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A full list of the group's publications can be found below. Click on the relevant years to see the corresponding papers. You can also download this information as a bibtex file here.

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Crook, O.M., Gittens, N., Chung, C.w. & Deane, C.M. (Rxiv) Inferring residue level hydrogen deuterium exchange with ReX bioRxiv
Ziv, Y., Marsden, B. & Deane, C. (Rxiv) MolSnapper: Conditioning Diffusion for Structure Based Drug Design bioRxiv
Moesser, M.A., Klein, D., Boyles, F., Deane, C.M., Baxter, A. & Morris, G.M. (Rxiv) Protein-Ligand Interaction Graphs: Learning from Ligand-Shaped 3D Interaction Graphs to Improve Binding Affinity Prediction bioRxiv
Chinery, L., Hummer, A.M., Mehta, B.B., Akbar, R., Rawat, P., Slabodkin, A., Quy, K.L., Lund-Johansen, F., Greiff, V., Jeliazkov, J.R. & Deane, C.M. (Rxiv) Baselining the Buzz Trastuzumab-HER2 Affinity, and Beyond bioRxiv
Ferla, M.P., Sanchez-Garcia, R., Skyner, R.E., Gahbauer, S., Taylor, J.C., von Delft, F., Marsden, B.D. & Deane, C.M. (Rxiv) Fragmenstein: predicting protein-ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology ChemRxiv
Olsen, T.H., Moal, I.H. & Deane, C. (Rxiv) Addressing the antibody germline bias and its effect on language models for improved antibody design bioRxiv
Jiang, Y., Rex, D.A.B., Schuster, D., Neely, B.A., Rosano, G.L., Volkmar, N., Momenzadeh, A., Peters-Clarke, T.M., Egbert, S., Kreimer, S., Doud, E.H., Crook, O.M., Yadav, A.K., Vanuopadath, M., Mayta, M.L., Duboff, A.G., Riley, N.M., Moritz, R.L. & Meyer, J.G. (Rxiv) Comprehensive Overview of Bottom-up Proteomics using Mass Spectrometry Rxiv
Durant, G., Boyles, F., Birchall, K., Marsden, B. & Deane, C.M. (Rxiv) Robustly interrogating machine learning-based scoring functions: what are they learning? bioRxiv
Huhn, A., Nissley, D., Wilson, D.B., Kutuzov, M., Donat, R., Tan, T.K., Zhang, Y., Barton, M.I., Liu, C., Dejnirattisai, W., Supasa, P., Mongkolsapaya, J., Townsend, A., James, W., Screaton, G., Merwe, P.A.v.d., Deane, C.M., Isaacson, S.A. & Dushek, O. (Rxiv) The molecular reach of antibodies determines their SARS-CoV-2 neutralisation potency bioRxiv
Hummer, A.M., Schneider, C., Chinery, L. & Deane, C.M. (Rxiv) Investigating the Volume and Diversity of Data Needed for Generalizable Antibody-Antigen ∆∆G Prediction bioRxiv
Homberg, S., Janosch, M., Morris, G.M. & Koch, O. (Rxiv) Interpreting Graph Neural Networks with Myerson Values for Cheminformatics Approaches ChemRxiv
Gordon, G.L., Raybould, M.I.J., Wong, A. & Deane, C.M. (2024) Prospects for the computational humanization of antibodies and nanobodies Frontiers in Immunology, 15:1399438
McMaster, B., Thorpe, C., Ogg, G., Deane, C.M. & Koohy, H. (2024) Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity? Nature Methods, 21:766-776
Fischer, K., Lulla, A., So, T., Pereyra-Gerber, P., Raybould, M.I.J., Kohler, T.N., Kaminski, T.S., Yam-Puc, J.C., Hughes, R., Leiss-Maier, F., Brear, P., Matheson, N.J., Deane, C.M., Hyvonen, M., Thaventhiran, J. & Hollfelder, F. (2024) Microfluidics-enabled fluorescence-activated cell sorting of single pathogen-specific antibody secreting cells for the rapid discovery of monoclonal antibodies Nature Biotechnology (Accepted, link to preprint)
