Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph.

The investigation of molecular interactions between ligands and their target molecules is becoming more significant as protein structure data continues to develop. In this study, we introduce PLA-STGCNnet, a deep fusion spatial-temporal graph neural network designed to study protein-ligand interactions based on the 3D structural data of protein-ligand complexes. Unlike 1D protein sequences or 2D ligand graphs, the 3D graph representation offers a more precise portrayal of the complex interactions between proteins and ligands.

Wfold: A new method for predicting RNA secondary structure with deep learning.

Precise estimations of RNA secondary structures have the potential to reveal the various roles that non-coding RNAs play in regulating cellular activity. However, the mainstay of traditional RNA secondary structure prediction methods relies on thermos-dynamic models via free energy minimization, a laborious process that requires a lot of prior knowledge. Here, RNA secondary structure prediction using Wfold, an end-to-end deep learning-based approach, is suggested.

Upscaling Brønsted acid intercalation and exfoliation of graphite into graphene by polyoxometalate clusters for sodium-ion battery application.

Non-oxidative intercalation of graphite avoids damage to graphene lattices and is a suitable method to produce high-quality graphene. However, the yield of exfoliated graphene is low in this process due to the poor delamination efficiency of guest species. In this study, a Brønsted acid intercalation protocol is developed involving polyoxometalate (POM) clusters (HPWO) as guests and intercalation of graphite is realized at the sub-nanometer scale.

Ultrastrong and ductile medium-entropy alloys via hierarchical ordering.

Long-range ordered phases in most high-entropy and medium-entropy alloys (HEAs/MEAs) exhibit poor ductility, stemming from their brittle nature of complex crystal structure with specific bonding state. Here, we propose a design strategy to severalfold strengthen a single-phase face-centered cubic (fcc) NiCoFeV MEA by introducing trigonal κ and cubic L1 intermetallic phases via hierarchical ordering. The tri-phase MEA has an ultrahigh tensile strength exceeding 1.

A Multi-view Molecular Pre-training with Generative Contrastive Learning.

Molecular representation learning can preserve meaningful molecular structures as embedding vectors, which is a necessary prerequisite for molecular property prediction. Yet, learning how to accurately represent molecules remains challenging. Previous approaches to learning molecular representations in an end-to-end manner potentially suffered information loss while neglecting the utilization of molecular generative representations.

Aqueous Developable and CO-Sourced Chemical Amplification Photoresist with High Performance.

Seeking high-performance photoresists is an important item for semiconductor industry due to the continuous miniaturization and intelligentization of integrated circuits. Polymer resin containing carbonate group has many desirable properties, such as high transmittance, acid sensitivity and chemical formulation, thus serving as promising photoresist material. In this work, a series of aqueous developable CO-sourced polycarbonates (CO-PCs) were produced via alternating copolymerization of CO and epoxides bearing acid-cleavable cyclic acetal groups in the presence of tetranuclear organoborane catalyst.

Corrigendum: Regional myocardial work measured by echocardiography for the detection of myocardial ischemic segments: a comparative study with invasive fractional flow reserve.

[This corrects the article DOI: 10.3389/fcvm.2022.

Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis.

The ordered assembly of Tau protein into filaments characterizes Alzheimer's and other neurodegenerative diseases, and thus, stabilization of Tau protein is a promising avenue for tauopathies therapy. To dissect the underlying aggregation mechanisms on Tau, we employ a set of molecular simulations and the Markov state model to determine the kinetics of ensemble of K18. K18 is the microtubule-binding domain of Tau protein and plays a vital role in the microtubule assembly, recycling processes, and amyloid fibril formation.

GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction.

Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable prediction approaches. In this study, we propose GraphsformerCPI, an end-to-end deep learning framework that improves prediction performance and interpretability.

INTransformer: Data augmentation-based contrastive learning by injecting noise into transformer for molecular property prediction.

Molecular property prediction plays an essential role in drug discovery for identifying the candidate molecules with target properties. Deep learning models usually require sufficient labeled data to train good prediction models. However, the size of labeled data is usually small for molecular property prediction, which brings great challenges to deep learning-based molecular property prediction methods.

Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction.

Accurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been successfully applied independently for protein-ligand binding affinity prediction. As local and global information of molecules are vital for protein-ligand binding affinity prediction, we aim to combine bi-directional gated recurrent unit (BiGRU) and convolutional neural network (CNN) to effectively capture both local and global molecular information.

Novel Structural Design and Adsorption/Insertion Coordinating Quasi-Metallic Na Storage Mechanism toward High-performance Hard Carbon Anode Derived from Carboxymethyl Cellulose.

Hard Carbon have become the most promising anode candidates for sodium-ion batteries, but the poor rate performance and cycle life remain key issues. In this work, N-doped hard carbon with abundant defects and expanded interlayer spacing is constructed by using carboxymethyl cellulose sodium as precursor with the assistance of graphitic carbon nitride. The formation of N-doped nanosheet structure is realized by the CN• or CC• radicals generated through the conversion of nitrile intermediates in the pyrolysis process.

