AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Drug Development

Showing 91 to 100 of 297 articles

Clear Filters

DAHNGC: A Graph Convolution Model for Drug-Disease Association Prediction by Using Heterogeneous Network.

Journal of computational biology : a journal of computational molecular cell biology
In the field of drug development and repositioning, the prediction of drug-disease associations is a critical task. A recently proposed method for predicting drug-disease associations based on graph convolution relies heavily on the features of adjac...

FDA Modernization Act 2.0: An insight from nondeveloping country.

Drug development research
Animal testing is required in drug development research and is crucial for assessing the efficacy and safety of medications before they are commercialized. However, the newly furnished Food and Drug Administration Modernization Act 2.0 has given new ...

Organ-on-a-chip meets artificial intelligence in drug evaluation.

Theranostics
Drug evaluation has always been an important area of research in the pharmaceutical industry. However, animal welfare protection and other shortcomings of traditional drug development models pose obstacles and challenges to drug evaluation. Organ-on-...

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science.

Journal of biomolecular structure & dynamics
In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science a...

ADis-QSAR: a machine learning model based on biological activity differences of compounds.

Journal of computer-aided molecular design
Drug candidates identified by the pharmaceutical industry typically have unique structural characteristics to ensure they interact strongly and specifically with their biological targets. Identifying these characteristics is a key challenge for devel...

DEDTI versus IEDTI: efficient and predictive models of drug-target interactions.

Scientific reports
Drug repurposing is an active area of research that aims to decrease the cost and time of drug development. Most of those efforts are primarily concerned with the prediction of drug-target interactions. Many evaluation models, from matrix factorizati...

Development of a 2D-QSAR Model for Tissue-to-Plasma Partition Coefficient Value with High Accuracy Using Machine Learning Method, Minimum Required Experimental Values, and Physicochemical Descriptors.

European journal of drug metabolism and pharmacokinetics
BACKGROUND: The demand for physiologically based pharmacokinetic (PBPK) model is increasing currently. New drug application (NDA) of many compounds is submitted with PBPK models for efficient drug development. Tissue-to-plasma partition coefficient (...

DrugormerDTI: Drug Graphormer for drug-target interaction prediction.

Computers in biology and medicine
Drug-target interactions (DTI) prediction is a crucial task in drug discovery. Existing computational methods accelerate the drug discovery in this respect. However, most of them suffer from low feature representation ability, significantly affecting...

Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples.

IEEE/ACM transactions on computational biology and bioinformatics
Many high-performance DTA deep learning models have been proposed, but they are mostly black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA models more trustworthy, and allows to distill biological knowledge from the mode...

AttentionDTA: Drug-Target Binding Affinity Prediction by Sequence-Based Deep Learning With Attention Mechanism.

IEEE/ACM transactions on computational biology and bioinformatics
The identification of drug-target relations (DTRs) is substantial in drug development. A large number of methods treat DTRs as drug-target interactions (DTIs), a binary classification problem. The main drawback of these methods are the lack of reliab...