Retrobiosynthesis provides an effective and sustainable approach to producing functional
molecules. The past few decades have witnessed a rapid expansion of biosynthetic …

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 …

We consider the problem of predicting a protein sequence from its backbone atom
coordinates. Machine learning approaches to this problem to date have been limited by the …

The binding and catalytic functions of proteins are generally mediated by a small number of
functional residues held in place by the overall protein structure. Here, we describe deep …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Deep generative models are increasingly powerful tools for the in silico design of novel
proteins. Recently, a family of generative models called diffusion models has demonstrated …

Despite tremendous progress of generative models in the natural sciences, their
controllability remains challenging. One fundamentally missing aspect of molecular or …

Machine learning (ML) is revolutionizing our ability to understand and predict the complex
relationships between protein sequence, structure, and function. Predictive sequence …

Deep learning approaches have produced substantial breakthroughs in fields such as
image classification and natural language processing and are making rapid inroads in the …

Generative machine learning (ML) has been postulated to become a major driver in the
computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to …