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논문검색

기계 학습 지원을 위한 QSAR 모델 급성 독성 예측 정확도 향상에 관한 연구

원문정보

A Study on Improvement of Machine Learning-Assisted QSAR Model for the Prediction of Acute Toxicity

양유호, 김홍관, 김덕한, 천영우

피인용수 : 0(자료제공 : 네이버학술정보)

초록

영어

In this study, chemicals with acute toxicity experimental data were selected as research subjects, and compareed the model derived from statistical analysis and QSAR open-source programs. The physical and chemical properties, dynamic behaviors, and toxicological estimates of the chemicals were calculated using Mordred, a molecular descriptor calculation program based on RDKit. LD50 was set as the toxicity comparison target for each chemical, and independent variables or factors with similarity to independent variables were estimated from the molecular descriptors calculated through Mordred. Molecule descriptors composed of independent variables were compared to predictions from QSAR open-source models, A regression model was created with the selected molecule descriptors and compared with predictions from QSAR programs, confirming high accuracy for specific functional groups. The QSAR model created in this study considers the characteristics and experimental values of each chemical, and provides evidence for selecting variables when constructing toxicity data for machine learning applications.

목차

Abstract
1. 서론
2. 연구방법
2.1 연구절차
2.2 유해성 지표 선정
2.3 데이터 세트 설정
2.4 소프트웨어 선정
2.5 분석방법
2.6 회귀모델 성능 검증
3. 연구결과
3.1 변수 간 상관분석 결과
3.2 회귀분석 결과 및 회귀모델 생성
3.3 QSAR 기반 회귀모델 성능 검증
4. 고찰 및 결론
5. References

저자정보

  • 양유호 Yu-Ho Yang. 인하대학교 순환경제환경시스템-환경안전공학전공
  • 김홍관 Hong-Kwan Kim. 인하대학교 순환경제환경시스템-환경안전공학전공
  • 김덕한 Duk-Han Kim. 인하대학교 순환경제환경시스템-환경안전공학전공
  • 천영우 Young-Woo Chon. 인하대학교 순환경제환경시스템-환경안전공학전공

참고문헌

자료제공 : 네이버학술정보

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