Active Learning in the Drug Discovery Process

We investigate the following data mining problem from Computational Chemistry: From a large data set of compounds, find those that bind to a target molecule in as few iterations of biological testing as possible. In each iteration a comparatively small batch of compounds is screened for binding to the target. We apply active learning techniques for selecting the successive batches. One selection strategy picks unlabeled examples closest to the maximum margin hyperplane. Another produces many weight vectors by running perceptrons over multiple permutations of the data. Each weight vector votes with its

• Type: Conference
• Year: 2001
• Where: NIPS
• Authors: Manfred K. Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, Christian Lemmen
• Keywords: NIPS 2001, NIPS 2007, Picks Unlabeled Examples, Unlabeled Examples, Weight Vectors