Methodology of aiQSAR: a group-specific approach to QSAR modelling

Kristijan Vukovic,Emilio Benfenati,Domenico Gadaleta

doi:10.1186/s13321-019-0350-y

Abstract

BackgroundSeveral QSAR methodology developments have shown promise in recent years. These include the consensus approach to generate the final prediction of a model, utilizing new, advanced machine learning algorithms and streamlining, standardization and automation of various QSAR steps. One approach that seems under-explored is at-the-runtime generation of local models specific to individual compounds. This approach was quite likely limited by the computational requirements, but with current increases in processing power and the widespread availability of cluster-computing infrastructure, this limitation is no longer that severe.ResultsWe propose a new QSAR methodology: aiQSAR, whose aim is to generate endpoint predictions directly from the input dataset by building an array of local models generated at-the-runtime and specific for each compound in the dataset. The local group of each compound is selected on the basis of fingerprint similarities and the final prediction is calculated by integrating the results of a number of autonomous mathematical models. The method is applicable to regression, binary classification and multi-class classification and was tested on one dataset for each endpoint type: bioconcentration factor (BCF) for regression, Ames test for binary classification and Environmental Protection Agency (EPA) acute rat oral toxicity ranking for multi-class classification. As part of this method, the applicability domain of each prediction is assessed through the applicability domain measure, calculated on the basis of the fingerprint similarities in each local group of compounds.ConclusionsWe outline the methodology for a new QSAR-based predictive tool whose advantages are automation, group-specific approach to modelling and simplicity of execution. Our aim now will be to develop this method into a stand-alone software tool. We hope that eventual adoption of our tool would make QSAR modelling more accessible and transparent. Our methodology could be used as an initial modelling step, to predict new compounds by simply loading the training dataset as an input. Predictions could then be further evaluated and refined either by other tools or through optimization of aiQSAR parameters.

Highlights

The use of quantitative structure–activity relationship (QSAR) methods has expanded significantly in recent decades [1, 2]
We have proposed a new methodology for predictive QSAR modelling based on local group selection during model development and at-the-runtime execution
The main feature of aiQSAR is compound-specific building of predictive models: for each target compound, only a local group of training set” (TS) compounds that are structurally similar to the target is considered

Summary

Introduction

The use of quantitative structure–activity relationship (QSAR) methods has expanded significantly in recent decades [1, 2]. The first is the move towards an integrated approach in modelling [7] This means that the final output of a model is a consensus of several predictions, each obtained by a distinct QSAR approach [8,9,10]. Several QSAR methodology developments have shown promise in recent years These include the consensus approach to generate the final prediction of a model, utilizing new, advanced machine learning algorithms and streamlining, standardization and automation of various QSAR steps. One approach that seems under-explored is at-the-runtime generation of local models specific to individual compounds. This approach was quite likely limited by the computational requirements, but with current increases in processing power and the widespread availability of cluster-computing infrastructure, this limitation is no longer that severe

Objectives

Methods

Results

Conclusion