Volume 5 Issue 9, September 2023

With the advances in neural language models, the question arises if some models align better with human processing than others. Golan et al. identify sentences that language models disagree about and use them to compare the shortcomings of different language models.

Tandem mass spectroscopy is a useful tool to identify metabolites but is limited by the capability of computational methods to annotate peaks with chemical structures when spectra are dissimilar to previously observed spectra. Goldman and colleagues use a transformer-based method to annotate chemical structure fragments, thereby incorporating domain insights into its architecture, and to simultaneously predict the structure of the metabolite and its fragments from the spectrum.

The heterogeneous and compartmentalized environments within living cells make it difficult to deploy theranostic agents with precise spatiotemporal accuracy. Zhao et al. demonstrate a DNA framework state machine that can switch among multiple structural states according to the temporal sequence of molecular cues, enabling temporally controlled CRISPR–Cas9 targeting in living mammalian cells.

Achieving sequential robotic actions involving different manipulation skills is an open challenge that is critical to enable robots to interact meaningfully with their physical environment. Triantafyllidis and colleagues present a hierarchical learning framework based on an ensemble of specialized neural networks to solve complex long-horizon manipulation tasks.

Local methods of explainable artificial intelligence identify where important features or inputs occur, while global methods try to understand what features or concepts have been learned by a model. The authors propose a concept-level explanation method that bridges the local and global perspectives, enabling more comprehensive and human-understandable explanations.

Generating novel molecules that bind to specific protein targets is a challenging but important task in computational drug design. Zhang and colleagues present a molecular generation method based on hierarchical auto-regression.

An outstanding challenge in materials science is doing large-scale simulations with complex electron interactions. Deng and colleagues introduce a universal graph neural network-based interatomic potential integrating atomic magnetic moments as charge constraints, which allows for capturing subtle chemical properties in several lithium-based solid-state materials

For virtual protein docking, an accurate scoring function is necessary that evaluates how likely a protein conformation is. Stebliankin and colleagues present a method based on vision transformers that provides a more accurate score by evaluating individual binding interfaces as multi-channel images.