Prescriptions

Future prospective of AI in drug discovery.

Drug discovery and development is very expensive and long with an inferior success rate. It is quite inefficient and costly due to huge R&D costs and lower productivity in pharmaceutical industries, to discover effective drugs and their development. AI can revolutionize the history of drug discovery and development because it will solve all these problems. AI can identify some promising drug candi...

System level network data and models attack cancer drug resistance.

Drug resistance is responsible for >90% of cancer related deaths. Cancer drug resistance is a system level network phenomenon covering the entire cell. Small-scale interactomes and signalling network models of drug resistance guide directed drug development. Recently, proteome-wide human interactome and signalling network data have become available, which have been extended by drug-target interact...

Cross-ViT based benign and malignant classification of pulmonary nodules.

The benign and malignant discrimination of pulmonary nodules plays a very important role in diagnosing the extent of lung cancer lesions. There are ma...

GTIGNet: Global Topology Interaction Graphormer Network for 3D hand pose estimation.

Estimating 3D hand poses from monocular RGB images presents a series of challenges, including complex hand structures, self-occlusions, and depth ambi...

In-silico exploring pathway and mechanism-based therapeutics for allergic rhinitis: Network pharmacology, molecular docking, ADMET, quantum chemistry and machine learning based QSAR approaches.

Allergic rhinitis is a devastating health complication that interrupts the quality of daily life and significantly affects around 40 % of the populati...

Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles.

This paper investigates delivery of encapsulated drug from poly lactic-co-glycolic micro-/nano-particles. Experimental data collected from about 50 pa...

Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics.

Development of antibodies often begins with the assessment and optimization of their physicochemical properties, and their efficient engagement with t...

A Multi-View Feature-Based Interpretable Deep Learning Framework for Drug-Drug Interaction Prediction.

Drug-drug interactions (DDIs) can result in deleterious consequences when patients take multiple medications simultaneously, emphasizing the critical ...

Highly Sensitive and Interference-Free Detection of Multiple Drug Molecules in Serum Using Dual-Modified SERS Substrates Combined with AI Algorithm Analysis.

Surface-enhanced Raman spectroscopy (SERS) technology has shown broad potential in drug concentration detection, but its application in blood drug mon...

Machine Learning-Based Prediction of Drug Solubility in Lipidic Environments: The Sol_ME Tool for Optimizing Lipid-Based Formulations with a Preliminary Apalutamide Case Study.

Lipid-based formulations are essential for enhancing drug solubility and bioavailability, yet selecting optimal lipid excipients for specific drugs re...

Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial.

BACKGROUND: Clinical decision support systems leveraging artificial intelligence (AI) are increasingly integrated into health care practices, includin...

Interventions to improve medication adherence in persons with mental disorders.

PURPOSE OF REVIEW: Nonadherence to medication is prevalent in patients with mental illness. Various factors responsible for it. As a result, there are...

ExPDrug: Integration of an interpretable neural network and knowledge graph for pathway-based drug repurposing.

Precision medicine aims to provide personalized therapies by analyzing patient molecular profiles, often focusing on gene expression data. However, ef...

MutualDTA: An Interpretable Drug-Target Affinity Prediction Model Leveraging Pretrained Models and Mutual Attention.

Efficient and accurate drug-target affinity (DTA) prediction can significantly accelerate the drug development process. Recently, deep learning models...

Intricacies of human-AI interaction in dynamic decision-making for precision oncology.

AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals' cancer progress...

Adaptive Multi-Kernel Graph Neural Network for Drug-Drug Interaction Prediction.

 Combination therapy, which synergistically enhances treatment efficacy and inhibits disease progression through the combined effects of multiple drug...

Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach.

Efficient virtual screening methods can expedite drug discovery and facilitate the development of innovative therapeutics. This study presents a novel...

DO-GMA: An End-to-End Drug-Target Interaction Identification Framework with a Depthwise Overparameterized Convolutional Network and the Gated Multihead Attention Mechanism.

Identification of potential drug-target interactions (DTIs) is a crucial step in drug discovery and repurposing. Although deep learning effectively de...

Artificial intelligence-powered chatbots in search engines: a cross-sectional study on the quality and risks of drug information for patients.

BACKGROUND: Search engines often serve as a primary resource for patients to obtain drug information. However, the search engine market is rapidly cha...

Deep Learning of CYP450 Binding of Small Molecules by Quantum Information.

Drug-drug interaction can lead to diminished therapeutic effects or increased toxicity, posing significant risks, especially in polypharmacy, and cyto...