# Regression Enrichment Surfaces: a Simple Analysis Technique for Virtual Drug Screening Models

arXiv: https://arxiv.org/abs/2006.01171 · HTML: https://mlsanity.com/paper/2006.01171/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulations](https://mlsanity.com/paper/2407.07674/) (2024)
- [Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening](https://mlsanity.com/paper/2106.07036/) (2021)
- [Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks](https://mlsanity.com/paper/2211.02720/) (2022)

## Abstract (arXiv)

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.