논문 상세보기
pp.392-400
This study developed a QSAR regression model using the XGBoost machine learning algorithm to predict the acute aquatic toxicity of highly hazardous PCBs. EC50 values for Daphnia magna were obtained from QSAR Toolbox 4.7. Input features consisted of approximately 3,000 molecular descriptors and fingerprints generated from official structure data using RDKit and the Morgan algorithm, excluding mixtures. The dataset was split into training and test sets (7 : 3) based on 500,000 randomized seeds, and the most balanced combination was selected using Kolmogorov-Smirnov and Wilcoxon rank-sum tests. Z-score standardization was applied based on the training set, and the XGBoost model was trained using 5-fold cross-validation with grid search optimization. The final model showed excellent predictive performance (R2 =0.97, RMSE= 0.19). A simplified model using only the top 10 predictive molecular features retained approximately 95% of the original accuracy while improving interpretability and efficiency. The model was applied to 38 PCB compounds lacking EC50 values, and the predicted values showed a statistically similar distribution to the measured group, with only minor differences in a few structural fingerprints. These results demonstrate the applicability of XGBoost-based models for reliable toxicity prediction and offer a promising alternative approach for assessing the environmental risk of untested PCBs.
서 론
재료 및 방 법
1. 데이터 수집 및 전처리
2. 분자특성 (Descriptor) 산출 및 표준화
3. 데이터셋 분할 및 분포 검증
4. XGBoost 기반 EC50 예측모델 구축
5. 예측 기여도 상위 분자특성의 분포 비교 및통계적 차이 검정
6. 핵심 분자특성 기반 예측모델 (Feature Selection모델) 재학습
7. 미측정 화합물 (예측불가 그룹)에 대한 EC50 예측 및신뢰성 평가
결과 및 고 찰
1. 데이터셋 특성 및 분포
2. XGBoost 예측모델의 성능 평가
3. 분자특성 중요도 분석 결과
4. 핵심 분자특성 기반 예측모델 성능 비교
5. 예측불가 화합물의 구조적 특성 및 예측 결과 해석
결 론
REFERENCES
