인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Prediction of a small molecule"s bioactivity is an essential research area that can improve the efficiency and probability of successful drug screening. In this research, we investigated the use of three various machine learning algorithms, XGBoost, CatBoost, and LightGBM, for predicting the bioactivity of compounds as inhibitors of acetylcholinesterase, a key target in the treatment of Alzheimer"s disease.
We used a dataset of 3,549 compounds with known activity against acetylcholinesterase as well as a set of molecular descriptors calculated for each compound. Our results showed that all three algorithms were able to achieve high accuracy in predicting the bioactivity of acetylcholinesterase inhibitors, with Specifically, CatBoost had an accuracy of 83%, a precision of 84%, a recall of 87%, and an F1 score of 86%. XgBoost and LightGBM also performed well, with accuracies of 81% and 83%, respectively.
We used a dataset of 3,549 compounds with known activity against acetylcholinesterase as well as a set of molecular descriptors calculated for each compound. Our results showed that all three algorithms were able to achieve high accuracy in predicting the bioactivity of acetylcholinesterase inhibitors, with Specifically, CatBoost had an accuracy of 83%, a precision of 84%, a recall of 87%, and an F1 score of 86%. XgBoost and LightGBM also performed well, with accuracies of 81% and 83%, respectively.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
