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Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon
[Image: see text] In silico approaches have acquired a towering role in pharmaceutical research and development, allowing laboratories all around the world to design, create, and optimize novel molecular entities with unprecedented efficiency. From a toxicological perspective, computational methods...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10523574/ https://www.ncbi.nlm.nih.gov/pubmed/37584277 http://dx.doi.org/10.1021/acs.chemrestox.3c00137 |
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author | Bassani, Davide Brigo, Alessandro Andrews-Morger, Andrea |
author_facet | Bassani, Davide Brigo, Alessandro Andrews-Morger, Andrea |
author_sort | Bassani, Davide |
collection | PubMed |
description | [Image: see text] In silico approaches have acquired a towering role in pharmaceutical research and development, allowing laboratories all around the world to design, create, and optimize novel molecular entities with unprecedented efficiency. From a toxicological perspective, computational methods have guided the choices of medicinal chemists toward compounds displaying improved safety profiles. Even if the recent advances in the field are significant, many challenges remain active in the on-target and off-target prediction fields. Machine learning methods have shown their ability to identify molecules with safety concerns. However, they strongly depend on the abundance and diversity of data used for their training. Sharing such information among pharmaceutical companies remains extremely limited due to confidentiality reasons, but in this scenario, a recent concept named “federated learning” can help overcome such concerns. Within this framework, it is possible for companies to contribute to the training of common machine learning algorithms, using, but not sharing, their proprietary data. Very recently, Lhasa Limited organized a hackathon involving several industrial partners in order to assess the performance of their federated learning platform, called “Effiris”. In this paper, we share our experience as Roche in participating in such an event, evaluating the performance of the federated algorithms and comparing them with those coming from our in-house-only machine learning models. Our aim is to highlight the advantages of federated learning and its intrinsic limitations and also suggest some points for potential improvements in the method. |
format | Online Article Text |
id | pubmed-10523574 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-105235742023-09-28 Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon Bassani, Davide Brigo, Alessandro Andrews-Morger, Andrea Chem Res Toxicol [Image: see text] In silico approaches have acquired a towering role in pharmaceutical research and development, allowing laboratories all around the world to design, create, and optimize novel molecular entities with unprecedented efficiency. From a toxicological perspective, computational methods have guided the choices of medicinal chemists toward compounds displaying improved safety profiles. Even if the recent advances in the field are significant, many challenges remain active in the on-target and off-target prediction fields. Machine learning methods have shown their ability to identify molecules with safety concerns. However, they strongly depend on the abundance and diversity of data used for their training. Sharing such information among pharmaceutical companies remains extremely limited due to confidentiality reasons, but in this scenario, a recent concept named “federated learning” can help overcome such concerns. Within this framework, it is possible for companies to contribute to the training of common machine learning algorithms, using, but not sharing, their proprietary data. Very recently, Lhasa Limited organized a hackathon involving several industrial partners in order to assess the performance of their federated learning platform, called “Effiris”. In this paper, we share our experience as Roche in participating in such an event, evaluating the performance of the federated algorithms and comparing them with those coming from our in-house-only machine learning models. Our aim is to highlight the advantages of federated learning and its intrinsic limitations and also suggest some points for potential improvements in the method. American Chemical Society 2023-08-16 /pmc/articles/PMC10523574/ /pubmed/37584277 http://dx.doi.org/10.1021/acs.chemrestox.3c00137 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Bassani, Davide Brigo, Alessandro Andrews-Morger, Andrea Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon |
title | Federated
Learning in Computational Toxicology: An
Industrial Perspective on the Effiris Hackathon |
title_full | Federated
Learning in Computational Toxicology: An
Industrial Perspective on the Effiris Hackathon |
title_fullStr | Federated
Learning in Computational Toxicology: An
Industrial Perspective on the Effiris Hackathon |
title_full_unstemmed | Federated
Learning in Computational Toxicology: An
Industrial Perspective on the Effiris Hackathon |
title_short | Federated
Learning in Computational Toxicology: An
Industrial Perspective on the Effiris Hackathon |
title_sort | federated
learning in computational toxicology: an
industrial perspective on the effiris hackathon |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10523574/ https://www.ncbi.nlm.nih.gov/pubmed/37584277 http://dx.doi.org/10.1021/acs.chemrestox.3c00137 |
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