Éliás, S., Wrzodek, C., Deane, C.M., Tissot, A.C., Klostermann, S. & Ros, F. (2024) Prediction of polyspecificity from antibody sequence data by machine learning Frontiers in Bioinformatics, 3:1286883
Jiang, Y., Deane, C.M., Morris, G.M. & O’Brien, E.P. (2024) It is theoretically possible to avoid misfolding into non-covalent lasso entanglements using small molecule drugs PLOS Computational Biology, 20(3):1-22
Carbery, A., Buttenschoen, M., Skyner, R., von Delft, F. & Deane, C.M. (2024) Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures Journal of Cheminformatics, 16(1):32
Theorell, J., Harrison, R., Williams, R., Raybould, M.I.J., Zhao, M., Fox, H., Fower, A., Miller, G., Wu, Z., Browne, E., Mgbachi, V., Sun, B., Mopuri, R., Li, Y., Waters, P., Deane, C.M., Handel, A., Makuch, M. & Irani, S.R. (2024) Ultrahigh frequencies of peripherally matured LGI1 & CASPR2-reactive B cells characterise encephalitis patient cerebrospinal fluid Proceedings of the National Academy of Sciences USA, 121(7):e2311049121
Raybould, M.I.J., Turnbull, O.M., Suter, A., Guloglu, B. & Deane, C.M. (2024) Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody profiling Communications Biology, 7:62
Outeiral, C. & Deane, C. (2024) Codon language embeddings provide strong signals for protein engineering Nature Machine Intelligence, 6:170-179
Greenshields-Watson, A., Abanades, B. & Deane, C.M. (2024) Investigating the ability of deep learning-based structure prediction to extrapolate and/or enrich the set of antibody CDR canonical forms Frontiers in Immunology, 15:1352703
Abanades, B., Olsen, T.H., Raybould, M.I.J., Aguilar-Sanjuan, B., Wong, W.K., Georges, G., Bujotzek, A. & Deane, C.M. (2024) The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures Nucleic Acids Research, 52(D1):D545-D551
Villanueva, E., Smith, T., Pizzinga, M., Elzek, M., Queiroz, R.M.L., Harvey, R.F., Breckels, L.M., Crook, O.M., Monti, M., Dezi, V., Willis, A.E. & Lilley, K.S. (2023) System-wide analysis of RNA and protein subcellular localization dynamics Nature Methods, 21:60-71
Hoerschinger, V.J., Waibl, F., Pomarici, N.D., Loeffler, J.R., Deane, C.M., Georges, G., Kettenberger, H., Fernández-Quintero, M. & Liedl, K.R. (2023) PEP-Patch: Electrostatics in Protein–Protein Recognition, Specificity, and Antibody Developability Journal of Chemical Information and Modelling, 63(22):6964-6971
Gordon, G.L., Capel, H.L., Guloglu, B., Richardson, E., Stafford, R.L. & Deane, C.M. (2023) A comparison of the binding sites of antibodies and single-domain antibodies Frontiers in Immunology, 14:1231623
Abanades, B., Wong, W.K., Boyles, F., Georges, G., Bujotzek, A. & Deane, C.M. (2023) ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins Communications Biology, 6:575
Wills, S., Sanchez-Garcia, R., Dudgeon, T., Roughley, S.D., Merritt, A., Hubbard, R.E., Davidson, J., von Delft, F. & Deane, C.M. (2023) Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search Journal of Chemical Information and Modeling, 63(11):3423-3437
Guloglu, B. & Deane, C.M. (2023) Specific attributes of the VL domain influence both the structure and structural variability of CDR-H3 through steric effects Frontiers in Immunology, 14:1223802
