AIMC Topic: Quantitative Structure-Activity Relationship
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Machine Learning Tool for New Selective Serotonin and Serotonin-Norepinephrine Reuptake Inhibitors.
Molecules (Basel, Switzerland)
Jan 31, 2025
Depression, a serious mood disorder, affects about 5% of the population. Currently, there are two groups of antidepressants that are the first-line treatment for depressive disorder: selective serotonin reuptake inhibitors and serotonin-norepinephrin...
On QSPR analysis of glaucoma drugs using machine learning with XGBoost and regression models.
Computers in biology and medicine
Jan 28, 2025
Glaucoma is an irreversible, progressive, degenerative eye disorder arising because of increased intraocular pressure, resulting in eventual vision loss if untreated. The QSPR relates, mathematically, by employing various algorithms, a specified prop...
Essential Oils as Antimicrobials against : Experimental and Literature Data to Definite Predictive Quantitative Composition-Activity Relationship Models Using Machine Learning Algorithms.
Journal of chemical information and modeling
Jan 22, 2025
Essential oils (EOs) exhibit a broad spectrum of biological activities; however, their clinical application is hindered by challenges, such as variability in chemical composition and chemical/physical instability. A critical limitation is the lack of...
Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model.
Scientific reports
Jan 20, 2025
Human organic cation transporter 2 (hOCT2/SLC22A2) is a key drug transporter that facilitates the transport of endogenous and exogenous organic cations. Because hOCT2 is responsible for the development of adverse effects caused by platinum-based anti...
A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors.
Scientific reports
Jan 11, 2025
In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure...
Predicting purification process fit of monoclonal antibodies using machine learning.
mAbs
Jan 9, 2025
In early-stage development of therapeutic monoclonal antibodies, assessment of the viability and ease of their purification typically requires extensive experimentation. However, the work required for upstream protein expression and downstream purifi...
Uncovering blood-brain barrier permeability: a comparative study of machine learning models using molecular fingerprints, and SHAP explainability.
SAR and QSAR in environmental research
Jan 8, 2025
This study illustrates the use of chemical fingerprints with machine learning for blood-brain barrier (BBB) permeability prediction. Employing the Blood Brain Barrier Database (B3DB) dataset for BBB permeability prediction, we extracted nine differen...
Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs.
Scientific reports
Jan 4, 2025
We have adopted the classification Read-Across Structure-Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally ac...
Unveiling key drivers of hepatocellular carcinoma: a synergistic approach with network pharmacology, machine learning-driven ligand discovery and dynamic simulations.
SAR and QSAR in environmental research
Jan 3, 2025
Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. This study aims to uncover the genes and pathways involved in HCC through network pharmacology (NP) and to discover potential drugs via machine learning (ML)-based lig...
Harnessing the Power of Machine Learning Guided Discovery of NLRP3 Inhibitors Towards the Effective Treatment of Rheumatoid Arthritis.
Cells
Dec 30, 2024
The NLRP3 inflammasome, plays a critical role in the pathogenesis of rheumatoid arthritis (RA) by activating inflammatory cytokines such as IL1β and IL18. Targeting NLRP3 has emerged as a promising therapeutic strategy for RA. In this study, a multid...
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