Understanding the genetic factors that underlie the normal variation in cardiac shape is of great interest. In this work, Bonazzola et al. apply unsupervised geometric deep learning to phenotype the left ventricle by using an MRI-derived three-dimensional mesh representation (as depicted on the cover). The authors show that this approach boosts genetic discovery and provides deeper insights into the genetic underpinnings of cardiac morphology.

See Bonazzola et al.

Predicting the structure of 3D biological binding complexes is a major challenge in structural biology. Qiao et al. report a diffusion model-based generative AI approach known as NeuralPLexer that enables the prediction of protein–ligand structures, including large-scale conformational changes of such structures after ligand binding, based on protein sequences and ligand molecular graphs. The methodology could advance the mechanistic understanding of biological pathways and aid the discovery of new therapeutic agents.

See Qiao et al.

Training a neural network, which involves optimizing its parameters to reduce a loss function, can be thought of as moving through a landscape with hills of high error and valleys of low error. In the cover image, the red line shows such a trajectory, moving along the gradient towards lower loss. In this issue, Ciceri et al. describe that in successfully learning classification tasks, this training trajectory does not follow a direct route. Instead, the path takes a detour, shown here in brighter red, in which the representation of the data separates in training before later rejoining.

See Ciceri et al.