Scantlebury, J., Vost, L., Carbery, A., Hadfield, T.E., Turnbull, O.M., Brown, N., Chenthamarakshan, V., Das, P., Grosjean, H., von Delft, F. & Deane, C.M. (2023) A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening Journal of Chemical Information and Modeling, 63(10):2960-2974
Dablander, M., Hanser, T., Lambiotte, R. & Morris, G.M. (2023) Exploring QSAR Models for Activity-Cliff Prediction Journal of Cheminformatics, 15:47
Raybould, M.I.J., Nissley, D.A., Kumar, S. & Deane, C.M. (2023) Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies Frontiers in Immunology, 13:1080596
Crook, O.M., Chung, C.w. & Deane, C.M. (2023) A functional Bayesian model for hydrogen-deuterium exchange mass-spectrometry Journal of Proteome Research, 22(4):2959-2972
Mokaya, M., Imrie, F., van Hoorn, W.P., Kalisz, A., Bradley, A.R. & Deane, C.M. (2023) Testing the Limits of SMILES-based De Novo Molecular Generation with Curriculum and Deep Reinforcement Learning Nature Machine Intelligence, 5:386-394
Richardson, E., Binter, S., Kosmac, M., Ghraichy, M., von Niederhäusern, V., Kovaltsuk, A., Galson, J.D., Trück, J., Kelly, D.F., Deane, C.M., Kellam, P. & Watson, S. (2023) Characterisation of the immune repertoire of a humanised transgenic mouse through immunophenotyping and high-throughput sequencing eLife, 12:e81629
Boby, M.L., Fearson, D., Ferla, M., Filep, M., Koekemoer, L., Robinson, M.C., Carbery A. & Morris G.M. & Smilova M.D. & Wild C.F. as part of Consortium, T.C.M., Chodera, J.D., Lee, A.A., London, N., Von Delft, A. & Von Delft, F. (2023) Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors Science, 382(6671):abo7201
Crook, O.M., Cucuringu, M., Hurst, T., Schönlieb, C.B., Thorpe, M. & Zygalakis, K. (2023) A linear transportation L^p distance for pattern recognition Pattern Recognition, 147(110080)
Hadfield, T.E., Scantlebury, J. & Deane, C.M. (2023) Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding Journal of Cheminformatics, 15:84
Spoendlin, F.C., Abanades, B., Raybould, M.I.J., Wong, W.K., Georges, G. & Deane, C.M. (2023) Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope Frontiers in Molecular Biosciences, 10:1237621
Vales, S., Kryukova, J., Chandra, S., Smagurauskaite, G., Payne, M., Clark, C.J., Hafner, K., Mburu, P., Denisov, S., Davies, G., Outeiral, C., Deane, C.M., Morris, G.M. & Bhattacharya, S. (2023) Discovery and pharmacophoric characterization of chemokine network inhibitors using phage-display, saturation mutagenesis and computational modelling Nature Communications, 14:5763
Buttenschoen, M., Morris, G.M. & Deane, C.M. (2023) PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences Chemical Science, 15(9):3130-3139
Olsen, T.H., Abanades, B., Moal, I.H. & Deane, C.M. (2023) KA-Search, a method for rapid and exhaustive sequence identity search of known antibodies Scientific Reports, 13(1):11612
Klarner, L., Rudner, T.G.J., Reutlinger, M., Schindler, T., Morris, G.M., Deane, C.M. & Teh, Y.W. (2023) Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions Proceedings of the 40th International Conference on Machine Learning, PMLR, 202():17176-17191
Pardo-Diaz, J., Poole, P.S., Beguerisse-Díaz, M., Deane, C.M. & Reinert, G. (2022) Generating weighted and thresholded gene coexpression networks using signed distance correlation Network Science, 10(2):131-145
