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Here we present a similarity-based pairing method for generating compound pairs to train Siamese neural networks for regression tasks. In comparison with the conventional exhaustive pairing, it reduces the algorithm complexity from O(n\n                    <jats:sup>2<\/jats:sup>\n                    ) to O(n). It also results in a better prediction performance consistently on the three physicochemical datasets, using a multilayer perceptron with the circular fingerprint as a proof of concept. We further include into a Siamese neural network the transformer-based Chemformer, which extracts task-specific features from the simplified molecular-input line-entry system representation of compounds. Additionally, we propose a means to measure the prediction uncertainty by utilizing the variance in predictions from a set of reference compounds. Our results demonstrate that the high prediction accuracy correlates with the high confidence. Finally, we investigate implications of the similarity property principle in machine learning.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1186\/s13321-023-00744-6","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T08:02:45Z","timestamp":1693382565000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Similarity-based pairing improves efficiency of siamese neural networks for regression tasks and uncertainty quantification"],"prefix":"10.1186","volume":"15","author":[{"given":"Yumeng","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Janosch","family":"Menke","sequence":"additional","affiliation":[]},{"given":"Jiazhen","family":"He","sequence":"additional","affiliation":[]},{"given":"Eva","family":"Nittinger","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Tyrchan","sequence":"additional","affiliation":[]},{"given":"Oliver","family":"Koch","sequence":"additional","affiliation":[]},{"given":"Hongtao","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"key":"744_CR1","doi-asserted-by":"publisher","first-page":"4977","DOI":"10.1021\/jm4004285","volume":"57","author":"A Cherkasov","year":"2014","unstructured":"Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz\u2019min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014) QSAR modeling: where have you been? 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