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In Silico Target Predictions: Defining a Benchmarking Data Set and Comparison of Performance of the Multiclass Naive Bayes and Parzen-Rosenblatt Window

Journal of chemical information and modeling, 2013-08, Vol.53 (8), p.1957-1966

2013

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Can human experts predict solubility better than computers?

Journal of cheminformatics, 2017-12, Vol.9 (1), p.63-14, Article 63

2017

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Machine learning methods in chemoinformatics

Wiley interdisciplinary reviews. Computational molecular science, 2014-09, Vol.4 (5), p.468-481

Mitchell, John B. O.

2014

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Robust identification of interactions between heat-stress responsive genes in the chicken brain using Bayesian networks and augmented expression data

Scientific reports, 2024-04, Vol.14 (1), p.9019-9019, Article 9019

2024

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Practical application of a Bayesian network approach to poultry epigenetics and stress

BMC bioinformatics, 2022-07, Vol.23 (1), p.261-16, Article 261

2022

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Are the Sublimation Thermodynamics of Organic Molecules Predictable?

Journal of chemical information and modeling, 2016-11, Vol.56 (11), p.2162-2179

2016

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Why Are Some Properties More Difficult To Predict than Others? A Study of QSPR Models of Solubility, Melting Point, and Log P

Journal of chemical information and modeling, 2008-01, Vol.48 (1), p.220-232

2008

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Melting Point Prediction Employing k-Nearest Neighbor Algorithms and Genetic Parameter Optimization

Journal of chemical information and modeling, 2006-11, Vol.46 (6), p.2412-2422

2006

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Allosteric activation unveils protein-mass modulation of ATP phosphoribosyltransferase product release

Communications chemistry, 2024-04, Vol.7 (1), p.77-77, Article 77

2024

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A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

Bioinformatics, 2010-05, Vol.26 (9), p.1169-1175

2010

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Predicting Intrinsic Aqueous Solubility by a Thermodynamic Cycle

Molecular pharmaceutics, 2008-03, Vol.5 (2), p.266-279

2008

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Is Experimental Data Quality the Limiting Factor in Predicting the Aqueous Solubility of Druglike Molecules?

Molecular pharmaceutics, 2014-08, Vol.11 (8), p.2962-2972

2014

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Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics

Journal of chemical theory and computation, 2021-06, Vol.17 (6), p.3700-3709

2021

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Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

Current medicinal chemistry, 2019-01, Vol.26 (21), p.3874-3889

2019

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Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules

Journal of chemical information and modeling, 2014-03, Vol.54 (3), p.844-856

2014

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Classifying Molecules Using a Sparse Probabilistic Kernel Binary Classifier

Journal of chemical information and modeling, 2011-07, Vol.51 (7), p.1539-1544

2011

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A Bayesian network structure learning approach to identify genes associated with stress in spleens of chickens

Scientific reports, 2022-05, Vol.12 (1), p.7482-7482, Article 7482

2022

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Predicting targets of compounds against neurological diseases using cheminformatic methodology

Journal of computer-aided molecular design, 2015-02, Vol.29 (2), p.183-198

2015

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Is EC class predictable from reaction mechanism?

BMC bioinformatics, 2012-04, Vol.13 (1), p.60-60, Article 60

2012

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Random Forest Models To Predict Aqueous Solubility

Journal of chemical information and modeling, 2007-01, Vol.47 (1), p.150-158

2007

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Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies

Frontiers in neuroscience, 2016-06, Vol.10, p.265-265

2016

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First-Principles Calculation of the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules

Journal of chemical theory and computation, 2012-09, Vol.8 (9), p.3322-3337

2012

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Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility

ACS omega, 2024-08, Vol.9 (32), p.35209-35222

2024

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A note on utilising binary features as ligand descriptors

Journal of cheminformatics, 2015-12, Vol.7 (1), p.58-58, Article 58

2015

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Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics

Journal of Chemical Information and Modeling, 2008-12, Vol.48 (12), p.2313-2325

2008

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In Silico Target Predictions: Defining a Benchmarking Data Set and Comparison of Performance of the Multiclass Naive Bayes and Parzen-Rosenblatt Window

Can human experts predict solubility better than computers?

Machine learning methods in chemoinformatics

Robust identification of interactions between heat-stress responsive genes in the chicken brain using Bayesian networks and augmented expression data

Practical application of a Bayesian network approach to poultry epigenetics and stress

Are the Sublimation Thermodynamics of Organic Molecules Predictable?

Why Are Some Properties More Difficult To Predict than Others? A Study of QSPR Models of Solubility, Melting Point, and Log P

Melting Point Prediction Employing k-Nearest Neighbor Algorithms and Genetic Parameter Optimization

Allosteric activation unveils protein-mass modulation of ATP phosphoribosyltransferase product release

A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

Predicting Intrinsic Aqueous Solubility by a Thermodynamic Cycle

Is Experimental Data Quality the Limiting Factor in Predicting the Aqueous Solubility of Druglike Molecules?

Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics

Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules

Classifying Molecules Using a Sparse Probabilistic Kernel Binary Classifier

A Bayesian network structure learning approach to identify genes associated with stress in spleens of chickens

Predicting targets of compounds against neurological diseases using cheminformatic methodology

Is EC class predictable from reaction mechanism?

Random Forest Models To Predict Aqueous Solubility

Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies

First-Principles Calculation of the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules

Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility

A note on utilising binary features as ligand descriptors

Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics

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