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Found 9 papers, 3 papers with code
Machine Learning for Protein Engineering
Directed evolution of proteins has been the most effective method for protein engineering.
Protein structure generation via folding diffusion
The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases.
Exploring evolution-aware & -free protein language models as protein function predictors
Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions.
FLIP: Benchmark tasks in fitness landscape inference for proteins
Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications.
Machine learning modeling of family wide enzyme-substrate specificity screens
However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates.
Adaptive machine learning for protein engineering
Machine-learning models that learn from data to predict how protein sequence encodes function are emerging as a useful protein engineering tool.
Protein sequence design with deep generative models
Protein engineering seeks to identify protein sequences with optimized properties.
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
Importantly, we show that our objective function can be efficiently decomposed as a difference of submodular functions (DS), which allows us to employ DS optimization tools to greedily identify sets of constraints that increase the likelihood of finding items with high utility.
Machine learning-guided directed evolution for protein engineering
Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions.
