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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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The next generation of scenarios for climate change research and assessment

Nature (London), 2010-02, Vol.463 (7282), p.747-756

2010

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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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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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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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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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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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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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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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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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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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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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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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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 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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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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Predicting Intrinsic Aqueous Solubility by a Thermodynamic Cycle

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

2008

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Comments on “Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets”: Significance for the Validation of Scoring Functions

Journal of chemical information and modeling, 2011-08, Vol.51 (8), p.1739-1741

2011

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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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The natural history of biocatalytic mechanisms

PLoS computational biology, 2014-05, Vol.10 (5), p.e1003642-e1003642

2014

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From sequence to enzyme mechanism using multi-label machine learning

BMC bioinformatics, 2014-05, Vol.15 (1), p.150-150, Article 150

2014

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Observational Constraints on Past Attributable Warming and Predictions of Future Global Warming

Journal of climate, 2006-07, Vol.19 (13), p.3055-3069

2006

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Machine learning methods in chemoinformatics

The next generation of scenarios for climate change research and assessment

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

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?

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

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

Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics

Are the Sublimation Thermodynamics of Organic Molecules Predictable?

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

Random Forest Models To Predict Aqueous Solubility

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

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

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

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

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

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

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

Predicting Intrinsic Aqueous Solubility by a Thermodynamic Cycle

Comments on “Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets”: Significance for the Validation of Scoring Functions

Is EC class predictable from reaction mechanism?

The natural history of biocatalytic mechanisms

From sequence to enzyme mechanism using multi-label machine learning

Observational Constraints on Past Attributable Warming and Predictions of Future Global Warming

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