3D 프린터의 활용이 높아짐에 따라 발생하는 화학물질에 대한 노출 빈도가 증가하고 있다. 그러나 3D 프린팅 발생

화학물질의 독성 및 유해성에 대한 연구는 미비하며, 분자 구조 데이터의 결측치로 인해 in silico 기법을 사용한 독성

예측 연구는 저조한 실정이다. 본 연구에서는 화학물질의 분자구조 정보를 나타내는 주요 분자표현자의 결측치를 보

간하여 3D 프린팅의 독성 및 유해성을 예측한 Data-centric QSAR 모델을 개발하였다. 먼저 MissForest 알고리즘을 사

용해 3D 프린팅으로 발생되는 유해물질의 분자표현자 결측치를 보완하였으며, 서로 다른 4가지 기계학습 모델(결정

트리, 랜덤포레스트, XGBoost, SVM)을 기반으로 Data-centric QSAR 모델을 개발하여 생물 농축 계수(Log BCF)와

옥탄올-공기분배계수(Log Koa), 분배계수(Log P)를 예측하였다. 또한, 설명 가능한 인공지능(XAI) 방법론 중 Tree-

SHAP (SHapley Additive exPlanations) 기법을 활용하여 Data-centric QSAR 모델의 신뢰성을 입증하였다. MissForest

알고리즘 기반 결측지 보간 기법은, 기존 분자구조 데이터에 비하여 약 2.5배 많은 분자구조 데이터를 확보할 수 있었

다. 이를 바탕으로 개발된 Data-centric QSAR 모델의 성능은 Log BCF, Log Koa와 Log P를 각각 73%, 76%, 92% 의

예측 성능으로 예측할 수 있었다. 마지막으로 Tree-SHAP 분석결과 개발된 Data-centric QSAR 모델은 각 독성치와 물

리적으로 상관성이 높은 분자표현자를 통하여 선택함을 설명할 수 있었고 독성 정보에 대한 높은 예측 성능을 확보할

수 있었다. 본 연구에서 개발한 방법론은 다른 프린팅 소재나 화학공정, 그리고 반도체/디스플레이 공정에서 발생 가

능한 오염물질의 독성 및 인체 위해성 평가에 활용될 수 있을 것으로 사료된다.

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated

with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is

insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular

structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach

was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors.

First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing

materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a

machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air

partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR

model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial

intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5

times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log

BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR

model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly

correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be

extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor

or display process.

3D printing, Data imputation, Explainable AI (XAI), Quantitative-structure activity relationship (QSAR), Data-centric AI, Computational toxicology

1. Kwon, K. M., Kim, H. G. and Moon, S. H., “The 4th Basic Plan
for Material and Parts Development,” Ministry of Trade, Industry
and Energy 16-28(2016).
2. Jang, Y. J. and Jeong, E. M.,, “Global Trends in the 4th Industrial
Revolution and Strategies for the Response of Korean
Industries,” Korea Institute for Industrial Economics & Trade
22-24(2017).
3. Park, S. H., “A Study on R&D Policy through 3D Printing
Industry Trend Analysis,” Science and Technology Policy 24(3),
93-104(2014).
4. An, K. C., “Trends and Implications of 3D Printing Industry in
the 4th Industrial Revolution,” Institute for Information & Communications
Technology Promotion 5-8(2018).
5. Zhou, Y., Kong, X., Chen, A. and Cao, S., “Investigation of Ultrafine
Particle Emissions of Desktop 3D Printers in the Clean
Room,” Procedia Eng., 121, 506-512(2015).
6. HUBS, Additive Manufacturing Trend Report 2021 (2021).
7. Stabile, L., Scungio, M., Buonanno, G., Arpino, F. and Ficco,
G., “Airborne Particle Emission of a Commercial 3D Printer: the
Effect of Filament Material and Printing Temperature,” Indoor
Air, 27(2), 398-408(2017).
8. Kim, Y. N., Yoon, C. S., Ham, S. H., Park, J. H., Kim, S. H.,
Kwon, O. H. and Tsai, P. J., “Emissions of Nanoparticles and
Gaseous Material from 3D Printer Operation,” Environ Sci Technol.,
49(20), 12044-12053(2015).
9. Azimi, P., Zhao, D., Pouzet, C., Crain, N. E. and Stephens B.,
“Emissions of Ultrafine Particles and Volatile Organic Compounds
from Commercially Available Desktop Three-Dimensional
Printers with Multiple Filaments,” Environ Sci Technol., 50(3),
1260-1268(2016).
10. Steinle, P., “Characterization of Emissions from a Desktop 3D
Printer and Indoor Air Measurements in Office Settings,” J.
Occup. Environ. Hyg., 13(2), 121-132(2016).
11. Kim G. H., Lyu K. G., Kim Y. J. and Kim H. C., “A Survey on
Quantitative Structure-Activity Relationship(QSAR) Models,”
2008 Proceedings of the Korean Information Science Society
Conference, July, Pyungchang 35(1), 43-44(2008).
12. Ding Y. L., Lyu Y. C. and Leong M. K., “In Silico Prediction of
the Mutagenicity of Nitroaromatic Compounds Using a Novel
Two-QSAR Approach,” Toxicology in Vitro 40(1), 102-114(2017).
13. Kobayashi, Y. and Yoshida, K., “Development of QSAR Models
for Prediction of Fish Bioconcentration Factors Using Physicochemical
Properties and Molecular Descriptors with Machine
Learning Algorithms,” Ecol Inform 63(1), 2-9(2021).
14. Pandit, S., Singh, P., Sinha, M. and Parthasarathi, R., “Integrated
QSAR and Adverse Outcome Pathway Analysis of Chemicals
Released on 3D Printing Using Acrylonitrile Butadiene Styrene,”
Chem Res Toxicol 34(2), 355-364(2021).
15. Kim D. W., Lee S. C., Kim M. J., Lee E. J. and Yoo C. K.,
“Development of QSAR Model Based on the Key Molecular
Descriptors Selection and Computational Toxicology for Prediction
of Toxicity of PCBs,” Korean Chemical Engineering Research
54(5), 621-629(2016).
16. To, K. T., Fry, R. C. and Reif, D. M., “Characterizing the Effects
of Missing Data and Evaluating Imputation Methods for Chemical
Prioritization Applications Using ToxPi,” BioData Min 11(1),
(2018).
17. Lee J. G., Shin G. J., Park C. Y. and Hwang U. J., "Robust, fair
and scalable data-driven continuous learning,” Communications
of the Korean Institute of Information Scientists and Engineers
40(11), 53-58(2022).
18. Yang, F., Du, J., Lang, J., Lu, W., Liu, L., Jin, C. and Kang, Q.,
“Missing Value Estimation Methods Research for Arrhythmia
Classification Using the Modified Kernel Difference-Weighted
KNN Algorithms,” Biomed Res Int 2020(1), 1-9(2020).
19. Luo, Y., “Evaluating the State of the Art in Missing Data Imputation
for Clinical Data,” Brief Bioinform 23(1), 1-9(2022).
20. Carli, M., Ward, M. H., Metayer, C. and Wheeler, D. C., “Imputation
of Below Detection Limit Missing Data in Chemical Mixture
Analysis with Bayesian Group Index Regression,” Int. J.
Environ Res. Public Health 19(3), 2-14(2022).
