Publications
A full list of the group's publications can be found below. You can also download this information as a bibtex file here.
Pre-prints
Ziv, Y., Buttenschoen, M., Schieblerger, L., Marsden, B. & Deane, C. M. Interpolation-Based Conditioning of Flow Matching Models for Bioisosteric Ligand Design. bioRxiv https://doi.org/10.1101/2025.10.20.683377 doi:10.1101/2025.10.20.683377.
Capel, H. L., Wang, E. J. D., Williams, B. H., Deane, C. M. & Raybould, M. I. J. Pox-AbDab: the Orthopoxvirus Antibody Database. bioRxiv https://doi.org/10.1101/2025.08.22.671799 doi:10.1101/2025.08.22.671799.
Capel, H. L. et al. LICHEN: Light-chain Immunoglobulin sequence generation Conditioned on the Heavy chain and Experimental Needs. bioRxiv https://doi.org/10.1101/2025.08.06.668938 doi:10.1101/2025.08.06.668938.
Branson, N. & Deane, C. M. AntiDIF: Accurate and Diverse Antibody Specific Inverse Folding with Discrete Diffusion. bioRxiv https://doi.org/10.1101/2025.07.12.664553 doi:10.1101/2025.07.12.664553.
Cagiada, M., Thomasen, F. E., Ovchinnikov, S., Deane, C. M. & Lindorff-Larsen, K. AF2χ: Predicting protein side-chain rotamer distributions with AlphaFold2. bioRxiv https://doi.org/10.1101/2025.04.16.649219 doi:10.1101/2025.04.16.649219.
Runcie, N. T., Deane, C. M. & Imrie, F. Assessing the Chemical Intelligence of Large Language Models. Rxiv https://doi.org/10.48550/arXiv.2505.07735 doi:10.48550/arXiv.2505.07735.
Spoendlin, F. C. et al. Predicting the conformational flexibility of antibody and T-cell receptor CDRs. bioRxiv https://doi.org/10.1101/2025.03.19.644119 doi:10.1101/2025.03.19.644119.
Mokaya, M., Deane, C. M. & Bradley, A. R. Expanding the Synthome: Generating and integrating novel reactions into retrosynthesis tools. bioRxiv https://doi.org/10.1101/2025.03.07.642029 doi:10.1101/2025.03.07.642029.
Sanchez-Garcia, R. et al. Supervised Deep Learning for Efficient Cryo-EM Image Alignment in Drug Discovery with cryoPARES. bioRxiv https://doi.org/10.1101/2025.03.04.641536 doi:10.1101/2025.03.04.641536.
Grosjean, H. et al. Structure-Activity-Relationships can be directly extracted from high-throughput crystallographic evaluation of fragment elaborations in crude reaction mixtures. ChemRxiv https://doi.org/10.26434/chemrxiv-2025-bg2ll doi:10.26434/chemrxiv-2025-bg2ll.
Greenshields-Watson, A. et al. ANARCII: A Generalised Language Model for Antigen Receptor Numbering. bioRxiv https://doi.org/10.1101/2025.04.16.648720 doi:10.1101/2025.04.16.648720.
Gordon, G. L., Gervasio, J., Souders, C. & Deane, C. M. The Therapeutic Nanobody Profiler: characterising and predicting nanobody developability to improve therapeutic design. bioRxiv https://doi.org/10.1101/2025.08.11.669635 doi:10.1101/2025.08.11.669635.
Fieseler, K. K. et al. Syndirella: Synthesis-directed fragment elaboration enables extensive binding site exploration beyond catalog compounds. ChemRxiv https://doi.org/10.26434/chemrxiv-2025-0jd39 doi:10.26434/chemrxiv-2025-0jd39.
Wills, S. et al. Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach. bioRxiv https://doi.org/10.1101/2024.08.02.606367 doi:10.1101/2024.08.02.606367.
Crook, O. M., Gittens, N., Chung, C. & Deane, C. M. Inferring residue level hydrogen deuterium exchange with ReX. bioRxiv https://doi.org/10.1101/2024.04.12.589190 doi:10.1101/2024.04.12.589190.
Chinery, L. et al. Baselining the Buzz Trastuzumab-HER2 Affinity, and Beyond. bioRxiv https://doi.org/10.1101/2024.03.26.586756 doi:10.1101/2024.03.26.586756.
