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  • 02월 22일 16시 이후 : 초록수정 불가능, 일정확인 및 검색만 가능

Prediction of GPCR Agonists and Antagonists using Bayesian Modeling

등록일
2008년 2월 14일 11시 12분 35초
접수번호
1294
발표코드
33P248포 이곳을 클릭하시면 발표코드에 대한 설명을 보실 수 있습니다.
발표시간
목 <발표Ⅱ>
발표형식
포스터
발표분야
의약화학
저자 및
공동저자
최인희, 강남숙1, 유성은1, 노경태
연세대학교 생명공학과, (사)분자설계연구소,
1한국화학연구원 생체기능조절물질개발사업단,
G-protein coupled receptors (GPCRs) are involved in a wide variety of physiological processes and are targets for nearly 50% of drugs. The various functions of GPCRs are affected by their cognate ligands which are mainly classified as agonists and antagonists. The purpose of this study is to develop in silico model that can distinguish human GPCR agonists from antagonists. Bayesian modeling was applied because it is an ideal way to rapidly analyze this data. This model distinguishes GPCR agonists (actives) from the GPCR antagonists (inactives) by using chemical fingerprint, FCFP_6. The compounds could be classified into distinctive structural characteristics: in general, 1) GPCR agonists were flexible and had aliphatic amines, and 2) GPCR antagonists had planar groups and aromatic amines. The Bayesian model reached more than 80% and 70% of accuracies for the training and test sets, respectively. The quality of our model suggests that it could aid to predict the compounds as either GPCR agonists or antagonists in the early stages of the drug discovery process.

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