Baddock, H.T., Brolih, S., Yosaatmadja, Y., Ratnaweera, M., Bielinski, M., Swift, L., Cruz-Migoni, A., Fan, H., Keown, J.R., Walker, A.P., Morris, G., Grimes, J., Fodor, E., Schofield, C., Gileadi, O. & McHugh, P. (2022) Characterization of the SARS-CoV-2 ExoN (nsp14ExoN–nsp10) complex: implications for its role in viral genome stability and inhibitor identification Nucleic Acids Research, 50(3):1484-1500
Crook, O.M., Chung, C.w. & Deane, C.M. (2022) Challenges and Opportunities for Bayesian Statistics in Proteomics Journal of Proteome Research, 21(4):849-864
Hadfield, T.E., Imrie, F., Merritt, A., Birchall, K. & Deane, C.M. (2022) Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration Journal of Chemical Information and Modeling, 62(10):2280-2292
Lomize, A.L., Schnitzer, K.A., Todd, S.C., Cherepanov, S., Outeiral, C., Deane, C.M. & Pogozheva, I.D. (2022) Membranome 3.0: Database of single-pass membrane proteins with AlphaFold models Protein Science, 31(5):e4318
Hummer, A.M., Abanades, B. & Deane, C.M. (2022) Advances in computational structure-based antibody design Current Opinion in Structural Biology, 74:102379
Sanchez-Garcia, R., Havasi, D., Takács, G., Robinson, M.C., Lee, A., von Delft, F. & Deane, C.M. (2022) CoPriNet: Deep learning compound price prediction for use in de novo molecule generation and prioritization Digital Discovery, 2:103-111
Outeiral, C., Nissley, D.A. & Deane, C.M. (2022) Current structure predictors are not learning the physics of protein folding Bioinformatics, 38(7):1881-1887
Abanades, B., Georges, G., Bujotzek, A. & Deane, C.M. (2022) ABlooper: Fast accurate antibody CDR loop structure prediction with accuracy estimation Bioinformatics, 38(7):1887-1880
Hadfield, T.E. & Deane, C.M. (2022) AI in 3D compound design Current Opinion in Structural Biology, 73:102326
Olsen, T.H., Moal, I.H. & Deane, C.M. (2022) AbLang: An antibody language model for completing antibody sequences Bioinformatics Advances, ():vbac046
Khetan, R., Curtis, R., Deane, C.M., Hadsung, J.T., Kar, U., Krawczyk, K., Kuroda, D., Robinson, S.A., Sormanni, P., Tsumoto, K., Warwicker, J. & Martin, A.C.R. (2022) Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics mAbs, 14(1):2020082
Ko, K.T., Lennartz, F., Mekhaiel, D., Guloglu, B., Marini, A., Deuker, D.J., Long, C.A., Jore, M.M., Miura, K., Biswas, S. & Higgins, M.K. (2022) Structure of the malaria vaccine candidate Pfs48/45 and its recognition by transmission blocking antibodies Nature Communications, 13(1):5603
Schneider, C., Raybould, M.I.J. & Deane, C.M. (2022) SAbDab in the age of biotherapeutics: updates including SAbDab-nano, the nanobody structure tracker Nucleic Acids Research, 50(D1):D1368-D1372
Chinery, L., Wahome, N., Moal, I. & Deane, C.M. (2022) Paragraph - Antibody paratope prediction using Graph Neural Networks with minimal feature vectors Bioinformatics, 39:btac732
Carbery, A., Skyner, R., von Delft, F. & Deane, C.M. (2022) Fragment Libraries Designed to Be Functionally Diverse Recover Protein Binding Information More Efficiently Than Standard Structurally Diverse Libraries Journal of Medicinal Chemistry, 65(16):11404-11413
Goto, A., Rodriguez-Esteban, R., Scharf, S.H. & Morris, G.M. (2022) Understanding the genetics of viral drug resistance by integrating clinical data and mining of the scientific literature Scientific Reports, 12:14476
