서울대학교 생물정보학 협동과정 주최로 세미나를 아래와 같이 열고자 하오니 많은 참여 바랍니다.

일시:　2019년 9월 10일 화요일 오전 11시

연사: 김완규 교수님　(이화여자대학교 생명과학과)

장소:　서울대학교　300동(유회진 학술정보관) 611호

TITLE

Data-driven Drug Discovery

ABSTRACT

　　Data-driven drug discovery (D4) exploits a comprehensive set of ‘Big’ data in order to provide an efficient path to new drug development. First, we present an in silico chemical genomics approach to predict drug repositioning (DR) candidates for cancer precision medicine. It is based on a recent large-scale dataset of ~20,000 drug-induced expression profiles in multiple cancer cell lines, which provides i) a global impact of transcriptional perturbation of both known targets and unknown off-targets, and ii) rich information on drug’s mode-of-action. Potential utility of the large-scale drug-transcriptome dataset will be discussed.　

　　Second, we exploited more than a million bioassay dataset for drug screening. It provides, though not complete, extensive information on bioactivity profiles for millions of compounds. We developed a novel systems pharmacology approach to virtually screen active compounds for a target protein, named BEAR (Bioactive compound Enrichment by Assay Repositioning). The underlying idea of BEAR is to re-use bioassay data for predicting active compounds for a target other than their originally intended targets, i.e. ‘assay repositioning.’ Our method does not depend on structural information of either target or ligand, but uses only bioactivity data. BEAR is capable of predicting active compounds of novel scaffolds and applicable to thousands of targets.