37 citations · 112 across the 12 of their papers we have counts for
13 papers
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…
Genetic algorithms are strong baselines for molecule generation
Austin Tripp, José Miguel Hernández-Lobato
Generating molecules, both in a directed and undirected fashion, is a huge part of the drug discovery pipeline. Genetic algorithms (GAs) generate molecules by randomly modifying kn…
Stochastic Gradient Descent for Gaussian Processes Done Right
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán +5
As is well known, both sampling from the posterior and computing the mean of the posterior in Gaussian process regression reduces to solving a large linear system of equations. We…
Retro-fallback: retrosynthetic planning in an uncertain world
Austin Tripp, Krzysztof Maziarz, Sarah Lewis +2
Retrosynthesis is the task of planning a series of chemical reactions to create a desired molecule from simpler, buyable molecules. While previous works have proposed algorithms to…
Tanimoto Random Features for Scalable Molecular Machine Learning
Austin Tripp, Sergio Bacallado, Sukriti Singh +1
The Tanimoto coefficient is commonly used to measure the similarity between molecules represented as discrete fingerprints, either as a distance metric or a positive definite kerne…
Retrosynthetic Planning with Dual Value Networks
Guoqing Liu, Di Xue, Shufang Xie +7
Retrosynthesis, which aims to find a route to synthesize a target molecule from commercially available starting materials, is a critical task in drug discovery and materials design…
GAUCHE: A Library for Gaussian Processes in Chemistry
Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss +24
We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantag…
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato
We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a novel framework for learning deep kernel Gaussian processes (GPs) by interpolating between meta…
DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp +3
The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solub…
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
Austin Tripp, Erik Daxberger, José Miguel Hernández-Lobato
Many important problems in science and engineering, such as drug design, involve optimizing an expensive black-box objective function over a complex, high-dimensional, and structur…