Conformer-based Multiple-Instance Learning for Predicting Biodegradability Classification

27 August 2024, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

In-silico methods are increasingly becoming reliable tools to replicate and extend from experimental findings of chemical biodegradability. Information derived from quantitative activity-structure relationships (QSARs) have the potential to have rules extracted that can aid the understanding of biodegradation. Using semi-empirical quantum chemical calculations, the use of a conformer-based augmentation approach, along with dimensionality reduction methods, was studied in the context of achieving improved model accuracy and applicability. This work highlights molecular features, from graph-based features, 3-dimensional structural descriptors, to direct graph-based learning methods, that can be used to distinguish readily biodegradable compounds, and the role of unsupervised pre-processing in refining the training set and choice of features.

Keywords

biodegradability

Supplementary materials

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Figure S1-S4, Tables S1-S3, Classification data.
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