37 citations · 112 across the 12 of their papers we have counts for
4 papers · 1 filter
A Deep Generative Model for the Design of Synthesizable Ionizable Lipids
Yuxuan Ou, Jingyi Zhao, Austin Tripp +2
Lipid nanoparticles (LNPs) are vital in modern biomedicine, enabling the effective delivery of mRNA for vaccines and therapies by protecting it from rapid degradation. Among the co…
Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach
Jingyi Zhao, Yuxuan Ou, Austin Tripp +2
Ionizable lipids are essential in developing lipid nanoparticles (LNPs) for effective messenger RNA (mRNA) delivery. While traditional methods for designing new ionizable lipids ar…
Batched Bayesian optimization by maximizing the probability of including the optimum
Jenna Fromer, Runzhong Wang, Mrunali Manjrekar +3
Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategie…
Diagnosing and fixing common problems in Bayesian optimization for molecule design
Austin Tripp, José Miguel Hernández-Lobato
Bayesian optimization (BO) is a principled approach to molecular design tasks. In this paper we explain three pitfalls of BO which can cause poor empirical performance: an incorrec…