The authors introduce a method that combines physics and machine learning to design dynamic unstructured proteins with tunable ensemble properties like size, shape, sensing and binding.

The recent surge in generative AI has introduced exciting possibilities for computational chemistry. Generative AI methods have made significant pr...

We propose a novel framework that leverages large language models (LLMs) to transform synthetically infeasible inorganic crystal structures into synthetically feasible ones. Unlike previous studies on...

This Review summarizes recent technical advancements in generative AI, outlines how new models might improve healthcare and discusses validation approaches—using lessons from recent successes and failures in the field.

This study presents a generative artificial intelligence approach for the high-throughput discovery of antimicrobials against multidrug-resistant bacteria.

Characterization of atomic-scale materials traditionally requires human experts with months to years of specialized training. Even for trained human operators, accurate and reliable characterization r...

META-SiM brings foundation model power to single-molecule time traces, excelling across diverse analysis tasks. Paired with the web-based META-SiM Projector and entropy mapping, it rapidly reveals hidden molecular behaviors inaccessible by other means.

Simulated quantum annealing enhances probabilistic Ising Machines by enabling faster, more reliable solutions to complex optimization problems using interacting copies of the system guided by a time-d...

Heat recharges enzyme-free DNA circuits, enabling complex logic operations and neural networks to perform multiple computations, offering a universal energy source for molecular machines and advancing autonomous behaviours in artificial chemical systems.

Adaptive optical vision Fourier transformer (AOViFT) is a machine learning-based framework for accurately inferring aberrations and restoring diffraction-limited performance in diverse biological specimens.

The activity and stability of supported metal catalysts is in large part influenced by their interaction with the support. Now, neural network molecular dynamics simulations are combined with interpretable machine learning to reveal the governing factors of metal–support interactions for Pt nanoparticles on various oxide supports, identifying key features and proposing sinter-resistant supports.

A framework rooted in perturbation theory extends machine-learning interatomic potential approaches, capturing complex dynamics and energetics at electrified solid-liquid interfaces.

Multi-step planning is a challenge for LLMs. Here, the authors introduce a brain-inspired Modular Agentic Planner that decomposes planning into specialized LLM modules, improving performance across tasks and highlighting the value of cognitive neuroscience for LLM design.

Accurately solving the Schrödinger equation is challenging. Here, authors present QiankunNet, a Transformer-based framework that efficiently captures quantum correlations, achieving high accuracy in complex molecular systems using neural network quantum states.

Predictive chemistry often faces data scarcity, limiting the performance of machine learning (ML) models. This is particularly the case for specialized tasks such as reaction rate or selectivity predi...

YouTube video by Jorge Bravo Abad

An infrared metasurface combined with physics-informed AI enables sensing of nanoscale protein dynamics in solution.

YouTube video by FundacionAreces

Recent advances in quantum technologies have demonstrated that quantum systems can outperform classical ones in specific tasks, a concept known as quantum advantage. Although previous efforts have foc...

A low-cost robotic platform using mainly optical detection to quantify yields of products and by-products allows the analysis of multidimensional chemical reaction hyperspaces and networks much faster than is possible by human chemists.

FlexConsensus is a multi-autoencoder-based algorithm for merging different conformational landscapes from cryogenic electron microscopy heterogeneity analysis into a common latent space for the identification of similarities and differences among various methods. This helps in the validation of estimated conformational landscape and provides tools to streamline the heterogeneity workflow.

A small and fast diffusion model is presented, which is able to efficiently generate long protein backbones.