| Title | Year | Venue | Link | Ref |
|---|---|---|---|---|
| A Dataset of Plausible Proton Transfer Steps for Arrow-Pushing Mechanisms | 2026 | Nature - Scientific Data | link | Dashuta, A. E., Miller, R. J., Baldi, P., Sander, T., & Van Vranken, D. L. (2026). A Dataset of Plausible Proton Transfer Steps for Arrow-Pushing Mechanisms. Scientific Data. |
| Mechanism-Aware Deep Learning for Polar Reaction Prediction | 2025 | ACS - Journal of the American Chemical Society | link | Miller, R. J., Dashuta, A. E., Rudisill, B., Van Vranken, D., & Baldi, P. (2025). Mechanism-Aware Deep Learning for Polar Reaction Prediction. Journal of the American Chemical Society, 147(44), 41168-41176. |
| PMechDB: A Public Database of Elementary Polar Reaction Steps | 2024 | ACS - Journal of Chemical Information and Modeling | link | Tavakoli, M., Miller, R. J., Angel, M. C., Pfeiffer, M. A., Gutman, E. S., Mood, A. D., ... & Baldi, P. (2024). Pmechdb: A public database of elementary polar reaction steps. Journal of Chemical Information and Modeling, 64(6), 1975-1983. |
| AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning | 2023 | Conference on Neural Information Processing Systems | link | Tavakoli, M., Chiu, Y.T.T., Shmakov, A., Carlton, A.M., Van Vranken, D. and Baldi, P., 2023. AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning. arXiv preprint arXiv:2311.01118. |
| RMechDB: A Public Database of Elementary Radical Reaction Steps | 2023 | ACS - Journal of Chemical Information and Modeling | link | M. Tavakoli, Y.T. Chiu, P. Baldi, A.M. Carlton, and D. Van Vranken, "RMechDB: A Public Database of Elementary Radical Reaction Steps", Journal of Chemical Information and Modeling, DOI: 10.1021/acs.jcim.2c01359 |
| Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity | 2022 | ACS - Journal of Chemical Information and Modeling | link | Tavakoli, M., Mood, A., Van Vranken, D. and Baldi, P., 2022. Quantum mechanics and machine learning synergies: graph attention neural networks to predict chemical reactivity. Journal of Chemical Information and Modeling, 62(9), pp.2121-2132. |
| Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation | 2022 | AAAI Deep Learning on Graphs: Methods and Applications | link | Tavakoli, M., Shmakov, A., Ceccarelli, F. and Baldi, P., 2022. Rxn hypergraph: a hypergraph attention model for chemical reaction representation. arXiv preprint arXiv:2201.01196. |
| Methyl Cation Affinities of Canonical Organic Functional Groups | 2021 | ACS - The Journal of Organic Chemistry | link | Kadish, D., Mood, A.D., Tavakoli, M., Gutman, E.S., Baldi, P. and Van Vranken, D.L., 2021. Methyl cation affinities of canonical organic functional groups. The Journal of Organic Chemistry, 86(5), pp.3721-3729. |
| Methyl Anion Affinities of the Canonical Organic Functional Groups | 2020 | ACS - The Journal of Organic Chemistry | link | Mood, A., Tavakoli, M., Gutman, E., Kadish, D., Baldi, P. and Van Vranken, D.L., 2020. Methyl Anion Affinities of the Canonical Organic Functional Groups. The Journal of organic chemistry, 85(6), pp.4096-4102. |
| Continuous Representation of Molecules Using Graph Variational Autoencoder | 2020 | AAAI Spring Symposium: MLPS | link | Tavakoli, M. and Baldi, P., 2020. Continuous representation of molecules using graph variational autoencoder. arXiv preprint arXiv:2004.08152. |
| Deep Learning for Chemical Reaction Prediction | 2018 | RCS - Molecular Systems Design & Engineering | link | Fooshee, D., Mood, A., Gutman, E., Tavakoli, M., Urban, G., Liu, F., Huynh, N., Van Vranken, D. and Baldi, P., 2018. Deep learning for chemical reaction prediction. Molecular Systems Design & Engineering, 3(3), pp.442-452. |