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All issues Volume 680 (2025) E3S Web Conf., 680 (2025) 00109 References
Open Access
Issue
E3S Web Conf.
Volume 680, 2025
The 4th International Conference on Energy and Green Computing (ICEGC’2025)
Article Number 00109
Number of page(s) 21
DOI https://doi.org/10.1051/e3sconf/202568000109
Published online 19 December 2025
  1. Tayane, S., Gaber, J., 2025. SymChemAI: A Symbolically Informed Neural Network for Physicochemical Modeling of Reaction Systems. https://doi.org/10.26434/chemrxiv-2025-2hp4c-v2 [Google Scholar]
  2. Dara, S., Dhamercherla, S., Jadav, S.S., Babu, C.M., Ahsan, M.J., 2022. Machine Learning in Drug Discovery: A Review. Artif Intell Rev 55, 1947–1999. https://doi.org/10.1007/s10462-021-10058-4 [Google Scholar]
  3. Zhou, Z., Li, X., Zare, R.N., 2017. Optimizing Chemical Reactions with Deep Reinforcement Learning. ACS Cent. Sci. 3, 1337–1344. https://doi.org/10.1021/acscentsci.7b00492 [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  4. Fu, C., Chen, Q., 2025. The future of pharmaceuticals: Artificial intelligence in drug discovery and development. Journal of Pharmaceutical Analysis 101248. https://doi.org/10.1016/j.jpha.2025.101248 [Google Scholar]
  5. Zheng, X., Yang, J., Zhou, L., Qin, L., Lu, C., Alonso-Vante, N., Huang, Z., Zhang, P., Zhou, Y.-N., Luo, Z.-H., Zhu, L.-T., 2025. Big data-driven machine learning transformation for atomic-scale heterogeneous catalyst design: a critical review. Chemical Engineering Science 313, 121740. https://doi.org/10.1016/j.ces.2025.121740 [Google Scholar]
  6. Hasselgren, C., Oprea, T.I., 2024. Artificial Intelligence for Drug Discovery: Are We There Yet? Annu. Rev. Pharmacol. Toxicol. 64, 527–550. https://doi.org/10.1146/annurev-pharmtox-040323-040828 [Google Scholar]
  7. Lu, X., Yang, H., Chen, Y., Li, Q., He, S., Jiang, X., Feng, F., Qu, W., Sun, H., 2018. The Development of Pharmacophore Modeling: Generation and Recent Applications in Drug Discovery. CPD 24, 3424–3439. https://doi.org/10.2174/1381612824666180810162944 [Google Scholar]
  8. Probst, D., Schwaller, P., Reymond, J.-L., 2022. Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital Discovery 1, 91–97. https://doi.org/10.1039/D1DD00006C [Google Scholar]
  9. Sandfort, F., Strieth-Kalthoff, F., Kühnemund, M., Beecks, C., Glorius, F., 2020. A Structure-Based Platform for Predicting Chemical Reactivity. Chem 6, 1379–1390. https://doi.org/10.1016/j.chempr.2020.02.017 [Google Scholar]
  10. Chen, T., Guestrin, C., 2016. XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Presented at the KDD ‘16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco California USA, pp. 785–794. https://doi.org/10.1145/2939672.2939785 [Google Scholar]
  11. Weininger, D., 1988. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36. https://doi.org/10.1021/ci00057a005 [Google Scholar]
  12. Kearnes, S.M., Maser, M.R., Wleklinski, M., Kast, A., Doyle, A.G., Dreher, S.D., Hawkins, J.M., Jensen, K.F., Coley, C.W., 2021. The Open Reaction Database. J. Am. Chem. Soc. 143, 18820–18826. https://doi.org/10.1021/jacs.1c09820 [Google Scholar]
  13. Mswahili, M.E., Jeong, Y.-S., 2024. Transformer-based models for chemical SMILES representation: A comprehensive literature review. Heliyon 10, e39038. https://doi.org/10.1016/j.heliyon.2024.e39038 [Google Scholar]
  14. Schwaller, P., Vaucher, A.C., Laino, T., Reymond, J.-L., 2021. Prediction of chemical reaction yields using deep learning. Mach. Learn.: Sci. Technol. 2, 015016. https://doi.org/10.1088/2632-2153/abc81d [Google Scholar]
  15. Nam, J., Kim, J., 2016. Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions. https://doi.org/10.48550/arXiv.1612.09529 [Google Scholar]
  16. Jin, D., Liang, Y., Xiong, Z., Yang, X., Wang, H., Zeng, J., Gu, S., 2025. Application of Transformers to Chemical Synthesis. Molecules 30, 493. https://doi.org/10.3390/molecules30030493 [Google Scholar]
  17. Jin, W., Coley, C.W., Barzilay, R., Jaakkola, T., 2017. Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. https://doi.org/10.48550/arXiv.1709.04555 [Google Scholar]
  18. Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., Persson, K.A., Ceder, G., Jain, A., 2019. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571, 95–98. https://doi.org/10.1038/s41586-019-1335-8 [Google Scholar]
  19. Goodman, J., 2009. Computer Software Review: Reaxys. J. Chem. Inf. Model. 49, 2897–2898. https://doi.org/10.1021/ci900437n [Google Scholar]
  20. Coley, C.W., Jin, W., Rogers, L., Jamison, T.F., Jaakkola, T.S., Green, W.H., Barzilay, R., Jensen, K.F., 2019. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 10, 370–377. https://doi.org/10.1039/C8SC04228D [Google Scholar]
