AI Medical Compendium Journal:
Chemical biology & drug design

Showing 11 to 20 of 30 articles

Bayesian machine learning to discover Bruton's tyrosine kinase inhibitors.

Chemical biology & drug design
Bruton's tyrosine kinase (BTK) has a crucial role in multiple cell signaling pathways including B-cell antigen receptor (BCR) and Fc receptor (FcR) signaling cascades, which has attracted much attention to find BTK inhibitors to treat autoimmune dise...

Applications of machine-learning methods for the discovery of NDM-1 inhibitors.

Chemical biology & drug design
The emergence of New Delhi metal beta-lactamase (NDM-1)-producing bacteria and their worldwide spread pose great challenges for the treatment of drug-resistant bacterial infections. These bacteria can hydrolyze most β-lactam antibacterials. Unfortuna...

Common cancer biomarkers of breast and ovarian types identified through artificial intelligence.

Chemical biology & drug design
Biomarkers can offer great promise for improving prevention and treatment of complex diseases such as cancer, cardiovascular diseases, and diabetes. These can be used as either diagnostic or predictive or as prognostic biomarkers. The revolution brou...

A combined drug discovery strategy based on machine learning and molecular docking.

Chemical biology & drug design
Data mining methods based on machine learning play an increasingly important role in drug design and discovery. In the current work, eight machine learning methods including decision trees, k-Nearest neighbor, support vector machines, random forests,...

ChemSuite: A package for chemoinformatics calculations and machine learning.

Chemical biology & drug design
Prediction of biological and toxicological properties of small molecules using in silico approaches has become a wide practice in pharmaceutical research to lessen the cost and enhance productivity. The development of a tool "ChemSuite," a stand-alon...

SAR study on inhibitors of GIIA secreted phospholipase A using machine learning methods.

Chemical biology & drug design
GIIA secreted phospholipase A (GIIA sPLA ) is a potent target for drug discovery. To distinguish the activity level of the inhibitors of GIIA sPLA , we built 24 classification models by three machine learning algorithms including support vector machi...

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery.

Chemical biology & drug design
In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, includin...

Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase.

Chemical biology & drug design
In this study, we describe the development of new machine learning models to predict inhibition of the enzyme 3-dehydroquinate dehydratase (DHQD). This enzyme is the third step of the shikimate pathway and is responsible for the synthesis of chorisma...

NAD_MCNN: Combining Protein Language Models and Multiwindow Convolutional Neural Networks for Deacetylase NAD+ Binding Site Prediction.

Chemical biology & drug design
Sirtuins, a class of NAD+ -dependent deacetylases, play a key role in aging, metabolism, and longevity. Their interaction with NAD+ at the catalytic site is crucial for function, but experimental methods to map NAD+ binding sites are time consuming. ...

Machine Learning-Based Discovery of a Novel Noncovalent MurA Inhibitor as an Antibacterial Agent.

Chemical biology & drug design
The bacterial cell wall is crucial for maintaining the integrity of bacterial cells. UDP-N-acetylglucosamine 1-carboxyethylene transferase (MurA) is an important enzyme involved in bacterial cell wall synthesis. Therefore, it is an important target f...