Regression Enrichment Surfaces: a Simple Analysis Technique for Virtual Drug Screening Models
arXiv:2006.01171
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.
References in corpus (3)
- Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties
- AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
- Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening