Vol. 29 No. 3 (2026) Cover Image
Vol. 29 No. 3 (2026)

Published: September 20, 2026

Pages: 566-580

Articles

Integrated 3D Molecular Visualization and Property Prediction Using SMILES-Based Cheminformatics and Machine Learning

Marni Jacinth Raj 1 E. Bijolin Edwin 1 M. Roshni Thanka 1 V. Ebenezer 1 S. Stewart Kirubakaran 1 Raj Thilak 1
1 Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore-India.
History
  • Received: February 20, 2026
  • Revised: May 1, 2026
  • Accepted: June 2, 2026
  • Available Online: September 20, 2026
DOI

https://doi.org/10.29194/NJES.29030566

Abstract

The expansion of chemical and pharmaceutical data increases the need for enhanced computational systems that can interpret molecule structures and forecast chemical properties. Due to the fact that molecular visualization or prediction of chemical properties are presently performed separately during the chemical research and initial drug development processes, chemical research and the initial drug development process are not as efficient as possible. As such, the research presents a fully integrated model that predicts chemical properties with a single input method (SMILES strings) for both 2D and 3D representations of molecular structure, while at the same time utilizing SMILES strings to reconstruct the molecule and extract the features from the molecule. The research develops physicochemical descriptors and circular (Morgan) fingerprints which scientists use to create features for training ensemble-based machine learning models that predict essential molecular properties. The framework enables users to create interactive two-dimensional and three-dimensional molecular visualizations which show the molecular structure in an intuitive way while displaying the results of quantitative prediction methods. The proposed method enhances usability and analytical efficiency through its unified visualization system for prediction and interpretation which outperforms current standalone tools. The Python prediction and visualization pipeline with MATLAB verification tool enables independent assessment of three-dimensional molecular geometries to confirm spatial structure representation and conformer generation while maintaining model training and inference integrity. The framework enables organizations to incorporate new datasets and molecular descriptors and learning algorithms through its modular scalable and extensible design which demonstrates the successful application of cheminformatics-based representations and machine learning to drive data-based molecular research and decision-making in chemical and pharmaceutical fields.

