Publications

Featured

Corso, Gabriele, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola. "Diffdock: Diffusion steps, twists, and turns for molecular docking." arXiv preprint arXiv:2210.01776 (2022).

Stärk, Hannes, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola. "Equibind: Geometric deep learning for drug binding structure prediction." In International conference on machine learning, pp. 20503-20521. PMLR, 2022.

Jing, Bowen, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola. "Torsional diffusion for molecular conformer generation." Advances in Neural Information Processing Systems 35 (2022): 24240-24253.

Stokes, Jonathan M., Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M. Donghia, Craig R. MacNair et al. "A deep learning approach to antibiotic discovery." Cell 180, no. 4 (2020): 688-702.

Yala, Adam, Constance Lehman, Tal Schuster, Tally Portnoi, and Regina Barzilay. "A deep learning mammography-based model for improved breast cancer risk prediction." Radiology 292, no. 1 (2019): 60-66.

Mikhael, Peter G., Jeremy Wohlwend, Adam Yala, Ludvig Karstens, Justin Xiang, Angelo K. Takigami, Patrick P. Bourgouin et al. "Sybil: A validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography." Journal of Clinical Oncology 41, no. 12 (2023): 2191-2200.

Yim, Jason, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola. "SE (3) diffusion model with application to protein backbone generation." arXiv preprint arXiv:2302.02277 (2023).

Yala, Adam, Peter G. Mikhael, Constance Lehman, Gigin Lin, Fredrik Strand, Yung-Liang Wan, Kevin Hughes et al. "Optimizing risk-based breast cancer screening policies with reinforcement learning." Nature medicine 28, no. 1 (2022): 136-143