AI Medical Compendium Journal:
Current computer-aided drug design

Showing 1 to 10 of 23 articles

DeepTransformer: Node Classification Research of a Deep Graph Network on an Osteoporosis Graph based on GraphTransformer.

Current computer-aided drug design
BACKGROUND: Osteoporosis (OP) is one of the most common diseases in the elderly population. It is mostly treated with medication, but drug research and development have the disadvantage of taking a long time and having a high cost.

Exploring the Mechanisms of Sanguinarine in the Treatment of Osteoporosis by Integrating Network Pharmacology Analysis and Deep Learning Technology.

Current computer-aided drug design
BACKGROUND: Sanguinarine (SAN) has been reported to have antioxidant, antiinflammatory, and antimicrobial activities with potential for the treatment of osteoporosis (OP).

Detection of Brain Tumor Employing Residual Network-based Optimized Deep Learning.

Current computer-aided drug design
BACKGROUND: Diagnosis and treatment planning play a very vital role in improving the survival of oncological patients. However, there is high variability in the shape, size, and structure of the tumor, making automatic segmentation difficult. The aut...

A Novel Deep Learning Model for Drug-drug Interactions.

Current computer-aided drug design
INTRODUCTION: Drug-drug interactions (DDIs) can lead to adverse events and compromised treatment efficacy that emphasize the need for accurate prediction and understanding of these interactions.

The Diagnostic Features of Peripheral Blood Biomarkers in Identifying Osteoarthritis Individuals: Machine Learning Strategies and Clinical Evidence.

Current computer-aided drug design
BACKGROUND: People with osteoarthritis place a huge burden on society. Early diagnosis is essential to prevent disease progression and to select the best treatment strategy more effectively. In this study, the aim was to examine the diagnostic featur...

Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding.

Current computer-aided drug design
BACKGROUND: In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topol...

Predicting Antitumor Activity of Anthrapyrazole Derivatives using Advanced Machine Learning Techniques.

Current computer-aided drug design
BACKGROUND: Anthrapyrazoles are a new class of antitumor agents and successors to anthracyclines possessing a broad range of antitumor activity in various model tumors.

A Comparative Analytical Review on Machine Learning Methods in Drugtarget Interactions Prediction.

Current computer-aided drug design
BACKGROUND: Predicting drug-target interactions (DTIs) is an important topic of study in the field of drug discovery and development. Since DTI prediction in vitro studies is very expensive and time-consuming, computational techniques for predict...

Relevance of Machine Learning to Predict the Inhibitory Activity of Small Thiazole Chemicals on Estrogen Receptor.

Current computer-aided drug design
BACKGROUND: Drug discovery requires the use of hybrid technologies for the discovery of new chemical substances. One of those interesting strategies is QSAR via applying an artificial intelligence system that effectively predicts how chemical alterat...