Volume 3 Issue 2, February 2023

Dr Valentino Cooper, a Distinguished R&D Staff Member at Oak Ridge National Laboratory, talks to Nature Computational Science about his research on density functional theory and on designing high-entropy materials and piezoelectrics.

A computational tool has been developed for the multiscale design of open disordered material systems, bridging network science, computational materials, and wave physics.

A topology-based approach is developed to better understand the relationship between protein structure and protein function.

Inferring gene networks from discrete RNA counts across cells remains a complex problem. Following Bayesian non-parametrics, a computational framework is proposed to perform non-biased inference of transcription kinetics from single-cell RNA counting experiments.

The concept of evolving scattering networks is proposed for material design in wave physics. The concept has the potential to enable network-based material classification, microstructure screening and the design of stealthy hyperuniformity with superdense phases.

A generative deep learning model of molecular structure is combined with supervised deep learning models of molecular properties to achieve high-throughput (multi-)property-driven design of organic molecules.

A topological data analysis-driven machine learning model for guiding protein engineering is proposed, complementing protein sequence and structure embeddings when navigating the fitness landscape.

A framework is presented to extrapolate the range of behaviors for influenza antibodies. Using this basis set of behaviors, the collective action of multiple antibodies can be teased apart to describe the individual antibodies within.

A method that infers gene networks and rate parameters directly from single-molecule fluorescence in situ hybridization RNA snapshot data is proposed and demonstrated on synthetic and real data, providing insights on data from S. cerevisiae and E. coli.