Keywords

References

  1. Pang, C., Tong, H. H., & Wei, L. (2023). Advanced deep learning methods for molecular property prediction. Quantitative Biology, 11(4), 395-404. https://doi.org/10.1002/qub2.23
  2. Zeng, X., Xiang, H., Yu, L., Wang, J., Li, K., Nussinov, R., & Cheng, F. (2022). Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework. Nature Machine Intelligence, 4(11), 1004-1016. https://doi.org/10.1038/s42256-022-00557-6
  3. Narayanan, R., Peker, E., DeGroat, W., Mendhe, D., Zeeshan, S., & Ahmed, Z. (2025). 3D IntelliGenes: AI/ML application using multi-omics data for biomarker discovery and disease prediction with multi-dimensional visualization. BMC medical research methodology, 25(1), 193. https://doi.org/10.1186/s12874-025-02649-4
  4. Fang, X., Liu, L., Lei, J., He, D., Zhang, S., Zhou, J., ... & Wang, H. (2022). Geometry-enhanced molecular representation learning for property prediction. Nature Machine Intelligence, 4(2), 127-134. https://doi.org/10.1038/s42256-021-00438-4
  5. Kuang, T., Ren, Y., & Ren, Z. (2024). 3D-Mol: a novel contrastive learning framework for molecular property prediction with 3D information. Pattern Analysis and Applications, 27(3), 71. https://doi.org/10.1007/s10044-024-01287-8
  6. Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., ... & Ke, G. (2023, May). Uni-mol: A Universal 3d Molecular Representation Learning Framework. In The eleventh international conference on learning representations. https://doi.org/10.26434/chemrxiv-2022-jjm0j-v4
  7. Lu, S., Gao, Z., He, D., Zhang, L., & Ke, G. (2024). Data-driven quantum chemical property prediction leveraging 3d conformations with uni-mol+. Nature communications, 15(1), 7104. https://doi.org/10.1038/s41467-024-51321-w
  8. Liyaqat, T., Ahmad, T., & Saxena, C. (2025). Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches: T. Liyaqat et al. Archives of Computational Methods in Engineering, 1-31. https://doi.org/10.1007/s11831-025-10317-5
  9. Lin, J., Zheng, Y., Chen, X., Ren, Y., Pu, X., & He, J. (2024, October). Cross-view Contrastive Unification Guides Generative Pretraining for Molecular Property Prediction. In Proceedings of the 32nd ACM International Conference on Multimedia (pp. 2108-2116). https://doi.org/10.1145/3664647.3681193
  10. Marimuthu, A. N., & McGuire, B. A. (2025). Machine Learning Pipeline for Molecular Property Prediction Using ChemXploreML. Journal of Chemical Information and Modeling, 65(11), 5424-5437. https://doi.org/10.1021/acs.jcim.5c00516
  11. Dobbelaere, M. R., Lengyel, I., Stevens, C. V., & Van Geem, K. M. (2024). Geometric deep learning for molecular property predictions with chemical accuracy across chemical space. Journal of Cheminformatics, 16(1), 99. https://doi.org/10.1186/s13321-024-00895-0
  12. Correia, J., Capela, J., & Rocha, M. (2024). Deepmol: an automated machine and deep learning framework for computational chemistry. Journal of Cheminformatics, 16(1), 136. https://doi.org/10.1186/s13321-024-00937-7
  13. Gao, Z., Ji, X., Zhao, G., Wang, H., Zheng, H., Ke, G., & Zhang, L. (2023). Uni-qsar: an auto-ml tool for molecular property prediction. arXiv preprint arXiv:2304.12239.
  14. Sun, J., Cao, Y., Hu, H., & Qi, B. (2025). Equivariant learning leveraging geometric invariances in 3D molecular conformers for accurate prediction of quantum chemical properties. Scientific Reports, 15(1), 26969. https://doi.org/10.1038/s41598-025-09842-x
  15. Baran, K., & Kloskowski, A. (2023). Graph neural networks and structural information on ionic liquids: a cheminformatics study on molecular physicochemical property prediction. The Journal of Physical Chemistry B, 127(49), 10542-10555. https://doi.org/10.1021/acs.jpcb.3c05521
  16. Zhu, J., Xia, Y., Wu, L., Xie, S., Qin, T., Zhou, W., ... & Liu, T. Y. (2022, August). Unified 2d and 3d pre-training of molecular representations. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining (pp. 2626-2636). https://doi.org/10.1145/3534678.3539368
  17. Henle, E. A. (2024). Graph-Based Algorithms and Machine Learning Approaches for Molecular Property Prediction and Material Design.
  18. Lu, X., Xie, L., Xu, L., Mao, R., Chang, S., & Xu, X. (2023). Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction. arXiv preprint arXiv:2312.17495.
  19. Atz, K., Grisoni, F., & Schneider, G. (2021). Geometric deep learning on molecular representations. Nature Machine Intelligence, 3(12), 1023-1032. https://doi.org/10.1038/s42256-021-00418-8
  20. Sheshanarayana, R., & You, F. (2025). Molecular representation learning: cross-domain foundations and Future Frontiers. Digital Discovery, 4(9), 2298-2335. https://doi.org/10.1039/D5DD00170F
  21. Luong, K. D., & Singh, A. (2024). Application of transformers in cheminformatics. Journal of Chemical Information and Modeling, 64(11), 4392-4409. https://doi.org/10.1021/acs.jcim.3c02070
  22. Sifain, A. E., Rice, B. M., Yalkowsky, S. H., & Barnes, B. C. (2021). Machine learning transition temperatures from 2D structure. Journal of Molecular Graphics and Modelling, 105, 107848. https://doi.org/10.1016/j.jmgm.2021.107848
  23. Lou, X., Cai, J., & Siu, S. W. (2025). Multi-MoleScale: a multi-scale approach for molecular property prediction with graph contrastive and sequence learning. Journal of Cheminformatics, 18, 5. https://doi.org/10.1186/s13321-025-01126-w
  24. Feinberg, E. N., Sur, D., Wu, Z., Husic, B. E., Mai, H., Li, Y., ... & Pande, V. S. (2018). PotentialNet for molecular property prediction. ACS central science, 4(11), 1520-1530. https://doi.org/10.1021/acscentsci.8b00507
  25. Le, H. V., Ren, W., Kim, J., Yun, Y., Park, Y. B., Kim, Y. J., ... & Choi, J. M. (2025). CaliciBoost: Performance-driven evaluation of molecular representations for caco-2 permeability prediction. Journal of Cheminformatics. https://doi.org/10.1186/s13321-025-01137-7
  26. Dong, J., Cao, D. S., Miao, H. Y., Liu, S., Deng, B. C., Yun, Y. H., ... & Chen, A. F. (2015). ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. Journal of cheminformatics, 7(1), 60. https://doi.org/10.1186/s13321-015-0109-z
  27. Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., ... & Barzilay, R. (2019). Analyzing learned molecular representations for property prediction. Journal of chemical information and modeling, 59(8), 3370-3388. https://doi.org/10.1021/acs.jcim.9b00237
  28. Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., ... & Pande, V. (2018). MoleculeNet: a benchmark for molecular machine learning. Chemical science, 9(2), 513-530. https://doi.org/10.1039/C7SC02664A
  29. W. Jin, C. Coley, R. Barzilay, and T. Jaakkola,” Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network”, https://doi.org/10.48550/arXiv.1709.04555
  30. "Hierarchical graph representation learning for molecular property prediction," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 2126-2137, 2021.
  31. S. Wang, Y. Guo, Y. Wang, H. Sun, and J. Huang, "QSAR modeling using machine learning methods: A review," Engineering Applications of Artificial Intelligence, vol. 98, 104122, 2021.