Sodium compensation: a critical technology for transforming batteries from sodium-starved to sodium-rich systems.

Sodium-ion batteries (SIBs) have attracted wide attention from academia and industry due to the low cost and abundant sodium resources. Despite the rapid industrialization development of SIBs, it still faces problems such as a low initial coulombic efficiency (ICE) leading to a significant decrease in battery energy density (., 20%).

Defect engineering of air-stable LiFeO towards an ultra-high capacity cathode prelithiation additive.

Antifluorite-type LiFeO (LFO) belongs to a class of promising prelithiation materials for next-generation high-energy lithium-ion batteries. Unfortunately, the incomplete de-lithiation performance and inferior air stability hinder its application. In this work, ultra-high capacity is achieved by selective doping of Zr into the Fe sites (LFO-Zr) of LFO to form a large number of defects.

Drug-Target Binding Affinity Prediction in a Continuous Latent Space Using Variational Autoencoders.

Accurate prediction of Drug-Target binding Affinity (DTA) is a daunting yet pivotal task in the sphere of drug discovery. Over the years, a plethora of deep learning-based DTA models have emerged, rendering promising results in predicting the binding affinities between drugs and their target proteins. However, in contrast to the conventional approach of modeling binding affinity in vector spaces, we propose a more nuanced modeling process in a continuous space to account for the diversity of input samples.

Harmless pre-lithiation via advantageous surface reconstruction in sacrificial cathode additives for lithium-ion batteries.

Sacrificial cathode additives have emerged as a tempting strategy to compensate the initial capacity loss (ICL) in Li-ion batteries (LIBs) manufacturing. However, the utilization of sacrificial cathode additives inevitably brings residuals, side reactions, and negative impacts in which relevant researches are still in the early stage. In this study, we conduct a systematic investigation on the effects of employing a nickel-based sacrificial additive, LiCuNiO (LCNO), and propose a feasible strategy to achieve advantageous surface reconstruction on LCNO.

Multiple sequence alignment based on deep reinforcement learning with self-attention and positional encoding.

Motivation: Multiple sequence alignment (MSA) is one of the hotspots of current research and is commonly used in sequence analysis scenarios. However, there is no lasting solution for MSA because it is a Nondeterministic Polynomially complete problem, and the existing methods still have room to improve the accuracy.

Results: We propose Deep reinforcement learning with Positional encoding and self-Attention for MSA, based on deep reinforcement learning, to enhance the accuracy of the alignment Specifically, inspired by the translation technique in natural language processing, we introduce self-attention and positional encoding to improve accuracy and reliability.

Effect of the N,S-Codoped Carbon Layer on the Rate Performance of Air-Stable Lithium Iron Oxide Prelithiation Additives.

Lithium iron oxide (LiFeO, LFO) holds great promise in cathode prelithiation additives for lithium-ion batteries. However, it is hard to make full use of the power under high current rates due to its poor air stability and electronic conductivity. The carbon protective layer is an effective approach, and introducing heteroatoms would be beneficial to further improving Li kinetics.

DataDTA: a multi-feature and dual-interaction aggregation framework for drug-target binding affinity prediction.

Motivation: Accurate prediction of drug-target binding affinity (DTA) is crucial for drug discovery. The increase in the publication of large-scale DTA datasets enables the development of various computational methods for DTA prediction. Numerous deep learning-based methods have been proposed to predict affinities, some of which only utilize original sequence information or complex structures, but the effective combination of various information and protein-binding pockets have not been fully mined.

DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks.

Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug-Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations.

A fingerprints based molecular property prediction method using the BERT model.

Molecular property prediction (MPP) is vital in drug discovery and drug reposition. Deep learning-based MPP models capture molecular property-related features from various molecule representations. In this paper, we propose a molecule sequence embedding and prediction model facing with MPP task.

Modeling DTA by Combining Multiple-Instance Learning with a Private-Public Mechanism.

The prediction of the strengths of drug-target interactions, also called drug-target binding affinities (DTA), plays a fundamental role in facilitating drug discovery, where the goal is to find prospective drug candidates. With the increase in the number of drug-protein interactions, machine learning techniques, especially deep learning methods, have become applicable for drug-target interaction discovery because they significantly reduce the required experimental workload. In this paper, we present a spontaneous formulation of the DTA prediction problem as an instance of multi-instance learning.

Learning representations for gene ontology terms by jointly encoding graph structure and textual node descriptors.

Measuring the semantic similarity between Gene Ontology (GO) terms is a fundamental step in numerous functional bioinformatics applications. To fully exploit the metadata of GO terms, word embedding-based methods have been proposed recently to map GO terms to low-dimensional feature vectors. However, these representation methods commonly overlook the key information hidden in the whole GO structure and the relationship between GO terms.

A brief review of protein-ligand interaction prediction.

The task of identifying protein-ligand interactions (PLIs) plays a prominent role in the field of drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious experiments. There is a need to develop PLI computational prediction approaches to speed up the drug discovery process.

ELECTRA-DTA: a new compound-protein binding affinity prediction model based on the contextualized sequence encoding.

Motivation: Drug-target binding affinity (DTA) reflects the strength of the drug-target interaction; therefore, predicting the DTA can considerably benefit drug discovery by narrowing the search space and pruning drug-target (DT) pairs with low binding affinity scores. Representation learning using deep neural networks has achieved promising performance compared with traditional machine learning methods; hence, extensive research efforts have been made in learning the feature representation of proteins and compounds. However, such feature representation learning relies on a large-scale labelled dataset, which is not always available.