The Rosetta software for macromolecular modeling, docking and design is extensively used
in laboratories worldwide. During two decades of development by a community of …

This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this first part, we describe a classification for …

This paper looks at philosophical questions that arise in the context of AI alignment. It
defends three propositions. First, normative and technical aspects of the AI alignment …

DeepONets have recently been proposed as a framework for learning nonlinear operators
map** between infinite-dimensional Banach spaces. We analyze DeepONets and prove …

Here, we describe a web server that integrates structural alignments with the MAFFT
multiple sequence alignment (MSA) tool. For this purpose, we have prepared a web-based …

The prediction of interresidue contacts and distances from coevolutionary data using deep
learning has considerably advanced protein structure prediction. Here, we build on these …

Background Predicting protein function and structure from sequence is one important
challenge for computational biology. For 26 years, most state-of-the-art approaches …

Physics-informed neural networks (PINNs) have recently been very successfully applied for
efficiently approximating inverse problems for partial differential equations (PDEs). We focus …

Enzyme engineering plays a central role in develo** efficient biocatalysts for
biotechnology, biomedicine, and life sciences. Apart from classical rational design and …

Physics-informed neural networks (PINNs) have recently been widely used for robust and
accurate approximation of partial differential equations (PDEs). We provide upper bounds …