Referenced Papers (4)
Atomwise
M. Béczi
NeurIPS ML BDD Bioxi. Vol. 10
"This citation is the source describing the AtomNet model and the large-scale dataset used for its training."
AI is a viable alternative to high throughput screening: a 318-target study
Izhar Wallach, Denzil Bernard, Kong Nguyen, Gregory Ho, Adrian Morrison, A Stecula
Sci. Rep., 2024
"This paper details the results of a massive, 318-target study validating the AtomNet platform's ability to successfully identify first-in-class hits, even for targets with no prior training data."
An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries
A. Peddinti, P. Ginis, C. Chang, B. Anderson, H. van den Bedem
Advances in Neural Information Processing Systems, 35, pp.8731-8743.
"This paper describes the foundational CSLVAE method developed by the speaker's team, which uses a variational autoencoder to efficiently generate synthesizable molecules from massive combinatorial libraries."
NGT: Generative AI with synthesizability guarantees discovers MC2R inhibitors from a Tera-scale virtual screen
Saulo H P de Oliveira, Aryan Pedawi, Victor Kenyon, Henry van den Bedem
J. Med. Chem., 2024
"This paper, published the week of the talk, serves as a case study for the successful application of the NGT generative AI method to discover novel, synthesizable inhibitors for the challenging MC2R target."