Homberg, S., Janosch, M., Morris, G. M. & Koch, O. Interpreting Graph Neural Networks with Myerson Values for Cheminformatics Approaches. ChemRxiv https://doi.org/10.26434/chemrxiv-2023-1hxxc-v2 doi:10.26434/chemrxiv-2023-1hxxc-v2.
Moesser, M. A. et al. Protein-Ligand Interaction Graphs: Learning from Ligand-Shaped 3D Interaction Graphs to Improve Binding Affinity Prediction. bioRxiv https://doi.org/10.1101/2022.03.04.483012 doi:10.1101/2022.03.04.483012.
Leung, S., Bodkin, M., von Delft, F., Brennan, P. & Morris, G. M. SuCOS is Better than RMSD for Evaluating Fragment Elaboration and Docking Poses. ChemRxiv https://doi.org/10.26434/chemrxiv.8100203.v1 doi:10.26434/chemrxiv.8100203.v1.
Peer-Reviewed
1.
Ziv, Y., Imrie, F., Marsden, B. & Deane, C. MolSnapper: Conditioning Diffusion for Structure Based Drug Design. Journal of Chemical Information and Modeling https://doi.org/10.1021/acs.jcim.4c02008 (2025) doi:10.1021/acs.jcim.4c02008.
2.
Vost, L., Ziv, Y. & Deane, C. M. Incorporating targeted protein structure in deep learning methods for molecule generation in computational drug design. Chemical Science D5SC05748E (2025) doi:10.1039/D5SC05748E.
3.
Vost, L., Chenthamarakshan, V., Das, P. & Deane, C. M. Improving Structural Plausibility in 3D Molecule Generation via Property-Conditioned Training with Distorted Molecules. Digital Discovery 10.1039/D4DD00331D (2025) doi:10.1039/D4DD00331D.
4.
Valsson, I. et al. Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data. Communications Chemistry 8, 41 (2025).
5.
Quast, N. P., Deane, C. M. & Raybould, M. I. J. STCRpy: a software suite for T cell receptor structure parsing, interaction profiling and machine learning dataset preparation. Bioinformatics btaf566 (2025) doi:10.1093/bioinformatics/btaf566.
6.
Quast, N. P. et al. T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity. Communications Biology 8, 362 (2025).
7.
Hummer, A. M., Schneider, C., Chinery, L. & Deane, C. M. Investigating the Volume and Diversity of Data Needed for Generalizable Antibody-Antigen ∆∆G Prediction. Nature Computational Science https://doi.org/10.1038/s43588-025-00823-8 (2025) doi:10.1038/s43588-025-00823-8.
8.
Huhn, A. et al. The molecular reach of antibodies determines their SARS-CoV-2 neutralisation potency. Nature Communications 16, 338 (2025).
9.
Høie, M. H. et al. AntiFold: Improved antibody structure-based design using inverse folding. Bioinformatics Advances vbae202 (2025) doi:10.1093/bioadv/vbae202.
10.
Greenshields-Watson, A., Vavourakis, O., Spoendlin, F. C., Cagiada, M. & Deane, C. M. Challenges and compromises: Predicting unbound antibody structures with deep learning. Current Opinion in Structural Biology 90, 102983 (2025).
11.
Gervasio, J. D. et al. HeavyBuilder: Analysis of High-Throughput of Antibody Heavy Chain Repertoires in the Structural Space. Journal of Molecular Biology 169509 (2025) doi:10.1016/j.jmb.2025.169509.
12.
Ferla, M. P. et al. Fragmenstein: predicting protein-ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology. Journal of Cheminformatics 17, 4 (2025).
13.
Ellmen, I., Schneider, C., Raybould, M. I. J. & Deane, C. M. Transformers trained on proteins can learn to attend to Euclidean distance. Transactions on Machine Learning Research https://openreview.net/forum?id=mU59bDyqqv (2025).
14.
Ellmen, I., Raybould, M. I. J. & Deane, C. M. The protein universe in 3D. Nature Chemical Biology 21, 27–28 (2025).
15.
Durant, G., Boyles, F., Birchall, K., Marsden, B. & Deane, C. M. Robustly interrogating machine learning-based scoring functions: what are they learning? Bioinformatics btaf040 (2025) doi:10.1093/bioinformatics/btaf040.
16.
Brennan, P. J. et al. Orthogonal IMiD-Degron Pairs Induce Selective Protein Degradation in Cells. ACS Chemical Biology 5c00751 (2025) doi:10.1021/acschembio.5c00751.
17.
