Computer Aided Drug Design Methodologies with Natural Products in the
Drug Research Against Alzheimer’s Disease

Sousa, Natália Ferreira de; Scotti, Luciana; Moura, Érika Paiva de; A, Mayara Dos Santos Maia; Rodrigues, Gabriela Cristina Soares; Medeiros, Herbert Igor Rodrigues de; Lopes, Simone Mendes; Scotti, Marcus Tullius

doi:10.2174/1570159x19666211005145952

Cited by 11 publications

(5 citation statements)

References 139 publications

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“…MVD is the fast and flexible docking program that gives the most likely ligand binding conformation to a macromolecule. 6 More than a hundred compounds were selected in search of new ligands for the Tubulin protein-PDB: 1SA0, Anaplastic lymphoma kinase (ALK)-PDB:6MX8, Estrogen Receptor (ER)-PDB:3ERT and Epidermal Growth Factor Receptor(EGFR)-PDB:1M17 as an anticancer drug like candidate. The 3D structures of drugs and other chemical structures were downloaded from the PubChem chemical database and drug bank, drawn using the Chem Draw software, and saved in the mol format after energy minimization.…”

Section: Structure-based Drug Design and Molecular Docking Studiesmentioning

confidence: 99%

In-silico and In-vitro Studies on Targeting Tumor Apoptosis by Activating Caspase-3

Sowjanya,

Kumar

2023

IJPQA

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Background: Despite the availability of numerous treatment options, cancer remains the primary cause of death in the current decade. The present study integrates computational and experimental methods to identify and develop a novel promising compound with anticancer properties. The study aims to evaluate the potential anticancer properties of selected molecules based on docking score, compliance with Lipinski’s rule of five, and in-silico safety prediction. Structure drug design by molecular docking methods was used to explore ligand conformations at various target binding sites. The in-silico physicochemical, pharmacokinetic, and toxicology properties of designed molecules were predicted using online software. Further, in-vitro, anticancer properties were studied by MTT Assay and flow cytometry. Results: 2-Butyl-3- (3, 5-diiodo-4- hydroxybenzoyl) benzofuran is one of the selected ligands showed potent anticancer activity against HT- 29 (Human Colon Cancer) cells and A549 (Human lung cancer) at IC50 concentrations. Further apoptosis studies demonstrated the possible mode of action against HT-29 cells. The proposed mechanism of action may be caspase-3 activation. As a consequence of caspase-3 activation, enzymes such as DNase are activated, leading to DNA fragmentation and apoptosis, and DNA fragmentation was 6.5 times greater than in untreated cells. The IC50 concentration of 2-Butyl-3- (3, 5-diiodo-4- hydroxybenzoyl) benzofuran inhibited 50% of HT-29 cells during G0/G1 phase. Conclusion: This research contributes to the burgeoning field of integrated in-vitro and in-silico analysis of biological systems, which can be used to study complicated cancer cell population dynamics. Future studies should investigate the impact on Human lung cancer cells.

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Section: Structure-based Drug Design and Molecular Docking Studiesmentioning

confidence: 99%

In-silico and In-vitro Studies on Targeting Tumor Apoptosis by Activating Caspase-3

Sowjanya,

Kumar

2023

IJPQA

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“…LB-CADD methods rather than SB-CADD procedures can also be used when the biologic target’s structure is unknown. Additionally, ligand-based virtual high-throughput screening (LB-vHTS) techniques frequently identify dynamic mixtures that are more potent than those identified by SB–vHTS [ 55 , 56 , 57 ].…”

Section: Computer-aided Drug Discovery and Designmentioning

confidence: 99%

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

et al. 2022

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Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

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“…Currently, computer methodologies are often used to evaluate the physicochemical and biochemical behavior of various substances [1][2][3][4][5][6][7][8][9][10]. The possibility of creating and distributing databases and software largely determines the semantic framework of modern scientific research activities.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure–Activity Relationship Models for the Angiotensin-Converting Enzyme Inhibitory Activities of Short-Chain Peptides of Goat Milk Using Quasi-SMILES

Toropova,

Toropov,

Roncaglioni

et al. 2024

Macromol

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The inhibitory activity of peptides on angiotensin-converting enzyme (ACE) is a measure of their antihypertensive potential. Quantitative structure–activity relationship (QSAR) models obtained based on the analysis of sequences of amino acids are suggested. The average determination coefficient for the active training sets is 0.36 ± 0.07. The average determination coefficient for validation sets is 0.79 ± 0.02. The paradoxical situation is caused by applying the vector of ideality of correlation, which improves the statistical quality of a model for the calibration and validation sets but is detrimental to the statistical quality of models for the training sets.

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Computer Aided Drug Design Methodologies with Natural Products in the Drug Research Against Alzheimer’s Disease

Cited by 11 publications

References 139 publications

In-silico and In-vitro Studies on Targeting Tumor Apoptosis by Activating Caspase-3

In-silico and In-vitro Studies on Targeting Tumor Apoptosis by Activating Caspase-3

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Quantitative Structure–Activity Relationship Models for the Angiotensin-Converting Enzyme Inhibitory Activities of Short-Chain Peptides of Goat Milk Using Quasi-SMILES

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