@inproceedings{
damani2023generative,
title={Generative design for gene therapy: An \${\textbackslash}textit\{in vivo\}\$ validated method},
author={Farhan Damani and David Brookes and Jeffrey Chan and Rishi Jajoo and Alexander Mijalis and Joyce Samson and Flaviu Vadan and Cameron Webster and Stephen Malina and Sam Sinai},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=Pa6mFTZY4R}
}
Machine learning-assisted biological sequence design is a topic of intense interest due to its potential impact on healthcare and biotechnology. In recent years many new approaches have been proposed for sequence design through learning from data alone (rather than mechanistic or structural approaches). These black-box approaches roughly fall into two camps: (i) optimization against a learned oracle (ii) sampling designs from a generative model. While both approaches have demonstrated promise, real-world evidence of their effectiveness is limited, whether used alone or in combination. Here we develop a robust generative model named $\texttt{VAEProp}$ and use it to optimize Adeno-associated virus (AAV) capsids, a fundamental gene therapy vector. We show that our method outperforms algorithmic baselines on this design task in the real world. Critically, we demonstrate that our approach is capable of generating vector designs with field-leading therapeutics potential through in-vitro and non-human primate validation experiments.
Generative design for gene therapy: An in vivo validated method
Farhan Damani David Brookes Jeffrey Chan Rishi Jajoo Alexander Mijalis Joyce Samson Flaviu Vadan Cameron Webster Stephen Malina Sam Sinai