New tools enable new ways of working, and materials science is no exception. In materials
discovery, traditional manual, serial, and human-intensive work is being augmented by …

Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …

Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and
powerful computational resources, impacting many fields, including protein structural …

Machine learning (ML) can expedite directed evolution by allowing researchers to move
expensive experimental screens in silico. Gathering sequence-function data for training ML …

Generative models have become adept at producing artifacts such as images, videos, and
prose at human-like levels of proficiency. New generative techniques, such as unsupervised …

The vast expansion of protein sequence databases provides an opportunity for new protein
design approaches which seek to learn the sequence-function relationship directly from …

Most proteins perform their biological function by interacting with themselves or other
molecules. Thus, one may obtain biological insights into protein functions, disease …

Abstract Development of potent and broad-spectrum antimicrobial peptides (AMPs) could
help overcome the antimicrobial resistance crisis. We develop a peptide language-based …

Antimicrobial resistance has become a critical global health problem due to the abuse of
conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides …

Introduction: Precision medicine is the concept of treating diseases based on environmental
factors, lifestyles, and molecular profiles of patients. This approach has been found to …