Machine Learning Boosted Docking (HASTEN): An Open-Source Tool To Accelerate Structurebased Virtual Screening Campaigns

01 April 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The software macHine leArning booSTed dockiNg (HASTEN) was developed to accelerate
structure-based virtual screening using machine learning models. It has been validated using
datasets both from literature (12 datasets, each containing three million molecules docked
with FRED) and in-house sources (one dataset of four million compounds docked with
Glide). HASTEN showed reasonable performance by having the mean recall value of 0.78 of
the top one percent scoring molecules after docking 10 % of the dataset for the literature data,
whereas excellent recall value of 0.95 was achieved for the in-house data. The program can be
used with any docking- and machine learning methodology, and is freely available from
https://github.com/TuomoKalliokoski/HASTEN.

Keywords

docking
machine learning
Structure-based virtual ligand screening

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