Prescriptions

Add-on treatment with Cerebrolysin improves clinical symptoms in patients with ALS: results from a prospective, single-center, placebo-controlled, randomized, double-blind, phase II study.

Amyotrophic lateral sclerosis (ALS) is a devastating and progressive neurodegenerative disease with limited treatment options available. Cerebrolysin is a drug candidate for the treatment of ALS because of its neuroprotective and neuroregenerative effects. We initiated a pilot clinical study of a combination of Cerebrolysin and riluzole to assess the therapeutic benefit of Cerebrolysin as an add-o...

BatmanNet: bi-branch masked graph transformer autoencoder for molecular representation.

Although substantial efforts have been made using graph neural networks (GNNs) for artificial intelligence (AI)-driven drug discovery, effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules. Recent studies suggest that big GNN models pre-trained by self-supervised learning on unlabeled datasets enable better transfer performa...

Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites.

The coronavirus disease 2019 (COVID-19) pandemic has spurred a wide range of approaches to control and combat the disease. However, selecting an effec...

H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA.

2'-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate ...

Attention is all you need: utilizing attention in AI-enabled drug discovery.

Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpr...

Reply to "Artificial intelligence's potential in tailoring prescription of biological therapy for chronic rhinosinusitis".

HTCL-DDI: a hierarchical triple-view contrastive learning framework for drug-drug interaction prediction.

Drug-drug interaction (DDI) prediction can discover potential risks of drug combinations in advance by detecting drug pairs that are likely to interac...

Spatom: a graph neural network for structure-based protein-protein interaction site prediction.

Accurate identification of protein-protein interaction (PPI) sites remains a computational challenge. We propose Spatom, a novel framework for PPI sit...

Protein-protein interaction and site prediction using transfer learning.

The advanced language models have enabled us to recognize protein-protein interactions (PPIs) and interaction sites using protein sequences or structu...

DMFDDI: deep multimodal fusion for drug-drug interaction prediction.

Drug combination therapy has gradually become a promising treatment strategy for complex or co-existing diseases. As drug-drug interactions (DDIs) may...

FG-BERT: a generalized and self-supervised functional group-based molecular representation learning framework for properties prediction.

Artificial intelligence-based molecular property prediction plays a key role in molecular design such as bioactive molecules and functional materials....

MSDRP: a deep learning model based on multisource data for predicting drug response.

MOTIVATION: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate pers...

Systematic Evaluation of a Knee Exoskeleton Misalignment Compensation Mechanism Using a Robotic Dummy Leg.

The objective and quantitative assessment of physical human-exoskeletons interaction (pHEI) represents a pressing necessity in the wearable robots fie...

Customized Series Elastic Actuator for a Safe and Compliant Human-Robot Interaction: Design and Characterization.

When it comes to robotic-mediated rehabilitation it is mandatory to design a system that guarantees a safe and compliant human-machine interaction. De...

Development and Validation of a Flexible Sensing Array for Placement within the Physical Human-Exoskeleton Interface.

Monitoring the human-exoskeleton interface (HEI) is vital for user safety in assistive exoskeletons. Considering interaction forces during design can ...

Visualization design of health detection products based on human-computer interaction experience in intelligent decision support systems.

In order to meet the needs of the human-computer interaction experience of health testing products and improve the decision-making efficiency of intel...

Design of synthetic promoters for cyanobacteria with generative deep-learning model.

Deep generative models, which can approximate complex data distribution from large datasets, are widely used in biological dataset analysis. In partic...

Attention-based cross domain graph neural network for prediction of drug-drug interactions.

Drug-drug interactions (DDI) may lead to adverse reactions in human body and accurate prediction of DDI can mitigate the medical risk. Currently, most...

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA.

Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets...

MCFF-MTDDI: multi-channel feature fusion for multi-typed drug-drug interaction prediction.

Adverse drug-drug interactions (DDIs) have become an increasingly serious problem in the medical and health system. Recently, the effective applicatio...