Deep Learning Model of Dock by Dock Process Significantly Accelerate the Process of Docking-based Virtual Screening

TL;DR

This paper introduces MLDDM, a machine learning approach that accelerates docking-based virtual screening by over 120 times while maintaining high accuracy, enabling efficient screening of ultra-large compound libraries.

Contribution

The paper presents a novel ML model that simulates docking protocols, significantly speeding up virtual screening without sacrificing accuracy.

Findings

Over 120 times speed increase in virtual screening

High consistency rate (>0.8) with classical docking results

Effective identification of active compounds in case studies

Abstract

Docking-based virtual screening (VS process) selects ligands with potential pharmacological activities from millions of molecules using computational docking methods, which greatly could reduce the number of compounds for experimental screening, shorten the research period and save the research cost. Howerver, a majority of compouds with low docking scores could waste most of the computational resources. Herein, we report a novel and practical docking-based machine learning method called MLDDM (Machince Learning Docking-by-Docking Models). It is composed of a regression model and a classification model that simulates a classical docking by docking protocol ususally applied in many virtual screening projects. MLDDM could quickly eliminate compounds with low docking scores and the retained compounds with potential high docking scores would be examined for further real docking program. We demonstrated that MLDDM has a good ability to identify active compounds in the case studies for 10 specific protein targets. Compared to pure docking by docking based VS protocol, the VS process with MLDDM can achieve an over 120 times speed increment on average and the consistency rate with corresponding docking by docking VS protocol is above 0.8. Therefore, it would be promising to be used for examing ultra-large compound libraries in the current big data era.