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DTSTAMP:20260907T072645Z
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DTSTART:20230913T090000Z
DTEND:20230913T170000Z
DESCRIPTION:The rapid development of computational approaches has revolutio
 nised the field of drug discovery and repurposing. Here I will show a prac
 tical application of digital twins built by the synergistic combination of
  mathematical modelling of the cell and machine learning techniques to ide
 ntify potential drug candidates for repurposing and subsequently validate 
 their efficacy using real-world data. After discussing the inherent challe
 nges associated with traditional drug discovery methods and the need for a
 lternative approaches I will introduce the concept of digital twins\, virt
 ual replicas of real biological systems\, which leverage mathematical mode
 ls to capture the intricacies of cellular behaviour and response to drugs.
  By integrating machine learning algorithms\, these digital twins can be t
 rained on vast datasets to predict drug effectiveness and identify promisi
 ng candidates for repurposing. Moreover\, I will emphasise the importance 
 of validating these predictions with real-world data\, obtained from biome
 dical databases\, to ensure reliability and efficacy. The presentation aim
 s to showcase the power of integrating mathematical modelling\, digital tw
 ins\, and machine learning for drug repurposing\, paving the way for accel
 erated and cost-effective drug discovery processes.\n\n### About the speak
 er\n\nDr. Joaquín Dopazo is the director of the Computational Medicine Pl
 atform of Fundación Progreso y Salud. He holds a degree in Chemistry and 
 a PhD in Biological Sciences. He has previously worked in public centres l
 ike the CIPF (Valencia)\, CNIO (Madrid) and companies\, like GlaxoWellcome
  SA (Tres Cantos). His field of work is personalised medicine with an appr
 oach that includes systems biology\, mathematical modelling and artificial
  intelligence\, areas in which he has published more than 350 articles. 
LOCATION:\, 
SUMMARY:Digital twins for drug repurposing by integrating mathematical mode
 lling and machine learning: bridging the gap between in silico and real-wo
 rld data
URL;VALUE=URI:https://www.ebi.ac.uk/training/events/digital-twins-drug-repu
 rposing-integrating-mathematical-modeling-and-machine-learning-bridging-ga
 p
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