[HTML][HTML] Adaptive machine learning for protein engineering
Abstract Machine-learning models that learn from data to predict how protein sequence
encodes function are emerging as a useful protein engineering tool. However, when using …
encodes function are emerging as a useful protein engineering tool. However, when using …
ECNet is an evolutionary context-integrated deep learning framework for protein engineering
Abstract Machine learning has been increasingly used for protein engineering. However,
because the general sequence contexts they capture are not specific to the protein being …
because the general sequence contexts they capture are not specific to the protein being …
Is novelty predictable?
Machine learning–based design has gained traction in the sciences, most notably in the
design of small molecules, materials, and proteins, with societal applications ranging from …
design of small molecules, materials, and proteins, with societal applications ranging from …
Accelerating bayesian optimization for biological sequence design with denoising autoencoders
Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous
optimization. However, its adoption for drug design has been hindered by the discrete, high …
optimization. However, its adoption for drug design has been hindered by the discrete, high …
Conservative objective models for effective offline model-based optimization
In this paper, we aim to solve data-driven model-based optimization (MBO) problems, where
the goal is to find a design input that maximizes an unknown objective function provided …
the goal is to find a design input that maximizes an unknown objective function provided …
Bayesian optimization of nanoporous materials
Nanoporous materials (NPMs) could be used to store, capture, and sense many different
gases. Given an adsorption task, we often wish to search a library of NPMs for the one with …
gases. Given an adsorption task, we often wish to search a library of NPMs for the one with …
Neural networks to learn protein sequence–function relationships from deep mutational scanning data
The map** from protein sequence to function is highly complex, making it challenging to
predict how sequence changes will affect a protein's behavior and properties. We present a …
predict how sequence changes will affect a protein's behavior and properties. We present a …
Proximal exploration for model-guided protein sequence design
Designing protein sequences with a particular biological function is a long-lasting challenge
for protein engineering. Recent advances in machine-learning-guided approaches focus on …
for protein engineering. Recent advances in machine-learning-guided approaches focus on …
Design-bench: Benchmarks for data-driven offline model-based optimization
Black-box model-based optimization (MBO) problems, where the goal is to find a design
input that maximizes an unknown objective function, are ubiquitous in a wide range of …
input that maximizes an unknown objective function, are ubiquitous in a wide range of …
Data-driven offline decision-making via invariant representation learning
The goal in offline data-driven decision-making is synthesize decisions that optimize a black-
box utility function, using a previously-collected static dataset, with no active interaction …
box utility function, using a previously-collected static dataset, with no active interaction …