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Here, we proposed an autoregressive torsion angle prediction model Tora3D for molecular 3D conformer generation. Rather than directly predicting the conformations in an end-to-end way, Tora3D predicts a set of torsion angles of rotatable bonds by an interpretable autoregressive method and reconstructs the 3D conformations from them, which keeps structural validity during reconstruction. Another advancement of our method over other conformational generation methods is the ability to use energy to guide the conformation generation. In addition, we propose a new message-passing mechanism that applies the Transformer to the graph to solve the difficulty of remote message passing. Tora3D shows superior performance to prior computational models in the trade-off between accuracy and efficiency, and ensures conformational validity, accuracy, and diversity in an interpretable way. Overall, Tora3D can be used for the quick generation of diverse molecular conformations and 3D-based molecular representation, contributing to a wide range of downstream drug design tasks.<\/jats:p>\n                <jats:p><jats:bold>Graphical Abstract<\/jats:bold><\/jats:p>","DOI":"10.1186\/s13321-023-00726-8","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T06:01:48Z","timestamp":1686117708000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Tora3D: an autoregressive torsion angle prediction model for molecular 3D conformation generation"],"prefix":"10.1186","volume":"15","author":[{"given":"Zimei","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Gang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Lin","family":"Ni","sequence":"additional","affiliation":[]},{"given":"RunZe","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Kaiyang","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Qun","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Xiangtai","family":"Kong","sequence":"additional","affiliation":[]},{"given":"Shengkun","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Xiaochu","family":"Tong","sequence":"additional","affiliation":[]},{"given":"Li","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Dingyan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaojie","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Mingyue","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Xutong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"726_CR1","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1038\/s41597-022-01288-4","volume":"9","author":"S Axelrod","year":"2022","unstructured":"Axelrod S, G\u00f3mez-Bombarelli R (2022) GEOM, energy-annotated molecular conformations for property prediction and molecular generation. 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