Sciweavers

Share
ICML
2007
IEEE

An integrated approach to feature invention and model construction for drug activity prediction

9 years 12 months ago
An integrated approach to feature invention and model construction for drug activity prediction
We present a new machine learning approach for 3D-QSAR, the task of predicting binding affinities of molecules to target proteins based on 3D structure. Our approach predicts binding affinity by using regression on substructures discovered by relational learning. We make two contributions to the state-of-the-art. First, we use multiple-instance (MI) regression, which represents a molecule as a set of 3D conformations, to model activity. Second, the relational learning component employs the "Score As You Use" (SAYU) method to select substructures for their ability to improve the regression model. This is the first application of SAYU to multipleinstance, real-valued prediction. We evaluate our approach on three tasks and demonstrate that (i) SAYU outperforms standard coverage measures when selecting features for regression, (ii) the MI representation improves accuracy over standard single feature-vector encodings and (iii) combining SAYU with MI regression is more accurate fo...
David Page, Jesse Davis, Soumya Ray, Vítor
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors David Page, Jesse Davis, Soumya Ray, Vítor Santos Costa
Comments (0)
books