Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, hel** scientists to generate hypotheses, design experiments …

The COVID-19 pandemic has emphasized the need for novel drug discovery process.
However, the journey from conceptualizing a drug to its eventual implementation in clinical …

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 …

Generative models, especially diffusion models (DMs), have achieved promising results for
generating feature-rich geometries and advancing foundational science problems such as …

Illuminating interactions between proteins and small drug molecules is a long-standing
challenge in the field of drug discovery. Despite the importance of understanding these …

Despite their widespread success in various domains, Transformer networks have yet to
perform well across datasets in the domain of 3D atomistic graphs such as molecules even …

While neural networks achieve state-of-the-art performance for many molecular modeling
and structure–property prediction tasks, these models can struggle with generalization to out …

Generating molecules that bind to specific proteins is an important but challenging task in
drug discovery. Most previous works typically generate atoms autoregressively, with element …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Abstract We introduce Clifford Group Equivariant Neural Networks: a novel approach for
constructing $\mathrm {O}(n) $-and $\mathrm {E}(n) $-equivariant models. We identify and …