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

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.