Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models

Machine learning models predicting the bioactivity of chemical compounds belong nowadays to the standard tools of cheminformaticians and computational medicinal chemists. Multi-task and federated learning are promising machine learning approaches that allow privacy-preserving usage of large amounts...

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Autores principales:	Lina Humbeck, Tobias Morawietz, Noe Sturm, Adam Zalewski, Simon Harnqvist, Wouter Heyndrickx, Matthew Holmes, Bernd Beck
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning classification multi-task learning federated weighting drug design Organic chemistry QD241-441
Acceso en línea:	https://doaj.org/article/1a348a51ff7646c193a44ace0101e956
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spelling	oai:doaj.org-article:1a348a51ff7646c193a44ace0101e9562021-11-25T18:28:44ZDon’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models10.3390/molecules262269591420-3049https://doaj.org/article/1a348a51ff7646c193a44ace0101e9562021-11-01T00:00:00Zhttps://www.mdpi.com/1420-3049/26/22/6959https://doaj.org/toc/1420-3049Machine learning models predicting the bioactivity of chemical compounds belong nowadays to the standard tools of cheminformaticians and computational medicinal chemists. Multi-task and federated learning are promising machine learning approaches that allow privacy-preserving usage of large amounts of data from diverse sources, which is crucial for achieving good generalization and high-performance results. Using large, real world data sets from six pharmaceutical companies, here we investigate different strategies for averaging weighted task loss functions to train multi-task bioactivity classification models. The weighting strategies shall be suitable for federated learning and ensure that learning efforts are well distributed even if data are diverse. Comparing several approaches using weights that depend on the number of sub-tasks per assay, task size, and class balance, respectively, we find that a simple sub-task weighting approach leads to robust model performance for all investigated data sets and is especially suited for federated learning.Lina HumbeckTobias MorawietzNoe SturmAdam ZalewskiSimon HarnqvistWouter HeyndrickxMatthew HolmesBernd BeckMDPI AGarticlemachine learningclassificationmulti-task learningfederatedweightingdrug designOrganic chemistryQD241-441ENMolecules, Vol 26, Iss 6959, p 6959 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning classification multi-task learning federated weighting drug design Organic chemistry QD241-441
spellingShingle	machine learning classification multi-task learning federated weighting drug design Organic chemistry QD241-441 Lina Humbeck Tobias Morawietz Noe Sturm Adam Zalewski Simon Harnqvist Wouter Heyndrickx Matthew Holmes Bernd Beck Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models
description	Machine learning models predicting the bioactivity of chemical compounds belong nowadays to the standard tools of cheminformaticians and computational medicinal chemists. Multi-task and federated learning are promising machine learning approaches that allow privacy-preserving usage of large amounts of data from diverse sources, which is crucial for achieving good generalization and high-performance results. Using large, real world data sets from six pharmaceutical companies, here we investigate different strategies for averaging weighted task loss functions to train multi-task bioactivity classification models. The weighting strategies shall be suitable for federated learning and ensure that learning efforts are well distributed even if data are diverse. Comparing several approaches using weights that depend on the number of sub-tasks per assay, task size, and class balance, respectively, we find that a simple sub-task weighting approach leads to robust model performance for all investigated data sets and is especially suited for federated learning.
format	article
author	Lina Humbeck Tobias Morawietz Noe Sturm Adam Zalewski Simon Harnqvist Wouter Heyndrickx Matthew Holmes Bernd Beck
author_facet	Lina Humbeck Tobias Morawietz Noe Sturm Adam Zalewski Simon Harnqvist Wouter Heyndrickx Matthew Holmes Bernd Beck
author_sort	Lina Humbeck
title	Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models
title_short	Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models
title_full	Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models
title_fullStr	Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models
title_full_unstemmed	Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models
title_sort	don’t overweight weights: evaluation of weighting strategies for multi-task bioactivity classification models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1a348a51ff7646c193a44ace0101e956
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Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models

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