Turnbull, O. M., Oglic, D., Croasdale-Wood, R. & Deane, C. M. p-IgGen: A Paired Antibody Generative Language Model. Bioinformatics 40, btae659 (2024).
18.
Theorell, J. et al. Ultrahigh frequencies of peripherally matured LGI1 & CASPR2-reactive B cells characterise encephalitis patient cerebrospinal fluid. Proceedings of the National Academy of Sciences USA 121, e2311049121 (2024).
19.
Sanchez-Garcia, R., Saur, M., Vargas, J., Poelking, C. & Deane, C. M. CESPED: a new benchmark for supervised particle pose estimation in Cryo-EM. Physical Review Research 8, 023245 (2024).
20.
Richardson, E. et al. Computational mining of B cell receptor repertoires reveals antigen-specific and convergent responses to Ebola vaccination. Frontiers in Immunology 15, 1383753 (2024).
21.
Riccabona, J. R. et al. Assessing AF2’s ability to predict structural ensembles of proteins. Structure 32, 2147–2159 (2024).
22.
Raybould, M. I. J., Turnbull, O. M., Suter, A., Guloglu, B. & Deane, C. M. Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody profiling. Communications Biology 7, 62 (2024).
23.
Raybould, M. I. J. et al. The Observed T cell receptor Space database enables paired-chain repertoire mining, coherence analysis and language modelling. Cell Reports 43, 114704 (2024).
24.
Outeiral, C. & Deane, C. Codon language embeddings provide strong signals for protein engineering. Nature Machine Intelligence 6, 170–179 (2024).
25.
Olsen, T. H., Moal, I. H. & Deane, C. M. Addressing the antibody germline bias and its effect on language models for improved antibody design. Bioinformatics 40, btae618 (2024).
26.
McMaster, B., Thorpe, C., Rossjohn, J., Deane, C. M. & Koohy, H. Quantifying conformational changes in the TCR:pMHC-I binding interface. Frontiers in Immunology 15, 1491656 (2024).
27.
McMaster, B., Thorpe, C., Ogg, G., Deane, C. M. & Koohy, H. Can AlphaFold’s breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity? Nature Methods 21, 766–776 (2024).
28.
Klarner, L., Rudner, T. G. J., Morris, G. M., Deane, C. M. & Teh, Y. W. Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design. Proceedings of the 41st International Conference on Machine Learning (ICML 2024) https://arxiv.org/abs/2407.11942 (2024).
29.
Jiang, Y. et al. Comprehensive Overview of Bottom-up Proteomics using Mass Spectrometry. ACS Measurement Science Au 4, 338–417 (2024).
30.
Jiang, Y., Deane, C. M., Morris, G. M. & O’Brien, E. P. It is theoretically possible to avoid misfolding into non-covalent lasso entanglements using small molecule drugs. PLOS Computational Biology 20, 1–22 (2024).
31.
Hummer, A. M. & Deane, C. M. Designing stable humanized antibodies. Nature Biochemical Engineering 8, 3–4 (2024).
32.
Greenshields-Watson, A., Abanades, B. & Deane, C. M. 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 (2024).
33.
Gordon, G. L., Raybould, M. I. J., Wong, A. & Deane, C. M. Prospects for the computational humanization of antibodies and nanobodies. Frontiers in Immunology 15, 1399438 (2024).
34.
Gordon, G. L. et al. PLAbDab-nano: a database of camelid and shark nanobodies from patents and literature. Nucleic Acids Research 53, gkae881 (2024).
35.
Fischer, K. et al. Microfluidics-enabled fluorescence-activated cell sorting of single pathogen-specific antibody secreting cells for the rapid discovery of monoclonal antibodies. Nature Biotechnology 10.1038/s41587-024-02346–5 (2024) doi:10.1038/s41587-024-02346-5.
36.
Erasmus, M. F. et al. AIntibody: An experimentally-validated in silico antibody discovery design challenge. Nature Biotechnology 42, 1637–1642 (2024).
37.
Éliás, S. et al. Prediction of polyspecificity from antibody sequence data by machine learning. Frontiers in Bioinformatics 3, 1286883 (2024).
38.
Durant, G., Boyles, F., Birchall, K. & Deane, C. M. The future of machine learning for small-molecule drug discovery will be driven by data. Nature Computational Science 4, 735–743 (2024).
39.
