Abstract

This talk will be given remotely on Google Meet -meet.google.com/swu-sycb-tkz

Abstract: The challenges in drug discovery, including high attrition rates in late development stage, are well documented. This has led to an increased interest and need for applying machine learning and artificial intelligence across the drug discovery pipeline from target identification to chemical lead selection and optimisation. It has also been demonstrated that drugs with human genetic validation data are more likely to succeed in the clinic. To address this, it is essential to unravel genetic networks to identify new or better targets for which the underlying mechanism is clear. Despite the significant advances in next generation sequencing technologies and evolving databases of patient cohorts, the sheer complexity of these datasets makes their integration and interrogation a daunting task. Through the development and application of cutting-edge computational approaches, such as artificial intelligence, machine learning and mathematical modelling, to pharmacogenomics and drug discovery, we identify novel therapeutic targets, biomarkers and drug repositioning opportunities. In this talk I will focus on (1) systematic integration and harmonisation of biomedical big data (2) multi-omic disease association study and (3) network theory-based analysis of targetable pathway which have significant potential to provide unprecedented insights into vital biological processes and the control hubs that underpin disease.