In silico proof of principle of machine learning-based antibody design at unconstrained scale
暂无分享,去创建一个
Cédric R. Weber | Ingrid Hobæk Haff | S. Hochreiter | R. Akbar | Philippe A. Robert | Milena Pavlović | Andrei Slabodkin | Lonneke Scheffer | Enkelejda Miho | F. Lund-Johansen | G. K. Sandve | V. Greiff | Michael Widrich | G. Klambauer | I. Snapkov | J. Andersen | R. Frank | Maria Chernigovskaya | Brij Bhushan Mehta
[1] Pedro M. Valero-Mora,et al. ggplot2: Elegant Graphics for Data Analysis , 2010 .
[2] Smita Raghava,et al. Predicting Antibody Developability Profiles Through Early Stage Discovery Screening , 2020, mAbs.
[3] R. Emerson,et al. Massively multiplexed affinity characterization of therapeutic antibodies against SARS-CoV-2 variants , 2021, bioRxiv.
[4] Low-N protein engineering with data-efficient deep learning. , 2021, Nature methods.
[5] Cédric R. Weber,et al. Learning the High-Dimensional Immunogenomic Features That Predict Public and Private Antibody Repertoires , 2017, The Journal of Immunology.
[6] Hsin-Jung Li,et al. Development of therapeutic antibodies for the treatment of diseases , 2020, Journal of Biomedical Science.
[7] Adam J. Riesselman,et al. Protein design and variant prediction using autoregressive generative models , 2019, Nature Communications.
[8] Jenna Kim,et al. The impact of imbalanced training data on machine learning for author name disambiguation , 2018, Scientometrics.
[9] Ingrid Hobæk Haff,et al. One billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction , 2021 .
[10] Daniel Neumeier,et al. Convergent selection in antibody repertoires is revealed by deep learning , 2020, bioRxiv.
[11] Mirko Omejc,et al. Drug Development: The Journey of a Medicine from Lab to Shelf , 2020 .
[12] S. Hochreiter,et al. On Failure Modes of Molecule Generators and Optimizers , 2020 .
[13] Graham W. Taylor,et al. Instance Selection for GANs , 2020, NeurIPS.
[14] A. Yermanos,et al. Applications of Machine and Deep Learning in Adaptive Immunity. , 2021, Annual review of chemical and biomolecular engineering.
[15] Rafał Kurczab,et al. The influence of the negative-positive ratio and screening database size on the performance of machine learning-based virtual screening , 2017, PloS one.
[16] G. A. Lazar,et al. Next generation antibody drugs: pursuit of the 'high-hanging fruit' , 2017, Nature Reviews Drug Discovery.
[17] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[18] Charlotte M. Deane,et al. Producing High-Accuracy Lattice Models from Protein Atomic Coordinates Including Side Chains , 2012, Adv. Bioinformatics.
[19] Cédric R. Weber,et al. High-throughput antibody engineering in mammalian cells by CRISPR/Cas9-mediated homology-directed mutagenesis , 2018, bioRxiv.
[20] Luis V. Santana-Quintero,et al. A new and updated resource for codon usage tables , 2017, BMC Bioinformatics.
[21] C. Deane,et al. Observed Antibody Space: A Resource for Data Mining Next-Generation Sequencing of Antibody Repertoires , 2018, The Journal of Immunology.
[22] Yanay Ofran,et al. Computational design of antibodies. , 2018, Current opinion in structural biology.
[23] Frank Grosveld,et al. A human monoclonal antibody blocking SARS-CoV-2 infection , 2020, Nature Communications.
[24] Geir Kjetil Sandve,et al. immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking , 2019, bioRxiv.
[25] S. Metsugi,et al. Antibody design using LSTM based deep generative model from phage display library for affinity maturation , 2021, Scientific Reports.
[26] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[27] Cédric R. Weber,et al. A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding. , 2021, Cell reports.
[28] Morten Nielsen,et al. Improved prediction of MHC II antigen presentation through integration and motif deconvolution of mass spectrometry MHC eluted ligand data. , 2020, Journal of proteome research.
[29] Kadina E. Johnston,et al. Protein sequence design with deep generative models , 2021, Current opinion in chemical biology.
[30] William S. DeWitt,et al. Deep generative models for T cell receptor protein sequences , 2019, eLife.
[31] Ya Chen,et al. Validation strategies for target prediction methods , 2019, Briefings Bioinform..
[32] Rahmad Akbar,et al. Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires , 2019, Molecular Systems Design & Engineering.
[33] Dan Jurafsky,et al. Utility Is in the Eye of the User: A Critique of NLP Leaderboard Design , 2020, EMNLP.
[34] A. H. Laustsen,et al. Animal Immunization, in Vitro Display Technologies, and Machine Learning for Antibody Discovery. , 2021, Trends in biotechnology.
