Directed evolution of proteins in sequence space with gradients
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Updated
Jun 14, 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 protein engineering (zero-shot).
Latent-based Directed Evolution guided by Gradient Ascent for Protein Design
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.
Learning to Solve Multiresolution Matrix Factorization by Manifold Optimization and Evolutionary Metaheuristics
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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