  21. Friedman, D., Zhao, S., 2021. When are mixed equilibria relevant? Journal of Economic Behavior & Organization 191, 51–65. https://doi.org/10.1016/j.jebo.2021.08.031 [Google Scholar]
  22. Yan, C., Ding, Q., Zhao, P., Zheng, S., Yang, J., Yu, Y., Huang, J., 2020. RetroXpert: Decompose Retrosynthesis Prediction like a Chemist. https://doi.org/10.48550/arXiv.2011.02893 [Google Scholar]
  23. Han, J., Kwon, Y., Choi, Y.-S., Kang, S., 2024. Improving chemical reaction yield prediction using pre-trained graph neural networks. J Cheminform 16, 25. https://doi.org/10.1186/s13321-024-00818-z [Google Scholar]
  24. Raissi, M., Perdikaris, P., Karniadakis, G.E., 2019a. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics 378, 686–707. https://doi.org/10.1016/j.jcp.2018.10.045 [CrossRef] [Google Scholar]
  25. Gusmão, G.S., Retnanto, A.P., Cunha, S.C.D., Medford, A.J., 2023a. Kinetics-informed neural networks. Catalysis Today 417, 113701. https://doi.org/10.1016/j.cattod.2022.04.002 [Google Scholar]
  26. Fraczkiewicz, R., 2025. Thermodynamics-informed neural networks and extensive data sets: key factors to accurate blind predictions of apparent p Ka values in the euroSAMPL challenge. Phys. Chem. Chem. Phys. 27, 8039–8042. https://doi.org/10.1039/D5CP00165J [Google Scholar]
  27. Malik, H., Chaudhry, M.U., Jasinski, M., 2022. Deep Learning for Molecular Thermodynamics. Energies 15, 9344. https://doi.org/10.3390/en15249344 [Google Scholar]
  28. Rittig, J.G., Mitsos, A., 2024. Thermodynamics-Consistent Graph Neural Networks. Chem. Sci. 15, 18504–18512. https://doi.org/10.1039/D4SC04554H [Google Scholar]
  29. Fujita, S., 1986. Description of organic reactions based on imaginary transition structures. Introduction of new concepts. J. Chem. Inf. Comput. Sci. 26, 205–212. https://doi.org/10.1021/ci00052a009 [Google Scholar]
  30. Ozer, D., Lamprier, S., Cauchy, T., Gutowski, N., Mota, B.D., 2025. A Transformer Model for Predicting Chemical Reaction Products from Generic Templates. https://doi.org/10.48550/arXiv.2503.05810 [Google Scholar]
  31. Sato, A., Asahara, R., Miyao, T., 2024. Chemical Graph-Based Transformer Models for Yield Prediction of High-Throughput Cross-Coupling Reaction Datasets. ACS Omega 9, 40907–40919. https://doi.org/10.1021/acsomega.4c06113 [Google Scholar]
  32. Liao, J., Shi, X., Zhu, T., 2024. Application of Modern Intelligent Algorithms in Retrosynthesis Prediction. https://doi.org/10.26434/chemrxiv-2024-jk7db [Google Scholar]
  33. Angus Keto, Taicheng Guo, Morgan Underdue, Thijs Stuyver, Connor W. Coley, Xiangliang Zhang, Elizabeth H. Krenske, and Olaf Wiest. (2024). Data-Efficient, Chemistry-Aware Machine Learning Predictions of Diels–Alder Reaction Outcomes. Journal of the American Chemical Society 146 (23), 16052-16061. https://doi.org/10.1021/jacs.4c03131 [Google Scholar]
  34. Zeng, K., Liu, X., Zhang, Y., Yang, X., Jin, Y., Xu, Y., 2025. Learning Chemical Reaction Representation with Reactant-Product Alignment. https://doi.org/10.48550/arXiv.2411.17629 [Google Scholar]
  35. Masclans, N., Vázquez-Novoa, F., Bernades, M., Badia, R.M., Jofre, L., 2023. Thermodynamics-informed neural network for recovering supercritical fluid thermophysical information from turbulent velocity data. International Journal of Thermofluids 20, 100448. https://doi.org/10.1016/j.ijft.2023.100448 [Google Scholar]
  36. Butcher, J.C., 2016. Numerical methods for ordinary differential equations, 3rd ed. ed. Wiley, Chichester, UK. https://doi.org/10.1002/9781119121534 [Google Scholar]
  37. Xu, L.-Y., Liu, C.-Y., Liu, S.-Y., Ren, Z.-G., Young, D.J., Lang, J.-P., 2017. Suzuki-Miyaura cross-coupling reaction of aryl chlorides with aryl boronic acids catalyzed by a palladium dichloride adduct of N-diphenylphosphanyl-2-aminopyridine. Tetrahedron 73, 3125–3132. https://doi.org/10.1016/j.tet.2017.04.034 [Google Scholar]
  38. Song, Z., Zhang, H., Yang, Z., Han, Y., Li, Y., & Wu, J. (2023). SPIN-ODE: Stiff Physics-Informed Neural ODE for Chemical Reaction Rate Estimation. arXiv preprint arXiv:2302.13942. DOI: 10.48550/arXiv.2302.13942 [Google Scholar]
  39. Wang, B., Wang, X., & Carlberg, K. T. (2022). Physics-informed neural networks and functional interpolation for stiff chemical kinetics. Journal of Computational Physics, 462, 111161. DOI: 10.1016/j.jcp.2022.111161 [Google Scholar]
  40. Lu, L., Jin, P., Pang, G., Zhang, Z., & Karniadakis, G. E. (2021). A chemical kinetic modeling approach based on physics-informed neural networks. The Journal of Physical Chemistry A, 125(32), 6799–6809. DOI: 10.1021/acs.jpca.1c05102 [Google Scholar]

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