Interpreting linear support vector machine models with heat map molecule coloring.

ABSTRACT:

Background

Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity.

Results

We evaluated

SUBMITTER: Rosenbaum L 

PROVIDER: S-EPMC3076244 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature