31 citations · 96 across the 11 of their papers we have counts for
13 papers
Active Learning for Discovering Complex Phase Diagrams with Gaussian Processes
Max Zhu, Jian Yao, Marcus Mynatt +5
We introduce a Bayesian active learning algorithm that efficiently elucidates phase diagrams. Using a novel acquisition function that assesses both the impact and likelihood of the…
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…
Conditional Neural Processes for Molecules
Miguel Garcia-Ortegon, Andreas Bender, Sergio Bacallado
Neural processes (NPs) are models for transfer learning with properties reminiscent of Gaussian Processes (GPs). They are adept at modelling data consisting of few observations of…
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…
The *-Edge-Reinforced Random Walk
Sergio Bacallado, Christophe Sabot, Pierre Tarrès
We define a linearly reinforced process called the *-Edge-Reinforced Random Walk (*-ERRW ) which can be seen as a Yaglom reversible, hence non-reversible, extension of the Edge-Rei…
BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic
Qingyuan Zhao, Nianqiao Ju, Sergio Bacallado +1
The coronavirus disease 2019 (COVID-19) has quickly grown from a regional outbreak in Wuhan, China to a global pandemic. Early estimates of the epidemic growth and incubation perio…