Regression Enrichment Surfaces: a Simple Analysis Technique for Virtual Drug Screening Models
Quantitative Methods
2020-06-03 v1 Machine Learning
Machine Learning
Abstract
We present a new method for understanding the performance of a model in virtual drug screening tasks. While most virtual screening problems present as a mix between ranking and classification, the models are typically trained as regression models presenting a problem requiring either a choice of a cutoff or ranking measure. Our method, regression enrichment surfaces (RES), is based on the goal of virtual screening: to detect as many of the top-performing treatments as possible. We outline history of virtual screening performance measures and the idea behind RES. We offer a python package and details on how to implement and interpret the results.
Cite
@article{arxiv.2006.01171,
title = {Regression Enrichment Surfaces: a Simple Analysis Technique for Virtual Drug Screening Models},
author = {Austin Clyde and Xiaotian Duan and Rick Stevens},
journal= {arXiv preprint arXiv:2006.01171},
year = {2020}
}