21. Jeong, J. S., Garcia-Reyero, N., Burgoon, L., Perkins, E., Park,
T. H., Kim, C. H., Roh, J. Y. and Choi, J. H., “Development of
Adverse Outcome Pathway for PPARγ Antagonism Leading to
Pulmonary Fibrosis and Chemical Selection for Its Validation:
ToxCast Database and a Deep Learning Artificial Neural Network
Model-Based Approach,” Chem Res Toxicol 32(6), 1212-
1222(2019).
22. Tiganis, B. E., Burn, L. S., Davis, P. and Hill, A. J., “Thermal
Degradation of Acrylonitrile-butadiene-styrene (ABS) Blends,”
Polym Degrad Stab 76(1), 425-434(2002).
23. Rutkowski, J. V. and Levin, B. C., “Acrylonitrile-Butadiene-Styrene
Copolymers (ABS): Pyrolysis and Combustion Products
and their Toxicity-A Review of the Literature,” Fire Mater 10(1),
93-105(1986).
24. Wojtyła, S., Klama, P. and Baran, T., “Is 3D Printing Safe? Analysis
of the Thermal Treatment of Thermoplastics: ABS, PLA,
PET, and Nylon,” J. Occup. Environ. Hyg., 14(6), 80-85(2017).
25. Davis, A. Y., Zhang, Q., Wong, J. P. S., Weber, R. J. and Black,
M. S., “Characterization of Volatile Organic Compound Emissions
from Consumer Level Material Extrusion 3D Printers,”
Build Environ 160, 106209(2019).
26. Pandit, S., Singh, P., Sinha, M. and Parthasarathi, R., “Integrated
QSAR and Adverse Outcome Pathway Analysis of Chemicals
Released on 3D Printing Using Acrylonitrile Butadiene Styrene,”
Chem Res Toxicol 34(2), 355-364(2021).
27. Park, J. H., Jeon, H. J., Oh, Y. S., Park, K. H. and Yoon, C. S.,
“Understanding Three-dimensional Printing Technology, Evaluation,
and Control of Hazardous Exposure Agents,” Journal of
Korean Society of Occupational and Environmental Hygiene
28(3), 241-256(2018).
28. Kim, S. H., Chung, E. K., Kim, S. D. and Kwon, J. W., “Assessment
of Emitted Volatile Organic Compounds, Metals and Characteristic
of Particle in Commercial 3D Printing Service
Workplace,” Original Article Journal of Korean Society of Occupational
and Environmental Hygiene 30(2), 153-162(2020).
29. Kim, S. H. and Chung, E. K., “A Study on the Types of Materials
and Hazardous Substances Used in 3D Printers,” Korea Occupational
Safety and Health Agency 10-13(2019).
30. Hong, M. K., Jo, J. H., Choi, B. K. and Kim, K. W., A Study on
the Application of OECD Toolbox in Chemical Information (2018).
31. Mauri, A., Srl, A., Consonni, V., Pavan, M. and Todeschini, R.,
DRAGON SOFTWARE: AN EASY APPROACH TO MOLECULAR
DESCRIPTOR CALCULATIONS (n.d.).
32. Rahimi, R., Keshavarz, M. H., and Akbarzadeh, A. R., “Prediction
of the Density of Energetic Materials on the Basis of their
Molecular Structures,” Central European Journal of Energetic
Materials 13(1), 73-101(2016).
33. Consonni, V., Todeschini, R. and Pavan, M., “Structure/response
Correlations and Similarity/diversity Analysis by GETAWAY Descriptors.
1. Theory of the Novel 3D Molecular Descriptors,” J. Chem.
Inf. Comput. Sci., 42(3), 682-692(2002).
34. Devinyak, O., Havrylyuk, D. and Lesyk, R., “3D-MoRSE Descriptors
Explained,” J. Mol. Graph. Model., 54, 194-203(2014).
35. Stekhoven, D. J. and Bühlmann, P., “Missforest-Non-parametric
Missing Value Imputation for Mixed-type Data,” Bioinformatics
28(1), 112-118(2012).
36. Marinov, D. and Karapetyan, D., “Hyperparameter Optimisation
with Early Termination of Poor Performers,” 2019 11th Computer
Science and Electronic Engineering (CEEC), Colchester, UK
160-163(2019).
37. Choi, G. C., Kim, W. J. and Koo, J. M., “Investigating the Performance
of Machine Learning Methods in Predicting Functional
Properties of the Hydrogenase Variants,” Biotechnology
and Bioprocess Engineering 28(1), 143-151(2023).
38. Moon, J. H., Park, S. W., Rho, S. M. and Hwang, E. J., “Interpretable
Short-Term Electrical Load Forecasting Scheme Using
Cubist,” Comput Intell Neurosci 2022(1), 2-19(2022).
39. Lundberg, S. M., Allen, P. G. and Lee, S. I., “A Unified Approach
to Interpreting Model Predictions,” 31st Conference on Neural
Information Processing Systems (NIPS 2017), December, California
1-10(2017).