Wang, Y., Tsitsiklis, A., Devoe, S., Gao, W., Chu, H.H., Zhang, Y., Li, W., Wong, W.K., Deane, C.M., Neau, D., Slansky, J.E., Thomas, P.G., Robey, E.A. & Dai, S. (2022) Peptide Centric Vβ Specific Germline Contacts Shape a Specialist T Cell Response Frontiers in Immunology, 13:847092
Pardo-Diaz, J., Beguerisse-Diaz, M., Poole, P.S., Deane, C.M. & Reinert, G. (2022) Extracting Information from Gene Coexpression Networks of Rhizobium leguminosarum Journal of Computational Biology, 27(7):752-768
Meli, R., Morris, G.M. & Biggin, P.C. (2022) Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review Frontiers in Bioinformatics, 2:885983
Crook, O.M., Chung, C.w. & Deane, C.M. (2022) Empirical Bayes functional models for hydrogen deuterium exchange mass spectrometry Communications Biology, 5:588
Raybould, M.I.J., Rees, A.R. & Deane, C.M. (2021) Current strategies for detecting functional convergence across B-cell receptor repertoires MAbs, 13(1):1996732
Outeiral, C., Morris, G.M., Shi, J., Strahm, M., Benjamin, S.C. & Deane, C.M. (2021) Investigating the potential for a limited quantum speedup on protein lattice problems New Journal of Physics, 23(10):103030
Marks, C., Hummer, A.M., Chin, M. & Deane, C.M. (2021) Humanization of antibodies using a machine learning approach on large-scale repertoire data Bioinformatics, 37(22):4041-4047
Robinson, S.A., Raybould, M.I.J., Schneider, C., Wong, W.K., Marks, C. & Deane, C.M. (2021) Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies PLoS Computational Biology, 17(2):e1009675
Chan, L., Morris, G.M. & Hutchison, G.R. (2021) Understanding Conformational Entropy in Small Molecules Journal of Chemical Theory and Computation, 17(4):2099-2106
Macpherson, A., Laabei, M.L., Ahdash, Z.A., Graewert, M., Birtley, J.R., Schulze, S., Crennell, S., Robinson, S.A., Holmes, B., Oleinikovas, V., Nilsson, P.H., Snowden, J., Ellis, V., Mollnes, T.E., Deane, C.M., Svergun, D., Lawson, A.D.G. & van den Elsen, J. (2021) The allosteric modulation of Complement C5 by knob domain peptides eLife, 10:e63586
Imrie, F., Bradley, A.R. & Deane, C. (2021) Generating Property-Matched Decoy Molecules Using Deep Learning Bioinformatics, 37(15):2134-2141
Nissley, D.A., Carbery, A., Chonofsky, M. & Deane, C.M. (2021) Ribosome occupancy profiles are conserved between structurally and evolutionarily related yeast domains Bioinformatics, 37(13):1853-1859
Chan, L., Hutchison, G. & Morris, G.M. (2021) Understanding Ring Puckering in Small Molecules and Cyclic Peptides Journal of Chemical Information and Modeling, 61(2):743-755
Pardo-Diaz, J., Bozhilova, L.V., Mariano, B.D., Poole, P.S., Deane, C.M. & Reinert, G. (2021) Robust gene coexpression networks using signed distance correlation Bioinformatics, 37(14):1982-1989
Richardson, E., Galson, J.D., Kellam, P., Kelly, D.F., Smith, S.E., Palser, A., Watson, S. & Deane, C.M. (2021) A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-Pertussis toxoid antibodies MAbs, 13(1):1869406
Raybould, M.I.J., Kovaltsuk, A., Marks, C. & Deane, C.M. (2021) CoV-AbDab: the Coronavirus Antibody Database Bioinformatics, 37(5):734-735
Imrie, F., Hadfield, T.E., Bradley, A.R. & Deane, C.M. (2021) Deep generative design with 3D pharmacophoric constraints Chemical Science, 12:14577-14589
