We present an exciting journey from 3D point‐cloud data (PCD) to the state of the art in
graph neural networks (GNNs) and their evolution with explainable artificial intelligence …

This review highlights recent advances in machine learning (ML)‐assisted design of energy
materials. Initially, ML algorithms were successfully applied to screen materials databases …

The directed design and discovery of compounds with pre-determined properties is a long-
standing challenge in materials research. We provide a perspective on progress toward …

Geometric deep learning models, which incorporate the relevant molecular symmetries
within the neural network architecture, have considerably improved the accuracy and data …

Enhanced sampling simulations make the computational study of rare events feasible. A
large family of such methods crucially depends on the definition of some collective variables …

Date: October 18, 2024 Correspondence: L. Barroso-Luque (lbluque@ meta. com), CL
Zitnick (zitnick@ meta. com), Z. Ulissi (zulissi@ meta. com) Code: https://github. com/FAIR …

Achieving a balance between computational speed, prediction accuracy, and universal
applicability in molecular simulations has been a persistent challenge. This paper presents …

Computational RNA design tasks are often posed as inverse problems, where sequences
are designed based on adopting a single desired secondary structure without considering …

Peptide design plays a pivotal role in therapeutics, allowing brand new possibility to
leverage target binding sites that are previously undruggable. Most existing methods are …

Chemical engineers heavily rely on precise knowledge of physicochemical properties to
model chemical processes. Despite the growing popularity of deep learning, it is only rarely …