[35] Viktor Seib,et al. Mixing Real and Synthetic Data to Enhance Neural Network Training - A Review of Current Approaches , 2020, ArXiv.
[36] Sepp Hochreiter,et al. Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery , 2018, J. Chem. Inf. Model..
[37] Protein design and variant prediction using autoregressive generative models , 2021, Nature communications.
[38] Alexander Yermanos,et al. immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking , 2020, Bioinformatics.
[39] Jordan Graves,et al. A Review of Deep Learning Methods for Antibodies , 2020, Antibodies.
[40] Bartek Wilczynski,et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics , 2009, Bioinform..
[41] R. Jernigan,et al. Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. , 1996, Journal of molecular biology.
[42] Friedrich Rippmann,et al. Interpretable Deep Learning in Drug Discovery , 2019, Explainable AI.
[43] Michael Meyer-Hermann,et al. A 3D structural affinity model for multi-epitope in silico germinal center simulations , 2019, bioRxiv.
[44] Srivamshi Pittala,et al. Learning Context-aware Structural Representations to Predict Antigen and Antibody Binding Interfaces. , 2020, Bioinformatics.
[45] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[46] Jeffrey J. Gray,et al. Deep Learning in Protein Structural Modeling and Design , 2020, Patterns.
[47] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[48] Anthony Gitter,et al. Neural networks to learn protein sequence–function relationships from deep mutational scanning data , 2020, Proceedings of the National Academy of Sciences.
[49] Andrew C. R. Martin,et al. AbDb: antibody structure database—a database of PDB-derived antibody structures , 2018, Database J. Biol. Databases Curation.
[50] Sebastian Kelm,et al. Computational approaches to therapeutic antibody design: established methods and emerging trends , 2019, Briefings Bioinform..
[51] Lindsay G. Cowell,et al. Mining adaptive immune receptor repertoires for biological and clinical information using machine learning , 2020 .
[52] Cynthia Liu,et al. Research and Development on Therapeutic Agents and Vaccines for COVID-19 and Related Human Coronavirus Diseases , 2020, ACS central science.
[53] Tileli Amimeur,et al. Designing Feature-Controlled Humanoid Antibody Discovery Libraries Using Generative Adversarial Networks , 2020, bioRxiv.
[54] Cédric R. Weber,et al. Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning , 2021, Nature Biomedical Engineering.
[55] A. Butté,et al. Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation , 2021, Trends in Pharmacological Sciences.
[56] Philippe A. Robert,et al. Induction of broadly neutralizing antibodies in Germinal Centre simulations. , 2018, Current opinion in biotechnology.
[57] Ian Kerman,et al. Predicting Antibody Developability from Sequence using Machine Learning , 2020, bioRxiv.
[58] Gisbert Schneider,et al. Drug discovery with explainable artificial intelligence , 2020, Nature Machine Intelligence.
[59] Ulrich Bodenhofer,et al. KeBABS: an R package for kernel-based analysis of biological sequences , 2015, Bioinform..
[60] Cédric R. Weber,et al. Systems Analysis Reveals High Genetic and Antigen-Driven Predetermination of Antibody Repertoires throughout B Cell Development. , 2017, Cell reports.
[61] Cédric R. Weber,et al. High-throughput antibody engineering in mammalian cells by CRISPR/Cas9-mediated homology-directed mutagenesis , 2018, bioRxiv.
[62] John F. Canny,et al. MSA Transformer , 2021, bioRxiv.
[63] W. Marasco,et al. The growth and potential of human antiviral monoclonal antibody therapeutics , 2007, Nature Biotechnology.
[64] Sai T. Reddy,et al. Immune Literacy: Reading, Writing, and Editing Adaptive Immunity , 2020, iScience.
[65] Christos A. Nicolaou,et al. Molecular property prediction: recent trends in the era of artificial intelligence. , 2019, Drug discovery today. Technologies.
[66] Namrata Anand,et al. Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation , 2020, bioRxiv.
[67] Geir Kjetil Sandve,et al. Modern Hopfield Networks and Attention for Immune Repertoire Classification , 2020, bioRxiv.
[68] P. Vermeesch,et al. An R package for statistical provenance analysis , 2016 .
[69] Jiye Shi,et al. Five computational developability guidelines for therapeutic antibody profiling , 2019, Proceedings of the National Academy of Sciences.
[70] Thomas Mensink,et al. Factors of Influence for Transfer Learning Across Diverse Appearance Domains and Task Types , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[71] Jeffrey J. Gray,et al. Antibody structure prediction using interpretable deep learning , 2021, bioRxiv.
[72] Chris Bailey-Kellogg,et al. Learning Context-aware Structural Representations to Predict Antigen and Antibody Binding Interfaces , 2019, bioRxiv.