Referenced Papers (6)
Learning the protein language: Evolution, structure, and function
Tristan Bepler, Bonnie Berger
Cell Syst., 2021
"This paper is cited as an example of using representation learning and unsupervised models for density estimation on large-scale protein databases."
Illuminating protein space with a programmable generative model
John B Ingraham, Max Baranov, Zak Costello, Karl W Barber, Wujie Wang, Ahmed Ismail
Nature, 2023
"Cited as an example of the latest trend in using generative models, specifically diffusion models, for predicting protein backbone structures."
Generative models for protein structures and sequences
Chloe Hsu, Clara Fannjiang, Jennifer Listgarten
Nat. Biotechnol., 2024
"The speaker cites this primer, which she co-authored, as a resource for the audience to learn more about generative models for protein structures and sequences."
Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla
ACS Cent. Sci., 2018
"This is one of several papers cited from the speaker's group that explores the fundamental tension between extrapolation and trustworthiness in machine learning-based protein design."
Unlocking guidance for discrete state-space diffusion and flow models
Hunter Nisonoff, Junhao Xiong, Stephan Allenspach, Jennifer Listgarten
arXiv [cs.LG], 2024
"This work from the speaker's lab is presented as a method for providing property guidance for diffusion and flow matching on discrete spaces."
Optimal trade-off control in machine learning-based library design, with application to adeno-associated virus (AAV) for gene therapy
Danqing Zhu, David H Brookes, Akosua Busia, Ana Carneiro, Clara Fannjiang, Galina Popova
Sci. Adv., 2024
"This paper describes the AAV library design project detailed in the talk, which combines various machine learning techniques to create more effective libraries for directed evolution."