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. 2023 Mar 28;15:38. doi: 10.1186/s13321-023-00702-2

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© The Author(s) 2023

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 3 — a The docking score result and some samples with top score of DRD2 (left) and PARP1 (right). Green is known molecules, yellow is generated. The Lower docking score indicates higher binding affinity. b Tanimoto similarity between generated molecule and each molecule in the training dataset. c The difference of top 1000 molecules’ Docking Score distribution when contrastive learning is or isn’t used. The left is from DRD2 and the right is from PARP1. d The docking score result of Zero-shot generation to unseen targets. e Generated molecules with top binding affinity and the reference molecule for representative binding sites. The known inhibitors (the big image) and the generated molecules (the small four images) of DRD2 (left) and PARP1 (right)