Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …

Enzymes can be engineered at the level of their amino acid sequences to optimize key
properties such as expression, stability, substrate range, and catalytic efficiency─ or even to …

Protein design holds immense potential for optimizing naturally occurring proteins, with
broad applications in drug discovery, material design, and sustainability. However …

In recent years, generative protein sequence models have been developed to sample novel
sequences. However, predicting whether generated proteins will fold and function remains …

Multiple sequence alignments (MSAs) of proteins encode rich biological information and
have been workhorses in bioinformatic methods for tasks like protein design and protein …

The rapid progress in the field of deep learning has had a significant impact on protein
design. Deep learning methods have recently produced a breakthrough in protein structure …

Protein language models (pLMs) capture some aspects of the grammar of the language of
life as written in protein sequences. The so-called pLM embeddings implicitly contain this …

Machine learning has been proposed as an alternative to theoretical modeling when
dealing with complex problems in biological physics. However, in this perspective, we argue …

Protein design and engineering are evolving at an unprecedented pace leveraging the
advances in deep learning. Current models nonetheless cannot natively consider non …

Variational autoencoders are unsupervised learning models with generative capabilities,
when applied to protein data, they classify sequences by phylogeny and generate de novo …