AIMC Topic: Quantitative Structure-Activity Relationship

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QSAR-based prediction of acute inhalation toxicity and SHAP interpretability analysis of fluorocarbon environmental-friendly insulating gases.

Environmental research
Environmental-friendly fluorocarbon insulating gases have emerged as the preferred substitutes for SF due to their low global warming potential (GWP) and excellent insulation properties. However, current gases face challenges in meeting multiple perf...

Exploring entropy measures with topological indices on colorectal cancer drugs using curvilinear regression analysis and machine learning approaches.

PloS one
A topological index is a numerical value derived from the structure of a molecule or graph that provides useful information about the molecule's physical, chemical, or biological properties. These indices are especially important in chemo-informatics...

Machine learning-based QSAR and structure-based virtual screening guided discovery of novel mIDH1 inhibitors from natural products.

Journal of computer-aided molecular design
Mutations in isocitrate dehydrogenase 1 (IDH1) have been widely observed in various tumors, such as gliomas and acute myeloid leukemia, and therefore has become one of the current research focal points. Therefore, it is crucial to find inhibitors tha...

A computational study of cardiac glycosides from Vernonia amygdalina as PI3K inhibitors for targeting HER2 positive breast cancer.

Journal of computer-aided molecular design
The PI3K/Akt pathway plays a crucial role in regulating a broad network of proteins involved in the proliferation of HER2-positive breast cancer. The ethyl acetate fraction of Vernonia amygdalina, which contains cardiac glycosides, has been shown to ...

Ionic liquids and lysosomotropic detergents as inhibitors of the SARS-CoV-2 main protease: QSAR modeling, synthesis and biological testing.

Biochemical and biophysical research communications
SARS-CoV-2 infection is highly contagious, prompting the World Health Organization to classify it as a global public health emergency. The virus has numerous potential hosts, which complicates efforts for effective prevention, diagnosis, and treatmen...

Identification of natural food-derived emulsifiers using QSAR and machine learning: Application in dairy emulsions.

Food chemistry
Emulsifiers maintain the stability of emulsions, and milk protein-formed emulsions are unstable. Hence, efficient ways to screen food-derived compounds need to be identified. This study combined molecular descriptors with machine learning algorithms ...

QSAR Model Development for the Environmental Risk Limits and High-Risk List Identification of Phenylurea Herbicides in Aquatic Environments.

Journal of agricultural and food chemistry
Due to the extensive residues of phenylurea herbicides (PUHs) in the environment, it is important for the ecological risk assessment of PUHs to determine their environmental risk limits and identify the high-risk PUHs. This study derived the environm...

Machine learning-driven 3D-QSAR models facilitated rapid on-site broad-spectrum immunoassay of (fluoro)quinolones using evanescent wave fiber-embedded optofluidic biochip.

Biosensors & bioelectronics
(Fluoro)quinolones (FQs) pose significant threats to public health due to their widespread use and persistence in food and water sources. Given the extensive variety of FQs, testing each compound individually is prohibitively expensive and time-consu...

Machine Learning Based Quantitative Structure-Dissolution Profile Relationship.

Journal of chemical information and modeling
Determining accurate drug dissolution processes in the gastrointestinal tract is critical in drug discovery as dissolution profiles provide essential information for estimating the bioavailability of orally administered drugs. While various methods h...

Computational approaches in drug chemistry leveraging python powered QSPR study of antimalaria compounds by using artificial neural networks.

Scientific reports
The application of Machine Learning has become a revolutionary instrument in the domain of pharmaceutical research. Machine learning enables the modelling of Quantitative Structure Property Relationship, a crucial task in forecasting the physiochemic...