Active Learning and Feature Selection in the Drug Discovery Process

Non-technical: In collaboration with the computational chemists at Telik, we will develop and apply novel approaches of Machine Learning to the characterization and classification of organic molecules with respect to their potential as pharmaceutical agents. In preliminary research we have already shown that our methods greatly improve the efficiency of the drug discovery cycle. In particular, we will develop search methods that identify small sets of chemical features of the compounds that are likely to be responsible for the relevant pharmaceutical properties. Technical: We propose to use modern Machine Learning techniques to help speed up the drug discovery cycle. Candidate compounds are represented as high-dimensional descriptor vectors. The algorithms are to decide which batch of compounds should be tested next and which features are responsible for the activity of the compounds. We use the maximum margin hyperplane separating the labeled compounds for selecting the next batch of unlabeled compounds. An alternate method based on the Voted Perceptron is more suitable for high-dimensional data. We also determine small sets of relevant features using the Maximum Entropy principle. ∗Computer Science Dept., University of California, Santa Cruz, CA 94065, USA