John B O Mitchell - Citegraph

Author Info

Name	Affiliation	Papers
JOHN B O MITCHELL	Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge. CB2 1EW, UK.	41
Collaborators	Citations	PageRank
68	384	32.48
Referers	Referees	References
996	953	401

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100996

Publications (41 rows)

Collaborators (68 rows)

Referers (100 rows)

Referees (100 rows)

Title	Citations	PageRank	Year
Can human experts predict solubility better than computers?	0	0.34	2017
Are the Sublimation Thermodynamics of Organic Molecules Predictable?	1	0.37	2016
Predicting targets of compounds against neurological diseases using cheminformatic methodology.	0	0.34	2015
Verifying the fully “Laplacianised” posterior Naïve Bayesian approach and more	3	0.42	2015
A note on utilising binary features as ligand descriptors.	0	0.34	2015
The Parzen Window method: In terms of two vectors and one matrix	0	0.34	2015
Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules.	5	0.47	2014
Erratum for "In Silico Target Predictions: Defining a Benchmarking Data Set and Comparison of Performance of the Multiclass Naı̈ve Bayes and Parzen-Rosenblatt Window".	0	0.34	2014
From sequence to enzyme mechanism using multi-label machine learning.	5	0.36	2014
The natural history of biocatalytic mechanisms.	0	0.34	2014
PFClust: an optimised implementation of a parameter-free clustering algorithm.	1	0.36	2014
In Silico Target Predictions: Defining a Benchmarking Data Set and Comparison of Performance of the Multiclass Naïve Bayes and Parzen-Rosenblatt Window.	23	0.81	2013
4273π: bioinformatics education on low cost ARM hardware.	5	0.34	2013
Full "Laplacianised" posterior naive Bayesian algorithm.	9	0.50	2013
Predicting the protein targets for athletic performance-enhancing substances.	4	0.40	2013
PFClust: a novel parameter free clustering algorithm.	10	0.46	2013
Winnow based identification of potent hERG inhibitors in silico: comparative assessment on different datasets.	1	0.39	2012
Predicting the mechanism of phospholipidosis.	10	0.56	2012
Is EC class predictable from reaction mechanism?	6	0.43	2012
Classifying Molecules Using a Sparse Probabilistic Kernel Binary Classifier.	7	0.55	2011
A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking	40	1.54	2010
Quantitative Comparison Of Catalytic Mechanisms And Overall Reactions In Convergently Evolved Enzymes: Implications For Classification Of Enzyme Function	5	0.58	2010
Why Are Some Properties More Difficult To Predict than Others? A Study of QSPR Models of Solubility, Melting Point, and Log P.	12	0.64	2008
Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics.	27	1.33	2008
How To Winnow Actives from Inactives: Introducing Molecular Orthogonal Sparse Bigrams (MOSBs) and Multiclass Winnow.	3	0.61	2008
Random Forest Models To Predict Aqueous Solubility.	28	1.87	2007
Scoring functions and enrichment: a case study on Hsp90.	17	0.62	2007
Macie (Mechanism, Annotation And Classification In Enzymes): Novel Tools For Searching Catalytic Mechanisms	19	1.13	2007
Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.	11	0.76	2007
Melting Point Prediction Employing -Nearest Neighbor Algorithms and Genetic Parameter Optimization.	28	1.74	2006
Classifying the world anti-doping agency’s 2005 prohibited list using the chemistry development kit fingerprint	3	0.41	2006
Chemoinformatics-Based Classification of Prohibited Substances Employed for Doping in Sport.	10	0.64	2006
Chemistry in bioinformatics.	3	0.61	2005
Communication and re-use of chemical information in bioscience.	7	1.12	2005
MACiE: a database of enzyme reaction mechanisms	11	0.83	2005
Evaluation of a knowledge-based potential of mean force for scoring docked protein-ligand complexes	5	0.56	2001
The Relationship between the Sequence Identities of Alpha Helical Proteins in the PDB and the Molecular Similarities of Their Ligands	7	0.95	2001
SATIS: Atom Typing from Chemical Connectivity	3	0.71	1999
BLEEP - potential of mean force describing protein-ligand interactions: I. Generating potential.	30	4.26	1999
BLEEP - potential of mean force describing protein-ligand interactions: II. Calculation of binding energies and comparison with experimental data	17	1.59	1999
Gaussian multipoles in practice: electrostatic energies for intermolecular potentials	8	1.53	1994