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Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants

M. Ertelt, R. Moretti, J. Meiler, and C. Schoeder. (June 2024)
DOI: 10.1101/2024.06.20.599843

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Identifying digenic disease genes via machine learning in the Undiagnosed Diseases NetworkS. Mukherjee, J. Cogan, J. Newman, J. Phillips, 3rd, R. Hamid, Undiagnosed Diseases Network, J. Meiler, and J. Capra. Am. J. Hum. Genet., 108 (10): 1946--1963 (October 2021)Application of machine learning approaches on quantitative structure activity relationshipsM. Butkiewicz, R. Mueller, D. Selic, E. Dawson, and J. Meiler. 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, (March 2009)BCL::Mol2Dâa robust atom environment descriptor for QSAR modeling and lead optimizationO. Vu, J. Mendenhall, D. Altarawy, and J. Meiler. Journal of Computer-Aided Molecular Design, 33 (5): 477â486 (April 2019)Benchmarking Ligand-Based Virtual High-Throughput Screening with the PubChem DatabaseM. Butkiewicz, E. Lowe, R. Mueller, J. Mendenhall, P. Teixeira, C. Weaver, and J. Meiler. Molecules, 18 (1): 735â756 (January 2013)Strandâloopâstrand motifs: Prediction of hairpins and diverging turns in proteinsM. Kuhn, J. Meiler, and D. Baker. Proteins: Structure, Function, and Bioinformatics, 54 (2): 282â288 (December 2003)A Novel Method for Guiding Protein-Ligand Docking with QSAR-Derived Pharmacophore MapsS. DeLuca, and J. Meiler. Biophysical Journal, 100 (3): 394aâ395a (February 2011)General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore MapsB. Brown, J. Mendenhall, A. Geanes, and J. Meiler. Journal of Chemical Information and Modeling, 61 (2): 603â620 (January 2021)Introduction to the BioChemical Library (BCL): An application-based open-source toolkit for integrated cheminformatics and machine learning in computer-aided drug discoveryB. Brown, O. Vu, A. Geanes, S. Kothiwale, M. Butkiewicz, E. Lowe, Jr, R. Mueller, R. Pape, J. Mendenhall, and J. Meiler. Front. Pharmacol., (February 2022)Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variantsM. Ertelt, R. Moretti, J. Meiler, and C. Schoeder. (June 2024)Predicting the functional impact of KCNQ1 variants with artificial neural networksS. Phul, G. Kuenze, C. Vanoye, C. Sanders, A. George, and J. Meiler. PLOS Computational Biology, 18 (4): e1010038 (April 2022)

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Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants

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Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants