We envision" AI scientists" as systems capable of skeptical learning and reasoning that
empower biomedical research through collaborative agents that integrate AI models and …

Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …

Attention-based models trained on protein sequences have demonstrated incredible
success at classification and generation tasks relevant for artificial-intelligence-driven …

Proteins are key to all cellular processes and their structure is important in understanding
their function and evolution. Sequence-based predictions of protein structures have …

Exploiting sequence–structure–function relationships in biotechnology requires improved
methods for aligning proteins that have low sequence similarity to previously annotated …

Adapting language models to protein sequences spawned the development of powerful
protein language models (pLMs). Concurrently, AlphaFold2 broke through in protein …

The design of novel protein structures remains a challenge in protein engineering for
applications across biomedicine and chemistry. In this line of work, a diffusion model over …

We are now entering a new era in protein sequence and structure annotation, with hundreds
of millions of predicted protein structures made available through the AlphaFold database …

The rapid evolution of viruses generates proteins that are essential for infectivity and
replication but with unknown functions, due to extreme sequence divergence. Here, using a …

Prediction methods inputting embeddings from protein language models have reached or
even surpassed state-of-the-art performance on many protein prediction tasks. In natural …