Volume 3 Issue 5, May 2023

As artificial intelligence (AI) proliferates, synthetic chemistry stands to benefit from its progress. Despite hidden variables and ‘unknown unknowns’ in datasets that may impede the realization of a digital twin for the laboratory flask, there are many opportunities to leverage AI and large datasets to advance synthesis science.

An image-inspired deep-learning model is developed to generate realistic de novo protein structures and scaffolds around functional sites, which helps the search for new structures and functions in protein engineering.

A computational tool based on an additive approach and linear algebra has been developed together with a fabrication strategy for the systematic exploration of rigid-deployable, compact and reconfigurable kirigami patterns.

We often encounter mental conflict in our lives. Such mental conflict has long been regarded as subjective. However, a machine learning method can be used to quantify the temporal dynamics of conflict between reward and curiosity from behavioral time-series.

A graph neural network — GAME-Net — has been developed to predict the adsorption energy of organic molecules on metal surfaces, which is a key descriptor of heterogeneous catalytic activity. This method allows for the study of large molecules derived from raw materials such as plastic waste, avoiding the use of costly and time-intensive first-principles simulations.

While digital twins have been recently used to represent cities and their physical structures, integrating complexity science into the digital twin approach will be key to deliver more explicable and trustworthy models and results.

This study proposes a diffusion model, ProteinSGM, for the design of novel protein folds. The designed proteins are diverse, experimentally stable and structurally consistent with predicted models

NeCLAS is a machine learning pipeline that can accurately and efficiently predict nanoscale interactions, which has broad applications in biological processes and material properties.

A disease space is constructed from clinical records by embedding all diseases and considering a patient’s space coordinates as a measure of their health state. This measure was associated with 108 genetic loci, on which models were built to predict various morbidities.

Mental conflict has been regarded as subjective instead of quantitative. This study developed a data-driven method to decode temporal dynamics of conflict between reward and curiosity, which can elucidate mechanisms of irrational decision-making.

GAME-Net is a graph deep learning model trained with small molecules containing a wide set of functional groups for predicting the adsorption energy of closed-shell organic molecules on metal surfaces, avoiding expensive density functional theory simulations.

Kirigami is an ancient art form that is now increasingly studied and applied in science and technology. This work presents an additive approach for the computational design of kirigami and two fabrication strategies for its physical instantiation.