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• W3197596721 abstract "Antibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time- and cost-intensive and heavily reliant on in vivo and in vitro high throughput screens. Here, we introduce a framework for structure-based deep learning for antibodies (DLAB) which can virtually screen putative binding antibodies against antigen targets of interest. DLAB is built to be able to predict antibody-antigen binding for antigens with no known antibody binders.We demonstrate that DLAB can be used both to improve antibody-antigen docking and structure-based virtual screening of antibody drug candidates. DLAB enables improved pose ranking for antibody docking experiments as well as selection of antibody-antigen pairings for which accurate poses are generated and correctly ranked. We also show that DLAB can identify binding antibodies against specific antigens in a case study. Our results demonstrate the promise of deep learning methods for structure-based virtual screening of antibodies.The DLAB source code and pre-trained models are available at https://github.com/oxpig/dlab-public.Supplementary data are available at Bioinformatics online." @default.
• W3197596721 created "2021-09-13" @default.
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• W3197596721 date "2021-09-21" @default.
• W3197596721 modified "2023-10-04" @default.
• W3197596721 title "DLAB: deep learning methods for structure-based virtual screening of antibodies" @default.
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• W3197596721 doi "https://doi.org/10.1093/bioinformatics/btab660" @default.
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