Condado-Morales, I. et al. A comparative study of the developability of full-length antibodies, fragments, and bispecific formats reveals higher stability risks for engineered constructs. mAbs 16, 2403156 (2024).
40.
Chinery, L., Jeliazkov, J. R. & Deane, C. M. Humatch - fast, gene-specific joint humanisation of antibody heavy and light chains. mAbs 16, 2434121 (2024).
41.
Carbery, A., Buttenschoen, M., Skyner, R., von Delft, F. & Deane, C. M. 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, 32 (2024).
42.
Abanades, B. et al. The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures. Nucleic Acids Research 52, D545–D551 (2024).
43.
Wills, S. et al. Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search. Journal of Chemical Information and Modeling 63, 3423–3437 (2023).
44.
Villanueva, E. et al. System-wide analysis of RNA and protein subcellular localization dynamics. Nature Methods 21, 60–71 (2023).
45.
Vales, S. et al. Discovery and pharmacophoric characterization of chemokine network inhibitors using phage-display, saturation mutagenesis and computational modelling. Nature Communications 14, 5763 (2023).
46.
Spoendlin, F. C. et al. 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 (2023).
47.
Scantlebury, J. et al. A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening. Journal of Chemical Information and Modeling 63, 2960–2974 (2023).
48.
Richardson, E. et al. Characterisation of the immune repertoire of a humanised transgenic mouse through immunophenotyping and high-throughput sequencing. eLife 12, e81629 (2023).
49.
Raybould, M. I. J., Nissley, D. A., Kumar, S. & Deane, C. M. Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies. Frontiers in Immunology 13, 1080596 (2023).
50.
Olsen, T. H., Abanades, B., Moal, I. H. & Deane, C. M. KA-Search, a method for rapid and exhaustive sequence identity search of known antibodies. Scientific Reports 13, 11612 (2023).
51.
Mokaya, M. et al. Testing the Limits of SMILES-based De Novo Molecular Generation with Curriculum and Deep Reinforcement Learning. Nature Machine Intelligence 5, 386–394 (2023).
52.
Klarner, L. et al. 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 (2023).
53.
Hoerschinger, V. J. et al. PEP-Patch: Electrostatics in Protein–Protein Recognition, Specificity, and Antibody Developability. Journal of Chemical Information and Modelling 63, 6964–6971 (2023).
54.
Hadfield, T. E., Scantlebury, J. & Deane, C. M. Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding. Journal of Cheminformatics 15, 84 (2023).
55.
Guloglu, B. & Deane, C. M. Specific attributes of the VL domain influence both the structure and structural variability of CDR-H3 through steric effects. Frontiers in Immunology 14, 1223802 (2023).
56.
Gordon, G. L. et al. A comparison of the binding sites of antibodies and single-domain antibodies. Frontiers in Immunology 14, 1231623 (2023).
57.
Fernández-Quintero, M. L. et al. Challenges in antibody structure prediction. mAbs 15, 2175319 (2023).
58.
Dablander, M., Hanser, T., Lambiotte, R. & Morris, G. M. Exploring QSAR Models for Activity-Cliff Prediction. Journal of Cheminformatics 15, 47 (2023).
59.
Crook, O. M. et al. A linear transportation Lp distance for pattern recognition. Pattern Recognition 147, 110080 (2023).
60.
Crook, O. M., Chung, C. & Deane, C. M. A functional Bayesian model for hydrogen-deuterium exchange mass-spectrometry. Journal of Proteome Research 22, 2959–2972 (2023).
61.
Buttenschoen, M., Morris, G. M. & Deane, C. M. PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chemical Science 15, 3130–3139 (2023).
62.
Boby, M. L. et al. Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors. Science 382, abo7201 (2023).
63.
Abanades, B. et al. ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins. Communications Biology 6, 575 (2023).
64.
Wang, Y. et al. Peptide Centric Vβ Specific Germline Contacts Shape a Specialist T Cell Response. Frontiers in Immunology 13, 847092 (2022).
65.
Schneider, C., Raybould, M. I. J. & Deane, C. M. SAbDab in the age of biotherapeutics: updates including SAbDab-nano, the nanobody structure tracker. Nucleic Acids Research 50, D1368–D1372 (2022).
66.
Sanchez-Garcia, R. et al. CoPriNet: Deep learning compound price prediction for use in de novo molecule generation and prioritization. Digital Discovery 2, 103–111 (2022).
67.
Raybould, M. I. J. & Deane, C. M. The Therapeutic Antibody Profiler for Computational Developability Assessment. Methods in Molecular Biology 23, 115–125 (2022).
68.
Pardo-Diaz, J., Poole, P. S., Beguerisse-Díaz, M., Deane, C. M. & Reinert, G. Generating weighted and thresholded gene coexpression networks using signed distance correlation. Network Science 10, 131–145 (2022).
69.
Pardo-Diaz, J., Beguerisse-Diaz, M., Poole, P. S., Deane, C. M. & Reinert, G. Extracting Information from Gene Coexpression Networks of Rhizobium leguminosarum. Journal of Computational Biology 27, 752–768 (2022).
70.
Outeiral, C., Nissley, D. A. & Deane, C. M. Current structure predictors are not learning the physics of protein folding. Bioinformatics 38, 1881–1887 (2022).
71.
Olsen, T. H., Moal, I. H. & Deane, C. M. AbLang: An antibody language model for completing antibody sequences. Bioinformatics Advances 2, vbac046 (2022).
72.
Meli, R., Morris, G. M. & Biggin, P. C. Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review. Frontiers in Bioinformatics 2, 885983 (2022).
73.
Lomize, A. L. et al. Membranome 3.0: Database of single-pass membrane proteins with AlphaFold models. Protein Science 31, e4318 (2022).
74.
Ko, K.-T. et al. Structure of the malaria vaccine candidate Pfs48/45 and its recognition by transmission blocking antibodies. Nature Communications 13, 5603 (2022).
75.
Khetan, R. et al. Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics. mAbs 14, 2020082 (2022).
76.
Hummer, A. M., Abanades, B. & Deane, C. M. Advances in computational structure-based antibody design. Current Opinion in Structural Biology 74, 102379 (2022).
77.
Hadfield, T. E. & Deane, C. M. AI in 3D compound design. Current Opinion in Structural Biology 73, 102326 (2022).
78.
Hadfield, T. E., Imrie, F., Merritt, A., Birchall, K. & Deane, C. M. Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration. Journal of Chemical Information and Modeling 62, 2280–2292 (2022).
79.
Goto, A., Rodriguez-Esteban, R., Scharf, S. H. & Morris, G. M. Understanding the genetics of viral drug resistance by integrating clinical data and mining of the scientific literature. Scientific Reports 12, 14476 (2022).
80.
Crook, O. M., Chung, C. & Deane, C. M. Empirical Bayes functional models for hydrogen deuterium exchange mass spectrometry. Communications Biology 5, 588 (2022).
81.
Crook, O. M., Chung, C. & Deane, C. M. Challenges and Opportunities for Bayesian Statistics in Proteomics. Journal of Proteome Research 21, 849–864 (2022).
82.
Chinery, L., Wahome, N., Moal, I. & Deane, C. M. Paragraph - Antibody paratope prediction using Graph Neural Networks with minimal feature vectors. Bioinformatics 39, btac732 (2022).
83.
Carbery, A., Skyner, R., von Delft, F. & Deane, C. M. Fragment Libraries Designed to Be Functionally Diverse Recover Protein Binding Information More Efficiently Than Standard Structurally Diverse Libraries. Journal of Medicinal Chemistry 65, 11404–11413 (2022).
84.
Baddock, H. T. et al. 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, 1484–1500 (2022).
85.
Abanades, B., Georges, G., Bujotzek, A. & Deane, C. M. ABlooper: Fast accurate antibody CDR loop structure prediction with accuracy estimation. Bioinformatics 38, 1887–1880 (2022).
86.
Wong, W. K. et al. Ab-Ligity: Identifying sequence-dissimilar antibodies that bind to the same epitope. MAbs 13, 1873478 (2021).
87.
Schwarz, D. et al. Co-evolutionary distance predictions contain flexibility information. Bioinformatics 38, 65–72 (2021).
88.
Schneider, C., Buchanan, A., Taddese, B. & Deane, C. M. DLAB—Deep learning methods for structure-based virtual screening of antibodies. Bioinformatics 38, 377–383 (2021).
89.
Robinson, S. A. et al. Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies. PLoS Computational Biology 17, e1009675 (2021).
90.
Richardson, E. et al. A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-Pertussis toxoid antibodies. MAbs 13, 1869406 (2021).
91.
Raybould, M. I. J., Kovaltsuk, A., Marks, C. & Deane, C. M. CoV-AbDab: the Coronavirus Antibody Database. Bioinformatics 37, 734–735 (2021).
