Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/79871
Título: | Evaluating molecular representations in machine learning models for drug response prediction and interpretability |
Autor(es): | Baptista, Delora Correia, João Pereira, Bruno Rocha, Miguel |
Palavras-chave: | cancer deep learning drug sensitivity learned representations molecular fingerprints |
Data: | 26-Ago-2022 |
Editora: | De Gruyter |
Revista: | Journal of Integrative Bioinformatics |
Citação: | Baptista, Delora, Correia, João, Pereira, Bruno and Rocha, Miguel. "Evaluating molecular representations in machine learning models for drug response prediction and interpretability" Journal of Integrative Bioinformatics, vol. 19, no. 3, 2022, pp. 20220006. https://doi.org/10.1515/jib-2022-0006 |
Resumo(s): | Machine learning (ML) is increasingly being used to guide drug discovery processes. When applying ML approaches to chemical datasets, molecular descriptors and fingerprints are typically used to represent compounds as numerical vectors. However, in recent years, end-to-end deep learning (DL) methods that can learn feature representations directly from line notations or molecular graphs have been proposed as alternatives to using precomputed features. This study set out to investigate which compound representation methods are the most suitable for drug sensitivity prediction in cancer cell lines. Twelve different representations were benchmarked on 5 compound screening datasets, using DeepMol, a new chemoinformatics package developed by our research group, to perform these analyses. The results of this study show that the predictive performance of end-to-end DL models is comparable to, and at times surpasses, that of models trained on molecular fingerprints, even when less training data is available. This study also found that combining several compound representation methods into an ensemble can improve performance. Finally, we show that a post hoc feature attribution method can boost the explainability of the DL models. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/79871 |
DOI: | 10.1515/jib-2022-0006 |
ISSN: | 1613-4516 |
Versão da editora: | https://www.degruyter.com/document/doi/10.1515/jib-2022-0006/html |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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document_55770_1.pdf | 2,14 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons