Inferring multi-target QSAR models with taxonomy-based multi-task learning.

ABSTRACT:

Background

A plethora of studies indicate that the development of multi-target drugs is beneficial for complex diseases like cancer. Accurate QSAR models for each of the desired targets assist the optimization of a lead candidate by the prediction of affinity profiles. Often, the targets of a multi-target drug are sufficiently similar such that, in principle, knowledge can be transferred between the QSAR models to improve the model accuracy. In this study, we present two different multi-task algorithms from the field of transfer learning that can exploit the similarity between several targets to transfer knowledge between the target specific QSAR models.

Results

We evaluated the two methods on simulated data and a data set of 112 human kinases assembled from the public datab

SUBMITTER: Rosenbaum L 

PROVIDER: S-EPMC4104930 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature