57793386) (← links)
• Role of simple descriptors and applicability domain in predicting change in protein thermostability (Q58759937) (← links)
• Quantifying model errors using similarity to training data (Q59254710) (← links)
• Rescoring of docking poses under Occam’s Razor: are there simpler solutions? (Q61999145) (← links)
• A Distance-Based Boolean Applicability Domain for Classification of High Throughput Screening Data (Q90642490) (← links)
• How Precise Are Our Quantitative Structure-Activity Relationship Derived Predictions for New Query Chemicals? (Q92940216) (← links)
• Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations (Q95318831) (← links)