Prescriptions

Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting the repression of protein translation, and their dysregulation has been implicated in several pathologies. miR21 in particular has been implicated in tumorigenesis and anticancer drug resistance, making it a critical target for drug design. miR21 biogenesis involves precise biochemical pathways, incl...

Improving drug-target interaction prediction through dual-modality fusion with InteractNet.

In the drug discovery process, accurate prediction of drug-target interactions is crucial to accelerate the development of new drugs. However, existing methods still face many challenges in dealing with complex biomolecular interactions. To this end, we propose a new deep learning framework that combines the structural information and sequence features of proteins to provide comprehensive feature ...

Anchoring temporal convolutional networks for epileptic seizure prediction.

. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigate their impact or prevent them altogether. Current...

Graph-based machine learning model for weight prediction in protein-protein networks.

Proteins interact with each other in complex ways to perform significant biological functions. These interactions, known as protein-protein interactio...

Crowdsourcing Adverse Events Associated With Monoclonal Antibodies Targeting Calcitonin Gene-Related Peptide Signaling for Migraine Prevention: Natural Language Processing Analysis of Social Media.

BACKGROUND: Clinical trials demonstrate the efficacy and tolerability of medications targeting calcitonin gene-related peptide (CGRP) signaling for mi...

Molecular mechanism underlying effect of D93 and D289 protonation states on inhibitor-BACE1 binding: exploration from multiple independent Gaussian accelerated molecular dynamics and deep learning.

BACE1 has been regarded as an essential drug design target for treating Alzheimer's disease (AD). Multiple independent Gaussian accelerated molecular ...

Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction.

Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as bindi...

Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches.

Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each y...

Evaluating a generative artificial intelligence accuracy in providing medication instructions from smartphone images.

BACKGROUND: The Food and Drug Administration mandates patient labeling materials like the Medication Guide (MG) and Instructions for Use (IFU) to supp...

Unleashing the future: The revolutionary role of machine learning and artificial intelligence in drug discovery.

Drug discovery is a complex and multifaceted process aimed at identifying new therapeutic compounds with the potential to treat various diseases. Trad...

Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures.

Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-lig...

Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care.

Reporting of diagnostic nuclear images in clinical cancer management is generally qualitative. Theranostic treatment with Lu radioligands for prostate...

Optimizing anemia management using artificial intelligence for patients undergoing hemodialysis.

Patients with end-stage kidney disease (ESKD) frequently experience anemia, and maintaining hemoglobin (Hb) levels within a targeted range using eryth...

Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems.

Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Na...

Two-Stream Modality-Based Deep Learning Approach for Enhanced Two-Person Human Interaction Recognition in Videos.

Human interaction recognition (HIR) between two people in videos is a critical field in computer vision and pattern recognition, aimed at identifying ...

NFSA-DTI: A Novel Drug-Target Interaction Prediction Model Using Neural Fingerprint and Self-Attention Mechanism.

Existing deep learning methods have shown outstanding performance in predicting drug-target interactions. However, they still have limitations: (1) th...

Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications.

Diabetes management is often complicated by comorbidities, requiring complex medication regimens that increase the risk of drug-drug interactions (DDI...

A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases.

Skin diseases are a significant global public health concern, affecting 21-85% of the world's population, particularly those in low- and middle-income...

AlzyFinder: A Machine-Learning-Driven Platform for Ligand-Based Virtual Screening and Network Pharmacology.

Alzheimer's disease (AD), a prevalent neurodegenerative disorder, presents significant challenges in drug development due to its multifactorial nature...

Integrated Knowledge Graph and Drug Molecular Graph Fusion via Adversarial Networks for Drug-Drug Interaction Prediction.

The Co-administration of multiple drugs can enhance the efficacy of disease treatment by reducing drug resistance and side effects. However, it also r...