92.
Raybould, M. I. J., Rees, A. R. & Deane, C. M. Current strategies for detecting functional convergence across B-cell receptor repertoires. MAbs 13, 1996732 (2021).
93.
Raybould, M. I. J. et al. Public Baseline and Shared Response Structures Support the Theory of Antibody Repertoire Functional Commonality. PLoS Computational Biology 17, e1008781 (2021).
94.
Pardo-Diaz, J. et al. Robust gene coexpression networks using signed distance correlation. Bioinformatics 37, 1982–1989 (2021).
95.
Outeiral, C. et al. Investigating the potential for a limited quantum speedup on protein lattice problems. New Journal of Physics 23, 103030 (2021).
96.
Olsen, T. H., Boyles, F. & Deane, C. M. OAS: A diverse database of cleaned, annotated and translated unpaired and paired antibody sequences. Protein Science 31, 141–146 (2021).
97.
Nissley, D. A., Carbery, A., Chonofsky, M. & Deane, C. M. Ribosome occupancy profiles are conserved between structurally and evolutionarily related yeast domains. Bioinformatics 37, 1853–1859 (2021).
98.
Mokaya, M. & Deane, C. M. A virtual drug-screening approach to conquer huge chemical libraries. Nature 601, 322–323 (2021).
99.
Meli, R., Anighoro, A., Bodkin, M. J., Morris, G. M. & Biggin, P. C. Learning protein-ligand binding affinity with atomic environment vectors. Journal of Cheminformatics 13, 59 (2021).
100.
Marks, C., Hummer, A. M., Chin, M. & Deane, C. M. Humanization of antibodies using a machine learning approach on large-scale repertoire data. Bioinformatics 37, 4041–4047 (2021).
101.
Macpherson, A. et al. The allosteric modulation of Complement C5 by knob domain peptides. eLife 10, e63586 (2021).
102.
Klimm, F., Deane, C. M. & Reinert, G. Hypergraphs for predicting essential genes using multiprotein complex data. Journal of Complex Networks 9, cnaa028 (2021).
103.
Imrie, F., Hadfield, T. E., Bradley, A. R. & Deane, C. M. Deep generative design with 3D pharmacophoric constraints. Chemical Science 12, 14577–14589 (2021).
104.
Imrie, F., Bradley, A. R. & Deane, M., Charlotte. Generating Property-Matched Decoy Molecules Using Deep Learning. Bioinformatics 37, 2134–2141 (2021).
105.
Ghraichy, M. et al. Different B cell subpopulations show distinct patterns in their IgH repertoire metrics. eLife 10, e73111 (2021).
106.
Chan, L., Morris, G. M. & Hutchison, G. R. Understanding Conformational Entropy in Small Molecules. Journal of Chemical Theory and Computation 17, 2099–2106 (2021).
107.
Chan, L., Hutchison, G. & Morris, G. M. Understanding Ring Puckering in Small Molecules and Cyclic Peptides. Journal of Chemical Information and Modeling 61, 743–755 (2021).
108.
Chan, H. T. H. et al. Discovery of SARS-CoV-2 Mpro Peptide Inhibitors from Modelling Substrate and Ligand Binding. Chemical Science 12, 13686–13703 (2021).
109.
Boyles, F., Deane, C. M. & Morris, G. M. Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses. Journal of Chemical Information and Modeling 62, 5329–5341 (2021).
110.
Wong, W. K. et al. TCRBuilder: Multi-state T-cell receptor structure prediction. Bioinformatics 36, 3580–3581 (2020).
111.
Scantlebury, J., Brown, N., Von Delft, F. & Deane, C. M. 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, 3722–3730 (2020).
112.
Raybould, M. I. J. et al. Thera-SAbDab: the Therapeutic Structural Antibody Database. Nucleic Acids Research 48, D383–D388 (2020).
113.
Outeiral, C. et al. The prospects of quantum computing in computational molecular biology. WIRES 11, e1481 (2020).
114.
Marks, C. & Deane, C. M. How repertoire data is changing antibody science. Journal of Biological Chemistry 295, 9823–9837 (2020).
115.
Kovaltsuk, A. et al. Structural Diversity of B-cell Receptor Repertoires along the B-cell Differentiation Axis in Humans and Mice. PLoS Computational Biology 16, e1007636 (2020).
116.
Klimm, F. et al. Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks. BMC Bioinformatics 21, 756 (2020).
117.
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