AIMC Topic: Quantitative Structure-Activity Relationship

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Molecular mechanism underlying effect of D93 and D289 protonation states on inhibitor-BACE1 binding: exploration from multiple independent Gaussian accelerated molecular dynamics and deep learning.

SAR and QSAR in environmental research
BACE1 has been regarded as an essential drug design target for treating Alzheimer's disease (AD). Multiple independent Gaussian accelerated molecular dynamics simulations (GaMD), deep learning (DL), and molecular mechanics general Born surface area (...

Small molecule inhibitors of IL-1R1/IL-1β interaction identified via transfer machine learning QSAR modelling.

International journal of biological macromolecules
The human interleukin-1 receptor I (IL-1R1) is a cytokine receptor recognized by interleukin 1β (IL-1β), among other cytokines. Over activation of IL-1R1 has been implicated in various inflammatory conditions. This research aims to identify small-mol...

Using the super-learner to predict the chemical acute toxicity on rats.

Journal of hazardous materials
With the rapid increase in the number of commercial chemicals, testing methods regarding on median lethal dose (LD) relying animal experiments face challenges such as high costs and ethical concerns. Classical quantitative structure-activity relation...

QSPR modeling to predict surface tension of psychoanaleptic drugs using the hybrid DA-SVR algorithm.

Journal of molecular graphics & modelling
A robust Quantitative Structure-Property Relationship (QSPR) model was presented to predict the surface tension property of psychoanaleptic (psychostimulant and antidepressant) drugs. A dataset of 112 molecules was utilized, and three feature selecti...

Machine Learning-Based Prediction of the Inhibitory Activity of Chemical Substances Against Rat and Human Cytochrome P450s.

Chemical research in toxicology
The prediction of cytochrome P450 inhibition by a computational (quantitative) structure-activity relationship approach using chemical structure information and machine learning would be useful for toxicity research as a simple and rapid tool. Howev...

Chemoinformatics for corrosion science: Data-driven modeling of corrosion inhibition by organic molecules.

Molecular informatics
This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure-property relationships (QSPR) and related data-driven approaches. The characterist...

Integration of 3D-QSAR, molecular docking, and machine learning techniques for rational design of nicotinamide-based SIRT2 inhibitors.

Computational biology and chemistry
Selective inhibitors of sirtuin-2 (SIRT2) are increasingly recognized as potential therapeutics for cancer and neurodegenerative diseases. Derivatives of 5-((3-amidobenzyl)oxy)nicotinamides have been identified as some of the most potent and selectiv...

Deciphering Cathepsin K inhibitors: a combined QSAR, docking and MD simulation based machine learning approaches for drug design.

SAR and QSAR in environmental research
Cathepsin K (CatK), a lysosomal cysteine protease, contributes to skeletal abnormalities, heart diseases, lung inflammation, and central nervous system and immune disorders. Currently, CatK inhibitors are associated with severe adverse effects, there...

Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach.

Computers in biology and medicine
Drug-induced Torsade de Pointes (TdP), a life-threatening polymorphic ventricular tachyarrhythmia, emerges due to the cardiotoxic effects of pharmaceuticals. The need for precise mechanisms and clinical biomarkers to detect this adverse effect presen...

FGTN: Fragment-based graph transformer network for predicting reproductive toxicity.

Archives of toxicology
Reproductive toxicity is one of the important issues in chemical safety. Traditional laboratory testing methods are costly and time-consuming with raised ethical issues. Only a few in silico models have been reported to predict human reproductive tox...