Scientific discovery in the age of artificial intelligence.

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

Attentive Cross-Modal Paratope Prediction.

Antibodies are a critical part of the immune system, having the function of recognizing and mediating the neutralization of undesirable molecules (antigens) for future destruction. Being able to predict which amino acids belong to the paratope , the region on the antibody that binds to the antigen, can facilitate antibody engineering and predictions of antibody-antigen structures. The suitability of deep neural networks has recently been confirmed for this task, with Parapred outperforming all prior models. In this work, we first significantly outperform the computational efficiency of Parapred by leveraging à trous convolutions and self-attention. Second, we implement cross-modal attention by allowing the antibody residues to attend over antigen residues. This leads to new state-of-the-art results in paratope prediction, along with novel opportunities to interpret the outcome of the prediction.