188 citations · 317 across the 11 of their papers we have counts for
12 papers
Zero-shot protein stability prediction by inverse folding models: a free energy interpretation
Jes Frellsen, Maher M. Kassem, Tone Bengtsen +4
Inverse folding models have proven to be highly effective zero-shot predictors of protein stability. Despite this success, the link between the amino acid preferences of an inverse…
Kermut: Composite kernel regression for protein variant effects
Peter Mørch Groth, Mads Herbert Kerrn, Lars Olsen +2
Reliable prediction of protein variant effects is crucial for both protein optimization and for advancing biological understanding. For practical use in protein engineering, it is…
A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences
Miguel González-Duque, Richard Michael, Simon Bartels +3
Optimizing discrete black-box functions is key in several domains, e.g. protein engineering and drug design. Due to the lack of gradient information and the need for sample efficie…
A Continuous Relaxation for Discrete Bayesian Optimization
Richard Michael, Simon Bartels, Miguel González-Duque +4
To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization. We propose a continuous relaxation of the object…
Implicit Variational Inference for High-Dimensional Posteriors
Anshuk Uppal, Kristoffer Stensbo-Smidt, Wouter Boomsma +1
In variational inference, the benefits of Bayesian models rely on accurately capturing the true posterior distribution. We propose using neural samplers that specify implicit distr…
Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters
Marloes Arts, Jes Frellsen, Wouter Boomsma
After the recent ground-breaking advances in protein structure prediction, one of the remaining challenges in protein machine learning is to reliably predict distributions of struc…
Adaptive Cholesky Gaussian Processes
Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz +3
We present a method to approximate Gaussian process regression models for large datasets by considering only a subset of the data. Our approach is novel in that the size of the sub…
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
Simon Bartels, Wouter Boomsma, Jes Frellsen +1
Algorithms involving Gaussian processes or determinantal point processes typically require computing the determinant of a kernel matrix. Frequently, the latter is computed from the…
What is a meaningful representation of protein sequences?
Nicki Skafte Detlefsen, Søren Hauberg, Wouter Boomsma
How we choose to represent our data has a fundamental impact on our ability to subsequently extract information from them. Machine learning promises to automatically determine effi…
(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
Anton Mallasto, Jes Frellsen, Wouter Boomsma +1
Generative Adversial Networks (GANs) have made a major impact in computer vision and machine learning as generative models. Wasserstein GANs (WGANs) brought Optimal Transport (OT)…
Protein structure validation and refinement using amide proton chemical shifts derived from quantum mechanics
Anders S. Christensen, Troels E. Linnet, Mikael Borg +4
We present the ProCS method for the rapid and accurate prediction of protein backbone amide proton chemical shifts - sensitive probes of the geometry of key hydrogen bonds that det…
Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized
Thomas Hamelryck, Mikael Borg, Martin Paluszewski +6
Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so…