Directed evolution of proteins in sequence space with gradients
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Updated
Aug 6, 2024 - Jupyter Notebook
Directed evolution of proteins in sequence space with gradients
Fusion of protein sequence and structural information, using denoising pre-training network for zero-shot protein engineering (eLife 2024).
Protein Design by Machine Learning guided Directed Evolution
Latent-based Directed Evolution guided by Gradient Ascent for Protein Design
Bayesian optimization with prescreening of search space via supervised outlier detection
Computational model of laboratory directed evolution + experiments.
Directed Evolution in Silico
Learning to Solve Multiresolution Matrix Factorization by Manifold Optimization and Evolutionary Metaheuristics
Generates randomly generated fastQ files from a template and upstream sequence.
Calculates the probability of finding the top variant in a library of sequences.
Protein engineering with large language models
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