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• W4378907565 startingPage "411" @default.
• W4378907565 abstract "Artificial intelligence (AI)-based predictive models are being used to foster a precision medicine approach to treat complex chronic diseases such as autoimmune and autoinflammatory disorders (AIIDs). In the past few years the first models of systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), and rheumatoid arthritis (RA) have been produced by molecular profiling of patients using omic technologies and integrating the data with AI. These advances have confirmed a complex pathophysiology involving multiple proinflammatory pathways and also provide evidence for shared molecular dysregulation across different AIIDs. I discuss how models are used to stratify patients, assess causality in pathophysiology, design drug candidates in silico, and predict drug efficacy in virtual patients. By relating individual patient characteristics to the predicted properties of millions of drug candidates, these models can improve the management of AIIDs through more personalized treatments." @default.
• W4378907565 created "2023-06-01" @default.
• W4378907565 creator A5035090767 @default.
• W4378907565 date "2023-07-01" @default.
• W4378907565 modified "2023-10-01" @default.
• W4378907565 title "Artificial intelligence-driven drug development against autoimmune diseases" @default.
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• W4378907565 doi "https://doi.org/10.1016/j.tips.2023.04.005" @default.
• W4378907565 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37268540" @default.
• W4378907565 hasPublicationYear "2023" @default.
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