Diffusion characteristics classification framework for identification of diffusion source in complex networks.

The diffusion phenomena taking place in complex networks are usually modelled as diffusion process, such as the diffusion of diseases, rumors and viruses. Identification of diffusion source is crucial for developing strategies to control these harmful diffusion processes. At present, accurately identifying the diffusion source is still an opening challenge.

EDC-DTI: An end-to-end deep collaborative learning model based on multiple information for drug-target interactions prediction.

Innovations in drug-target interactions (DTIs) prediction accelerate the progression of drug development. The introduction of deep learning models has a dramatic impact on DTIs prediction, with a distinct influence on saving time and money in drug discovery. This study develops an end-to-end deep collaborative learning model for DTIs prediction, called EDC-DTI, to identify new targets for existing drugs based on multiple drug-target-related information including homogeneous information and heterogeneous information by the way of deep learning.

NoiseMol: A noise-robusted data augmentation via perturbing noise for molecular property prediction.

Simplified Molecular-Input Line-Entry System (SMILES) is one of a widely used molecular representation methods for molecular property prediction. We conjecture that all the characters in the SMILES string of a molecule are essential for making up the molecules, but most of them make little contribution to determining a particular property of the molecule. Therefore, we verified the conjecture in the pre-experiment.

Clinical Outcomes of Cardiac Shockwave Therapy in Severe Coronary Artery Disease Patients after Coronary Artery Bypass Grafting.

Cardiac shockwave therapy (CSWT) is a noninvasive treatment for patients with refractory angina or myocardial ischemia. This study aims to evaluate the potential beneficial effect and safety of CSWT in patients with severe coronary artery disease (CAD) who have undergone coronary artery bypass grafting (CABG).This was a single-arm prospective cohort study.

Noninvasive assessment of myocardial work during left ventricular isovolumic relaxation in patients with diastolic dysfunction.

Background: This study aims to investigate the value of myocardial work (MW) parameters during the isovolumic relaxation (IVR) period in patients with left ventricular diastolic dysfunction (LVDD).

Methods: This study prospectively recruited 448 patients with risks for LVDD and 95 healthy subjects. An additional 42 patients with invasive measurements of left ventricular (LV) diastolic function were prospectively included.

TranGRU: focusing on both the local and global information of molecules for molecular property prediction.

Molecular property prediction is an essential but challenging task in drug discovery. The recurrent neural network (RNN) and Transformer are the mainstream methods for sequence modeling, and both have been successfully applied independently for molecular property prediction. As the local information and global information of molecules are very important for molecular properties, we aim to integrate the bi-directional gated recurrent unit (BiGRU) into the original Transformer encoder, together with self-attention to better capture local and global molecular information simultaneously.

MolRoPE-BERT: An enhanced molecular representation with Rotary Position Embedding for molecular property prediction.

Molecular property prediction is a significant task in drug discovery. Most deep learning-based computational methods either develop unique chemical representation or combine complex model. However, researchers are less concerned with the possible advantages of enormous quantities of unlabeled molecular data.

Quantum Chemical Calculations with Machine Learning for Multipolar Electrostatics Prediction in RNA: An Application to Pentose.

To develop a realistic electrostatic model that allows for the anisotropy of the atomic electron density, high-rank atomic multipole moments computed by quantum chemical calculations have been studied extensively. However, it is hard to process huge RNA systems only relying on quantum chemical calculations due to its highly computational cost. In this study, we employ five machine learning methods of Gaussian process regression with automatic relevance determination (ARDGPR), Kriging, radial basis function neural networks, Bagging, and generalized regression neural network to predict atomic multipole moments.

A deep learning method for predicting molecular properties and compound-protein interactions.

Predicting molecular properties and compound-protein interactions (CPIs) are two important areas of drug design and discovery. They are also an essential way to discover lead compounds in virtual screening. Recently, in silico methods based on deep learning have demonstrated excellent performance in various challenges.

MultiGran-SMILES: multi-granularity SMILES learning for molecular property prediction.

Motivation: Extracting useful molecular features is essential for molecular property prediction. Atom-level representation is a common representation of molecules, ignoring the sub-structure or branch information of molecules to some extent; however, it is vice versa for the substring-level representation. Both atom-level and substring-level representations may lose the neighborhood or spatial information of molecules.

The value of myocardial work in assessment of ventricular function in patients with non-obstructive hypertrophic cardiomyopathy.

Background: To evaluate myocardial work using speckle tracking echocardiography in patients with non-obstructive hypertrophic cardiomyopathy (HCM).

Methods: Fifty patients with HCM and 50 normal controls were included. Left ventricular ejection fraction (LVEF) was quantified using the bi-plane Simpson's method.