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Found 10 papers, 4 papers with code
Blind Biological Sequence Denoising with Self-Supervised Set Learning
This set embedding represents the "average" of the subreads and can be decoded into a prediction of the clean sequence.
OpenProteinSet: Training data for structural biology at scale
Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein structure prediction for decades.
3D molecule generation by denoising voxel grids
We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids.
Protein Discovery with Discrete Walk-Jump Sampling
We resolve difficulties in training and sampling from a discrete generative model by learning a smoothed energy function, sampling from the smoothed data manifold with Langevin Markov chain Monte Carlo (MCMC), and projecting back to the true data manifold with one-step denoising.
BOtied: Multi-objective Bayesian optimization with tied multivariate ranks
At the heart of MOBO is the acquisition function, which determines the next candidate to evaluate by navigating the best compromises among the objectives.
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling
Instead, recent methods propose to leverage non-stationary data, as well as the sparse mechanism shift assumption in order to learn disentangled representations with a causal semantic.
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences.
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design
Bayesian optimization offers a sample-efficient framework for navigating the exploration-exploitation trade-off in the vast design space of biological sequences.
Multi-segment preserving sampling for deep manifold sampler
We introduce an alternative approach to this guided sampling procedure, multi-segment preserving sampling, that enables the direct inclusion of domain-specific knowledge by designating preserved and non-preserved segments along the input sequence, thereby restricting variation to only select regions.
Black Box Recursive Translations for Molecular Optimization
We call this method Black Box Recursive Translation (BBRT), a new inference method for molecular property optimization.
Ranked #1 on
Molecular Graph Generation
on ZINC
(QED Top-3 metric)
