Deane CM - Search Results

1

Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures.

Carbery A, Buttenschoen M, Skyner R, von Delft F, Deane CM. Carbery A, et al. Among authors: deane cm. J Cheminform. 2024 Mar 14;16(1):32. doi: 10.1186/s13321-024-00821-4. J Cheminform. 2024. PMID: 38486231 Free PMC article.

2

WONKA: objective novel complex analysis for ensembles of protein-ligand structures.

Bradley AR, Wall ID, von Delft F, Green DV, Deane CM, Marsden BD. Bradley AR, et al. Among authors: deane cm. J Comput Aided Mol Des. 2015 Oct;29(10):963-73. doi: 10.1007/s10822-015-9866-z. Epub 2015 Sep 19. J Comput Aided Mol Des. 2015. PMID: 26387008 Free PMC article.

3

Partial-occupancy binders identified by the Pan-Dataset Density Analysis method offer new chemical opportunities and reveal cryptic binding sites.

Pearce NM, Bradley AR, Krojer T, Marsden BD, Deane CM, von Delft F. Pearce NM, et al. Among authors: deane cm. Struct Dyn. 2017 Feb 28;4(3):032104. doi: 10.1063/1.4974176. eCollection 2017 May. Struct Dyn. 2017. PMID: 28345007 Free PMC article.

4

A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density.

Pearce NM, Krojer T, Bradley AR, Collins P, Nowak RP, Talon R, Marsden BD, Kelm S, Shi J, Deane CM, von Delft F. Pearce NM, et al. Among authors: deane cm. Nat Commun. 2017 Apr 24;8:15123. doi: 10.1038/ncomms15123. Nat Commun. 2017. PMID: 28436492 Free PMC article.

5

Data Set Augmentation Allows Deep Learning-Based Virtual Screening to Better Generalize to Unseen Target Classes and Highlight Important Binding Interactions.

Scantlebury J, Brown N, Von Delft F, Deane CM. Scantlebury J, et al. Among authors: deane cm. J Chem Inf Model. 2020 Aug 24;60(8):3722-3730. doi: 10.1021/acs.jcim.0c00263. Epub 2020 Aug 4. J Chem Inf Model. 2020. PMID: 32701288 Free PMC article.

6

Ribosome occupancy profiles are conserved between structurally and evolutionarily related yeast domains.

Nissley DA, Carbery A, Chonofsky M, Deane CM. Nissley DA, et al. Among authors: deane cm. Bioinformatics. 2021 Jul 27;37(13):1853-1859. doi: 10.1093/bioinformatics/btab020. Bioinformatics. 2021. PMID: 33483722 Free PMC article.

7

Fragment Libraries Designed to Be Functionally Diverse Recover Protein Binding Information More Efficiently Than Standard Structurally Diverse Libraries.

Carbery A, Skyner R, von Delft F, Deane CM. Carbery A, et al. Among authors: deane cm. J Med Chem. 2022 Aug 25;65(16):11404-11413. doi: 10.1021/acs.jmedchem.2c01004. Epub 2022 Aug 12. J Med Chem. 2022. PMID: 35960886 Free PMC article.

8

A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening.

Scantlebury J, Vost L, Carbery A, Hadfield TE, Turnbull OM, Brown N, Chenthamarakshan V, Das P, Grosjean H, von Delft F, Deane CM. Scantlebury J, et al. Among authors: deane cm. J Chem Inf Model. 2023 May 22;63(10):2960-2974. doi: 10.1021/acs.jcim.3c00322. Epub 2023 May 11. J Chem Inf Model. 2023. PMID: 37166179 Free PMC article.

9

Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search.

Wills S, Sanchez-Garcia R, Dudgeon T, Roughley SD, Merritt A, Hubbard RE, Davidson J, von Delft F, Deane CM. Wills S, et al. Among authors: deane cm. J Chem Inf Model. 2023 Jun 12;63(11):3423-3437. doi: 10.1021/acs.jcim.3c00276. Epub 2023 May 25. J Chem Inf Model. 2023. PMID: 37229647 Free PMC article.

10

Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding.

Hadfield TE, Scantlebury J, Deane CM. Hadfield TE, et al. Among authors: deane cm. J Cheminform. 2023 Sep 19;15(1):84. doi: 10.1186/s13321-023-00755-3. J Cheminform. 2023. PMID: 37726844 Free PMC article.

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