Datasets Comparison

Exploratory data domains for Alzheimer's Disease (AD) analysis are inherently complex due to the disease's polyetiological and polypathogenic nature. Addressing this complexity requires a multidisciplinary approach to data exploration. Effective drug discovery and repositioning strategies must account for the diversity of ligand–receptor interactions, aiming to expand the scope of the exploratory domain and identify therapeutic solutions targeting the most prevalent pathological features of AD. Considering the multifactorial characteristics of the disease, we propose a data retrieval and extraction strategy that integrates heterogeneous and relevant information from multiple bioinformatic databases, focusing on the key targets Acetylcholinesterase (AChE), Butyrylcholinesterase (BChE), and Beta-secretase 1 (BACE1). This strategy generates datasets that capture diverse facets of the disease's complexity, enabling comprehensive domain representations. To address the inherent challenges in the investigative process, we leveraged ChEMBL, ZINC, and the Protein Data Bank (PDB), producing an extensive and well-curated dataset that facilitates the analysis of causal relationships and reduces the complexity of AD-related research.

The steps to reproduce the process used to compose such a dataset are presented in "Neurodegenerative Disease Computer-Aided Drug Design Challenges: An Optimized Exploration of Data Domains with a Focus on Alzheimer's Disease and Polypharmacology Agents Researching," a paper published in Scientific Data - Nature.

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Neurodegenerative Disorder, Computer-Aided Drug Design, Alzheimer's Disease, Virtual Screening

Creative Commons Attribution 4.0 International

Exploratory data domains for Alzheimer's Disease (AD) analysis are inherently complex due to the disease's polyetiological and polypathogenic nature. Addressing this complexity requires a multidisciplinary approach to data exploration. Effective drug discovery and repositioning strategies must account for the diversity of ligand–receptor interactions, aiming to expand the scope of the exploratory domain and identify therapeutic solutions targeting the most prevalent pathological features of AD. Considering the multifactorial characteristics of the disease, we propose a data retrieval and extraction strategy that integrates heterogeneous and relevant information from multiple bioinformatic databases, focusing on the key targets Acetylcholinesterase (AChE), Butyrylcholinesterase (BChE), and Beta-secretase 1 (BACE1). This strategy generates datasets that capture diverse facets of the disease's complexity, enabling comprehensive domain representations. To address the inherent challenges in the investigative process, we leveraged ChEMBL, ZINC, and the Protein Data Bank (PDB), producing an extensive and well-curated dataset that facilitates the analysis of causal relationships and reduces the complexity of AD-related research.

The steps to reproduce the process used to compose such a dataset are presented in "BioMolExplorer: Enabling Multi-Source Data Integration for Structure- and Ligand-Based Virtual Screening," a paper currently under review in Journal of Cheminformatics - BMC: part of Springer Nature, and in the official GitHub repository of BioMolExplorer, as presented in this data description.

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Neurodegenerative Disorder, Computer-Aided Drug Design, Alzheimer's Disease, Virtual Screening

Creative Commons Attribution 4.0 International