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However, this is fundamentally a low data problem that is incompatible with regular deep learning approaches. Contemporary deep networks require large amounts of training data, which severely limits the prediction of new molecular entities from limited available data. In this paper, we address the challenge of low data in molecular property prediction by: (1) defining a set of deep learning architectures that accept compound chemical structures in the form of molecular graphs, (2) creating a few-shot learning strategy across graph neural networks and convolutional neural networks to leverage the rich information of graph embeddings, and (3) proposing a two-module meta-learning framework to learn from task-transferable knowledge and predict molecular properties on few-shot data. Furthermore, we conduct multiple experiments on two benchmark multiproperty datasets to demonstrate a superior performance over conventional graph-based baselines. ROC-AUC results for 10-shot experiments show an average improvement of <jats:inline-formula><jats:alternatives><jats:tex-math>$$+11.37\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mo>+<\/mml:mo>\n                    <mml:mn>11.37<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> on Tox21 and <jats:inline-formula><jats:alternatives><jats:tex-math>$$+0.53\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mo>+<\/mml:mo>\n                    <mml:mn>0.53<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> on SIDER, which are representative small-sized biological datasets for molecular property prediction.<\/jats:p>","DOI":"10.1007\/s00521-023-08403-5","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T17:02:51Z","timestamp":1678467771000},"page":"13167-13185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Few-shot learning via graph embeddings with convolutional networks for low-data molecular property prediction"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9243-680X","authenticated-orcid":false,"given":"Luis","family":"Torres","sequence":"first","affiliation":[]},{"given":"Joel P.","family":"Arrais","sequence":"additional","affiliation":[]},{"given":"Bernardete","family":"Ribeiro","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"8403_CR1","doi-asserted-by":"publisher","first-page":"2694","DOI":"10.3762\/bjoc.12.267","volume":"12","author":"SP Leelananda","year":"2016","unstructured":"Leelananda SP, Lindert S (2016) Computational methods in drug discovery. 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