AIMC Topic: Quantitative Structure-Activity Relationship

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Uncovering blood-brain barrier permeability: a comparative study of machine learning models using molecular fingerprints, and SHAP explainability.

SAR and QSAR in environmental research
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
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
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
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...

HDAC3_VS_assistant: cheminformatics-driven discovery of histone deacetylase 3 inhibitors.

Molecular diversity
Histone deacetylase 3 (HDAC3) inhibitors keep significant therapeutic promise for treating oncological, neurodegenerative, and inflammatory diseases. In this work, we developed robust QSAR regression models for HDAC3 inhibitory activity and acute tox...

Advanced Mass-Spectra-Based Machine Learning for Predicting the Toxicity of Traditional Chinese Medicines.

Analytical chemistry
Traditional Chinese medicine (TCM) has been a cornerstone of health care for centuries, valued for its preventive and therapeutic properties. However, recent decades have revealed significant toxicological concerns associated with TCMs due to their c...

AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity.

Molecular cancer
BACKGROUND: Immunogenic cell death (ICD) inducers are often identified in phenotypic screening campaigns by the release or surface exposure of various danger-associated molecular patterns (DAMPs) from malignant cells. This study aimed to streamline t...

Enhanced prediction of beta-secretase inhibitory compounds with mol2vec technique and machine learning algorithms.

SAR and QSAR in environmental research
A comprehensive computational strategy that combined QSAR modelling, molecular docking, and ADMET analysis was used to discover potential inhibitors for β-secretase 1 (BACE-1). A dataset of 1,138 compounds with established BACE-1 inhibitory activitie...

Cyto-Safe: A Machine Learning Tool for Early Identification of Cytotoxic Compounds in Drug Discovery.

Journal of chemical information and modeling
Cytotoxicity is essential in drug discovery, enabling early evaluation of toxic compounds during screenings to minimize toxicological risks. assays support high-throughput screening, allowing for efficient detection of toxic substances while conside...

Integrating traditional QSAR and read-across-based regression models for predicting potential anti-leishmanial azole compounds.

Molecular diversity
Leishmaniasis, a neglected tropical disease caused by various Leishmania species, poses a significant global health challenge, especially in resource-limited regions. Visceral Leishmaniasis (VL) stands out among its severe manifestations, and current...