| Title | Year | Venue | Link | Ref |
|---|---|---|---|---|
| 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. |