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-omics 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. As an example of our research projects, I will present our data-driven computational analysis and simulation approaches that allowed us to identify drug repurposing opportunities for COVID-19. It is a timely research of general interest, which uses Computational Biology, Network-based algorithms and Artificial Neural Network to predict the repositioning of 200 already approved drugs against SARS-CoV-2. We are confident our approach has identified potential targets, since 20% of these drugs are currently in COVID-19 clinical trials. We present the mechanism of action of the 200 drugs and demonstrate the efficacy of two of these (Proguanil and Sulfasalazine) in cellular assays. This huge dataset of SARS-CoV-2 induced pathways, already approved drugs to target them, along with their mechanism of action, defines a resource for repurposing of drugs against COVID-19, either in monotherapies or in combination therapy.

한남식 교수는 케임브리지대학교 의과대학 밀너연구소에서 인공지능연구센터를 이끌고 있다. 인공지능 및 머신러닝 기법과 같은 최신 컴퓨터공학 기법들을 활용하여 대용량 바이오메디컬데이터들을 분석함으로써 신약물질들을 발굴하는 연구를 활발히 진행하고 있다. 연구과제들은 다국적 제약회사들과의 공동연구를 통하여 진행하고 있다. 아울러 동대학 수학과에서 기계학습 및 신약개발연구 관련 강의를 담당하고 있으며 대학원생들 연구프로젝트를 지도를 하고 있다. 학계 활동 외에는 다수의 기업체 과학자문역을 수행 중이며, 특히 인공지능 신약개발 스타트업들로 심혈관계질환 관련한 CardiaTec Bioscience과 면역항암제 관련한 KURE.AI 을 공동창업하였다.