Directed evolution of proteins in sequence space with gradients
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Updated
Apr 13, 2024 - Jupyter Notebook
Directed evolution of proteins in sequence space with gradients
Latent-based Directed Evolution guided by Gradient Ascent for Protein Design
Fusion of protein sequence and structural information, using denoising pre-training network for protein engineering (zero-shot).
Protein Design by Machine Learning guided Directed Evolution
Bayesian optimization with prescreening of search space via supervised outlier detection
Computational model of laboratory directed evolution + experiments.
Directed Evolution in Silico
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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