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We show that an algorithmic approach (Fragmenstein) that \u2018stitches\u2019 the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein\u2013ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode. Fragmenstein either takes the atomic coordinates of ligands from a experimental fragment screen and combines the atoms together to produce a novel merged virtual compound, or uses them to predict the bound complex for a provided molecule. The molecule is then energy minimised under strong constraints to obtain a structurally plausible conformer. The code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/oxpig\/Fragmenstein\" ext-link-type=\"uri\">https:\/\/github.com\/oxpig\/Fragmenstein<\/jats:ext-link>\n                    .\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Scientific contribution<\/jats:bold>\n                  <\/jats:p>\n                  <jats:p>This work shows the importance of using the coordinates of known binders when predicting the conformation of derivative molecules through a retrospective analysis of the COVID Moonshot data. This method has had a prior real-world application in hit-to-lead screening, yielding a sub-micromolar merger from parent hits in a single round. It is therefore likely to further benefit future drug design campaigns and be integrated in future pipelines.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1186\/s13321-025-00946-0","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T07:50:21Z","timestamp":1736754621000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Fragmenstein: predicting protein\u2013ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding\u2013based methodology"],"prefix":"10.1186","volume":"17","author":[{"given":"Matteo P.","family":"Ferla","sequence":"first","affiliation":[]},{"given":"Rub\u00e9n","family":"S\u00e1nchez-Garc\u00eda","sequence":"additional","affiliation":[]},{"given":"Rachael E.","family":"Skyner","sequence":"additional","affiliation":[]},{"given":"Stefan","family":"Gahbauer","sequence":"additional","affiliation":[]},{"given":"Jenny C.","family":"Taylor","sequence":"additional","affiliation":[]},{"given":"Frank","family":"von Delft","sequence":"additional","affiliation":[]},{"given":"Brian D.","family":"Marsden","sequence":"additional","affiliation":[]},{"given":"Charlotte M.","family":"Deane","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"key":"946_CR1","first-page":"371","volume":"50","author":"BJ Davis","year":"2017","unstructured":"Davis BJ, Roughley SD (2017) Fragment-based lead discovery. 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