Klimm, F., Deane, C.M. & Reinert, G. (2021) Hypergraphs for predicting essential genes using multiprotein complex data Journal of Complex Networks, 9(2):cnaa028
Wong, W.K., Robinson, S.A., Bujotzek, A., Georges, G., Lewis, A.P., Shi, J., Snowden, J., Taddese, B. & Deane, C.M. (2021) Ab-Ligity: Identifying sequence-dissimilar antibodies that bind to the same epitope MAbs, 13(1):1873478
Raybould, M.I.J., Marks, C., Kovaltsuk, A., Lewis, A.P., Shi, J. & Deane, C.M. (2021) Public Baseline and Shared Response Structures Support the Theory of Antibody Repertoire Functional Commonality PLoS Computational Biology, 17(3):e1008781
Olsen, T.H., Boyles, F. & Deane, C.M. (2021) OAS: A diverse database of cleaned, annotated and translated unpaired and paired antibody sequences Protein Science
Schneider, C., Buchanan, A., Taddese, B. & Deane, C.M. (2021) DLAB—Deep learning methods for structure-based virtual screening of antibodies Bioinformatics, 38(2):377-383
Chan, H.T.H., Moesser, M.A., Walters, R.K., Malla, T.R., Twidale, R.M., John, T., Deeks, H.M., Johnston-Wood, T., Mikhailov, V., Sessions, R.B., Dawson, W., Saleh, E., Lukacik, P., Strain-Damerell, C., Owen, C.D., Nakajima, T., Swiderek, K., Lodola, A., Moliner, V., Glowacki, D.R., Spencer, J., Walsh, M.A.A., Schofield, C.J., Genovese, L., Shoemark, D.K., Mulholland, A.J., Duarte, F. & Morris, G.M. (2021) Discovery of SARS-CoV-2 Mpro Peptide Inhibitors from Modelling Substrate and Ligand Binding Chemical Science, 12:13686-13703
Boyles, F., Deane, C.M. & Morris, G.M. (2021) Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses Journal of Chemical Information and Modeling
Schwarz, D., Georges, G., Kelm, S., Shi, J., Vangone, A. & Deane, C.M. (2021) Co-evolutionary distance predictions contain flexibility information Bioinformatics, 38(1):65-72
Meli, R., Anighoro, A., Bodkin, M.J., Morris, G.M. & Biggin, P.C. (2021) Learning protein-ligand binding affinity with atomic environment vectors Journal of Cheminformatics, 13:59
Ghraichy, M., von Niederhäusern, V., Kovaltsuk, A., Galson, J.D., Deane, C.M. & Trück, J. (2021) Different B cell subpopulations show distinct patterns in their IgH repertoire metrics eLife, 10:e73111
Bozhilova, L.V., Pardo-Diaz, J., Reinert, G. & Deane, C.M. (2020) COGENT: evaluating the consistency of gene co-expression networks Bioinformatics, ():btaa787
Galson, J.D., Schaetzle, S., Bashford-Rogers, R.J.M., Raybould, M.I.J., Kovaltsuk, A., Kilpatrick, G.J., Minter, R., Finch, D.K., Dias, J., James, L., Thomas, G., Lee, W.Y.J., Betley, J., Cavlan, O., Leech, A., Deane, C.M., Seoane, J., Caldas, C., Pennington, D., Pfeffer, P. & Osbourn, J. (2020) Deep sequencing of B cell receptor repertoires from COVID-19 patients reveals strong convergent immune signatures Frontiers in Immunology, 11:605170
Klimm, F., Toledo, E.M., Monfeuga, T., Zhang, F., Deane, C.M. & Reinert, G. (2020) Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks. BMC Bioinformatics, 21:756
Ghraichy, M., Galson, J.D., Kovaltsuk, A., von Niederhäusern, V., Schmid, J.M., Miho, E., Kelly, D.F., Deane, C.M. & Trück, J. (2020) Maturation of Naïve and Antigen-experienced B-cell Receptor Repertoires with Age Frontiers in Immunology, 11:1734
