Active Learning for High Throughput Screening
DS '08 Proceedings of the 11th International Conference on Discovery Science
An Efficiently Computable Graph-Based Metric for the Classification of Small Molecules
DS '08 Proceedings of the 11th International Conference on Discovery Science
Recursive Neural Networks for Undirected Graphs for Learning Molecular Endpoints
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Structured output prediction of anti-cancer drug activity
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Multi-task drug bioactivity classification with graph labeling ensembles
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Computers & Mathematics with Applications
Annals of Mathematics and Artificial Intelligence
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Motivation: Several kernel-based methods have been recently introduced for the classification of small molecules. Most available kernels on molecules are based on 2D representations obtained from chemical structures, but far less work has focused so far on the definition of effective kernels that can also exploit 3D information. Results: We introduce new ideas for building kernels on small molecules that can effectively use and combine 2D and 3D information. We tested these kernels in conjunction with support vector machines for binary classification on the 60 NCI cancer screening datasets as well as on the NCI HIV data set. Our results show that 3D information leveraged by these kernels can consistently improve prediction accuracy in all datasets. Availability: An implementation of the small molecule classifier is available from http://www.dsi.unifi.it/neural/src/3DDK Contact: costa@dsi.unifi.it