Abstract
In recent years, machine learning techniques have been widely used in biomedical research to predict unseen data based on models trained on experimentally derived data. In the current study, we used machine learning algorithms to emulate computationally complex predictions in a reverse engineering–like manner and developed ContraDRG, a software that can be used to predict partial charges for small molecules based on PRODRG and Automated Topology Builder (ATB) predictions. Both tools generate molecular topology files, including the partial atomic charge, by using different procedures. We show that ContraDRG can accurately predict partial charges in a fraction of the time, because it exploits existing complex models with intensive calculations by using machine learning techniques and thus can also be applied for screening projects with large amounts of molecules. We provide ContraDRG as a web server, which can be used to automatically assign partial charges to incoming user-specified molecules by using our machine learning models. In this study, we compared ContraDRG with PRODRG and ATB in regard of predictivity by statistical methods. ContraDRG allows predicting ATB-derived partial charges with an R2 value up to 0.980 and for PRODRG up to 1.00. While ATB requires hours or days for the quantum mechanical accurate calculation and refinements, ContraDRG does its approximation within seconds.
Highlights
In the last decades, several studies demonstrated how machine learning algorithms were able to create accurate predictions or classifications from experimentally derived data
We show that ContraDRG can accurately predict partial charges in a fraction of the time, because it exploits existing complex models with intensive calculations by using machine learning techniques and can be applied for screening projects with large amounts of molecules
This study demonstrates the usefulness of machine learning models for reverse engineering of costly calculations, which are provided in an easy-to-use online tool
Summary
Several studies demonstrated how machine learning algorithms were able to create accurate predictions or classifications from experimentally derived data. The applications of machine learning algorithms in biomedical research are diverse (Larrañaga et al, 2006) and range from single-molecule interaction prediction for drug design (Lavecchia, 2015) or omics pattern recognition (Stanke and Morgenstern, 2005), toward the prediction of entire biological systems (D’Alche-Buc and Wehenkel, 2008). In the current study, we used machine learning algorithms to emulate computationally intensive calculations. MD simulations depend heavily on the accurate parameterization of the molecules; otherwise, the simulations tend to be unreliable and misleading (Lemkul et al, 2010). One main challenge for generating reliable predictions is the ability to create
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