Predicting protein–membrane interactions is a formidable challenge due to the subtle physicochemical features that distinguish membrane-binding regions of a protein surface as well as the scarcity of...

Polyethylene terephthalate (PET) is a widely used plastic due to its durability and adaptability; however, its resistance to natural degradation has led to severe accumulation in the environment....

Metabolic pathway design is a fundamental aspect of metabolic engineering, playing a crucial role in the microbial synthesis of high-value compounds. While metabolic engineers recognize the prevale...

In this study, we utilized a high-performance soaking system of protein kinase A (PKA) to perform a crystallographic screening of a natural product-like fragment library. We resolved 36 fragment-bo...

We validate semiempirical sTDA-xTB and sTD-DFT-xTB methods for high-throughput screening of thermally activated delayed fluorescence (TADF) emitters using 747 experimentally characterized molecules...

The introduction of chemical fragment spaces as a way to model large chemical spaces led to readily available compound libraries several orders of magnitude larger than seen before. The possibility...

The major histocompatibility complex class I-related protein 1 (MR1) is an antigen-presenting protein that binds its ligand in the endoplasmic reticulum and presents the resulting complex on the cell...

With the surge in QSAR model development, concerns about evaluation rigor, particularly regarding the influence of data splitting, have grown. Using five data sets of various sizes, we systematically...

The atomic-scale mechanisms of asphalt oxidative aging remain poorly understood due to the chemical complexity of asphalt and limitations of conventional methods. Herein, we develop a reactive neural...

Coarse-grained (CG) modeling enables molecular simulations to reach time and length scales inaccessible to fully atomistic methods. For classical CG models, the choice of mapping, that is, how atoms...

Identifying transition states (TSs), the high-energy configurations that molecules pass through during chemical reactions, is essential for understanding and designing chemical processes. However,...

Ionizable lipids are fundamental to the efficacy of lipid nanoparticles (LNPs) in pivotal areas including mRNA vaccines. Their development, however, is hindered by intricate structure–property...

Discovering novel molecules within the vast chemical space is a central scientific challenge, increasingly delegated to deep generative models. However, the prevailing “black box” paradigm, built on...

Classical protein–ligand docking has been a cornerstone technique in computational drug discovery for decades but has reached an accuracy and performance plateau. Recently introduced Machine Learning...

Because of their ultrahigh porosity and tunable chemistry, metal–organic frameworks (MOFs) have emerged as leading candidates for gas adsorption applications. Nevertheless, their combinatorial nature...

Reliably evaluating the most stable conformations of charged small organic molecules in solution poses a major challenge to computational chemistry, due to limitations in the accuracy or efficiency...

Structure relaxation plays a crucial role in atomic simulation and materials modeling, yet traditional first-principles approaches remain computationally expensive and, therefore, difficult to scale...

Data-driven machine learning (ML) technologies have become increasingly prevalent in the prediction of the optical properties of fluorescent dyes, especially across diverse solvent environments─a key...

Designing descriptors for multiple defects in two-dimensional materials is challenging due to the diverse local atomic environments created by different defect types and arrangements. Existing...

Accurate and rapid prediction of protein–ligand binding affinities is critical for drug discovery, particularly when evaluating large chemical libraries or new drug molecules from high-throughput...