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However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the wider application of AI to reaction prediction. Here, we introduce a novel reaction representation, GraphRXN, for reaction prediction. It utilizes a universal graph-based neural network framework to encode chemical reactions by directly taking two-dimension reaction structures as inputs. The GraphRXN model was evaluated by three publically available chemical reaction datasets and gave on-par or superior results compared with other baseline models. To further evaluate the effectiveness of GraphRXN, wet-lab experiments were carried out for the purpose of generating reaction data. GraphRXN model was then built on high-throughput experimentation data and a decent accuracy (R<jats:sup>2<\/jats:sup> of 0.712) was obtained on our in-house data. This highlights that the GraphRXN model can be deployed in an integrated workflow which combines robotics and AI technologies for forward reaction prediction.<\/jats:p>","DOI":"10.1186\/s13321-023-00732-w","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T13:02:03Z","timestamp":1691758923000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data"],"prefix":"10.1186","volume":"15","author":[{"given":"Baiqing","family":"Li","sequence":"first","affiliation":[]},{"given":"Shimin","family":"Su","sequence":"additional","affiliation":[]},{"given":"Chan","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Xinyue","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Lebin","family":"Su","sequence":"additional","affiliation":[]},{"given":"Zhunzhun","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Kuangbiao","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Hongming","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"key":"732_CR1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aat0805","author":"KR Campos","year":"2019","unstructured":"Campos KR, Coleman PJ, Alvarez JC et al (2019) The importance of synthetic chemistry in the pharmaceutical industry. 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