Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction

Learning protein fitness models from evolutionary and assay-labeled data

(2022) Chloe Hsu et al.   NATURE BIOTECHNOLOGY

Machine learning-aided engineering of hydrolases for PET depolymerization

(2022) Hongyuan Lu et al.   NATURE

Robust deep learning–based protein sequence design using ProteinMPNN

(2022) J. Dauparas et al.   SCIENCE

Low-N protein engineering with data-efficient deep learning

(2021) Surojit Biswas et al.   NATURE METHODS

Active and machine learning-based approaches to rapidly enhance microbial chemical production

(2021) Prashant Kumar et al.   METABOLIC ENGINEERING

Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions

(2021) Amirali Aghazadeh et al.   Nature Communications

Informed training set design enables efficient machine learning-assisted directed protein evolution

(2021) Bruce J. Wittmann et al.   Cell Systems

Neural networks to learn protein sequence–function relationships from deep mutational scanning data

(2021) Sam Gelman et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds

(2019) Murat Cihan Sorkun et al.   Scientific Data

Deep generative models of genetic variation capture the effects of mutations

(2018) Adam J. Riesselman et al.   NATURE METHODS

The EVcouplings Python framework for coevolutionary sequence analysis

(2018) Thomas A Hopf et al.   BIOINFORMATICS

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

(2017) Rebecca F. Alford et al.   Journal of Chemical Theory and Computation

Mutation effects predicted from sequence co-variation

(2017) Thomas A Hopf et al.   NATURE BIOTECHNOLOGY

Local fitness landscape of the green fluorescent protein

(2016) Karen S. Sarkisyan et al.   NATURE

Adaptation in protein fitness landscapes is facilitated by indirect paths

(2016) Nicholas C Wu et al.   eLife

Improving landscape inference by integrating heterogeneous data in the inverse Ising problem

(2016) Pierre Barrat-Charlaix et al.   Scientific Reports

Navigating the protein fitness landscape with Gaussian processes

(2013) P. A. Romero et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Bridging solubility between drug discovery and development

(2011) Li Di et al.   DRUG DISCOVERY TODAY

Getting physical in drug discovery: a contemporary perspective on solubility and hydrophobicity

(2010) Alan P. Hill et al.   DRUG DISCOVERY TODAY