MolClass: a web portal to interrogate diverse small molecule screen datasets with different computational models.
The MolClass toolkit and data portal generate computational models from user-defined small molecule datasets based on structural features identified in hit and non-hit molecules in different screens. Each new model is applied to all datasets in the database to classify compound specificity. MolClass thus defines a likelihood value for each compound entry and creates an activity fingerprint across diverse sets of screens. MolClass uses a variety of machine-learning methods to find molecular patterns and can therefore also assign a priori predictions of bioactivities for previously untested molecules. The power of the MolClass resource will grow as a function of the number of screens deposited in the database.


METHODS
The MolClass webportal, software package and source code are freely available for non-commercial use at http://tyerslab.bio.ed.ac.uk/molclass. A MolClass tutorial and a guide on how to build models from datasets can also be found on the web site. MolClass uses the chemistry development kit (CDK), WEKA and MySQL for its core functionality. A REST service is available at http://tyerslab.bio.ed.ac.uk/molclass/api based on the OpenTox API 1.2.
Wellcome Trust Centre for Cell Biology and School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3JR, UK. jan.wildenhain@ed.ac.uk
Jan
J
Wildenhain
Nicholas
N
Fitzgerald
Mike
M
Tyers
D000465:Algorithms; D001185:Artificial Intelligence; D001669:Biochemical Phenomena; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D020407:Internet; D012984:Software