Outeiral, C., Strahm, M., Shi, J., Morris, G.M., Benjamin, S.C. & Deane, C.M. (2020) The prospects of quantum computing in computational molecular biology WIRES, 11(1):e1481
Marks, C. & Deane, C.M. (2020) How repertoire data is changing antibody science Journal of Biological Chemistry, 295:9823-9837
Wong, W.K., Marks, C., Leem, J., Lewis, A.P., Shi, J. & Deane, C.M. (2020) TCRBuilder: Multi-state T-cell receptor structure prediction Bioinformatics, 36(11):3580-3581
Scantlebury, J., Brown, N., Von Delft, F. & Deane, C.M. (2020) Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalise To Unseen Target Classes, And Highlight Important Binding Interactions. Journal of Chemical Information Modeling, 60(8):3722-3730
Chan, L., Hutchison, G.R. & Morris, G.M. (2020) BOKEI: Bayesian optimization using knowledge of correlated torsions and expected improvement for conformer generation Physical Chemistry Chemical Physics, 22(9):5211-5219
Imrie, F., Bradley, A.R., van der Schaar, M. & Deane, C.M. (2020) Deep Generative Models for 3D Linker Design Journal of Chemical Information Modeling, 60(4):1983-1995
Kovaltsuk, A., Raybould, M.I.J., Wong, W.K., Marks, C., Kelm, S., Snowden, J., Trück, J. & Deane, C.M. (2020) Structural Diversity of B-cell Receptor Repertoires along the B-cell Differentiation Axis in Humans and Mice PLoS Computational Biology, 16(2):e1007636
Raybould, M.I.J., Marks, C., Lewis, A.P., Shi, J., Bujotzek, A., Taddese, B. & Deane, C.M. (2020) Thera-SAbDab: the Therapeutic Structural Antibody Database Nucleic Acids Research, 48(D1):D383-D388
Knapp, B., van der Merwe, P.A., Dushek, O. & Deane, C.M. (2019) MHC binding affects the dynamics of different T-cell receptors in different ways PLoS Computational Biology, 15:1-17
Wong, W.K., Leem, J. & Deane, C.M. (2019) Comparative analysis of the CDR loops of antigen receptors Frontiers in Immunology, 10:2454
Ebejer, J.P., Finn, P.W., Wong, W.K., Deane, C.M. & Morris, G.M. (2019) Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening Journal of Chemical Information and Modeling, 59(6):2600-2616
Chan, L., Hutchison, G.R. & Morris, G.M. (2019) Bayesian Optimization for Conformer Generation Journal of Cheminformatics, 11:32
Raybould, M.I.J., Marks, C., Krawczyk, K., Taddese, B., Nowak, J., Lewis, A.P., Bujotzek, A., Shi, J. & Deane, C.M. (2019) Five Computational Developability Guidelines for Therapeutic Antibody Profiling Proceedings of the National Academy of Sciences USA, 116(10):4025-4030
Chonofsky, M., de Oliveira, S.H.P., Krawczyk, K. & Deane, C.M. (2019) The evolution of contact prediction: Evidence that contact selection in statistical contact prediction is changing Bioinformatics, ():btz816
West, C.E., de Oliveira, S.H.P. & Deane, C.M. (2019) RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold PLoS One, 14(10):1-16
Bozhilova, L.V., Whitmore, A.V., Wray, J., Reinert, G. & Deane, C.M. (2019) Measuring rank robustness in scored protein interaction networks BMC Bioinformatics, 20:446
Boyles, F., Deane, C.M. & Morris, G.M. (2019) Learning From The Ligand: Using Ligand-Based Features To Improve Binding Affinity Prediction Bioinformatics, 36(3):758-764
Schwarz, D., Merget, B., Deane, C.M. & Fulle, S. (2019) Modeling conformational flexibility of kinases in inactive states Proteins, 87(11):943-951
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