Publications#

Selected papers#

These publications reflect three recurring themes in my work: reaction informatics and graph-based representations, chemistry-aware machine learning, and software-centered molecular discovery.

Reaction informatics and graph-based molecular transformations#

  1. Reaction rebalancing: a novel approach to curating reaction databases. Journal of Cheminformatics (2024).

  2. SynTemp: Efficient Extraction of Graph-Based Reaction Rules from Large-Scale Reaction Databases. Journal of Chemical Information and Modeling (2025).

  3. SynKit: A Graph-Based Python Framework for Rule-Based Reaction Modeling and Analysis. Journal of Chemical Information and Modeling (2025).

  4. SynRXN: An Open Benchmark and Curated Dataset for Computational Reaction Modeling. Scientific Data (accepted, 2026).

Chemistry-aware machine learning#

  1. SynCat: molecule-level attention graph neural network for precise reaction classification. Digital Discovery (2026).

  2. KGG: Knowledge-Guided Graph Self-Supervised Learning to Enhance Molecular Property Predictions. Journal of Chemical Information and Modeling (2025).

Drug discovery and molecular modeling#

  1. ProQSAR: A Modular and Reproducible Framework for Small-Data QSAR Modeling with Fit-and-Use Models. Journal of Cheminformatics (accepted, 2025).

  2. Novel machine learning approach toward classification model of HIV-1 integrase inhibitors. RSC Advances (2024).

Complete bibliography#

  1. Tieu-Long Phan, N.-N. N. Song, and Peter F. Stadler. Synrxn: an open benchmark and curated dataset for computational reaction modeling. Scientific Data, 2026. accepted.

  2. Tieu-Long Phan, M. E. González Laffitte, K. Weinbauer, D. Merkle, J. L. Andersen, R. Fagerberg, T. Gatter, and Peter F. Stadler. Synkit: a graph-based python framework for rule-based reaction modeling and analysis. Journal of Chemical Information and Modeling, 65(24):13012–13019, 2025. doi:10.1021/acs.jcim.5c02123.

  3. Tieu-Long Phan, K. Weinbauer, M. E. González Laffitte, Y. Pan, D. Merkle, J. L. Andersen, R. Fagerberg, C. Flamm, and Peter F. Stadler. Syntemp: efficient extraction of graph-based reaction rules from large-scale reaction databases. Journal of Chemical Information and Modeling, 65(6):2882–2896, 2025. doi:10.1021/acs.jcim.4c01795.

  4. P.-C. V. Nguyen, V.-T. To, N.-V. N. Tran, Tieu-Long Phan, T. N. Truong, T. Gärtner, D. Merkle, and Peter F. Stadler. Syncat: molecule-level attention graph neural network for precise reaction classification. Digital Discovery, 5:241–253, 2026.

  5. V.-T. To, P.-C. V. Nguyen, G.-B. Truong, T.-M. Phan, Tieu-Long Phan, R. Fagerberg, Peter F. Stadler, and T. N. Truong. Kgg: knowledge-guided graph self-supervised learning to enhance molecular property predictions. Journal of Chemical Information and Modeling, 65(18):9443–9458, 2025.

  6. T.-M. Phan, Tieu-Long Phan, P. C. Van-Nguyen, H. S. L. Le, V. T. To, T. N. Truong, and others. Proqsar: a modular and reproducible framework for small-data qsar modeling with fit-and-use models. Journal of Cheminformatics, 2025. accepted.

  7. Tieu-Long Phan, K. Weinbauer, T. Gärtner, D. Merkle, J. L. Andersen, R. Fagerberg, and Peter F. Stadler. Reaction rebalancing: a novel approach to curating reaction databases. Journal of Cheminformatics, 16(1):82, 2024. doi:10.1186/s13321-024-00875-4.

  8. M. E. González Laffitte, K. Weinbauer, Tieu-Long Phan, N. Beier, N. Domschke, C. Flamm, T. Gatter, D. Merkle, and Peter F. Stadler. Partial imaginary transition state (its) graphs: a formal framework for research and analysis of atom-to-atom maps of unbalanced chemical reactions and their completions. Symmetry, 16(9):1217, 2024. doi:10.3390/sym16091217.

  9. Tieu-Long Phan, T.-C. Trinh, V.-T. To, T.-A. Pham, P.-C. V. Nguyen, T.-M. Phan, and T. N. Truong. Novel machine learning approach toward classification model of hiv-1 integrase inhibitors. RSC Advances, 14:14506, 2024. doi:10.1039/D4RA02231A.