ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug Discovery

14 November 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Developing new drugs is an expensive and lengthy endeavor, partly due to the reliance on high-throughput screening (HTS), which involves significant costs and is time-consuming. Virtual screening, particularly molecular docking, offers a more cost-effective and faster alternative for identifying promising drug candidates. However, the effectiveness of molecular docking can vary greatly, which has led to the use of consensus docking approaches. These approaches combine results from different docking methods to improve the identification of active compounds and can reduce the occurrence of false positives. However, many of these methods do not fully leverage the latest advancements in docking technology. In response, we present ESSENCE-Dock (Effective Structural Screening ENrichment ConsEnsus Dock), a new consensus docking workflow aimed at decreasing false positives and increasing the discovery of active compounds. By utilizing a combination of novel docking algorithms, we improve the selection process for potential active compounds. ESSENCE-Dock has been made to be user-friendly, requiring only a few simple commands to perform a complete screening, while also being designed for use in high-performance computing (HPC) environments.

Keywords

Virtual Screening
Molecular Docking
Consensus Docking
ESSENCE-Dock
Metascreener

Supplementary materials

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Supplementary Data
Description
The Supplementary Data contains additional figures not included in the original manuscript. These include comparative bar charts of the Enrichment Factors (EFs) of the individual DUD-E targets for each method (Fig. S1). This document also contains distribution plots of the actives and decoys across different methods for all individual DUD-E targets (Fig. S2). These plots demonstrate the separation and overlap of the actives and decoys in the ranked lists of the methods.
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