A newly synthesized small molecule: the evaluation against Alzheimer's Disease by in silico drug design and computational structure analysis methods

: The search for novel drugs that can prevent or control Alzheimer’s disease has attracted lot of attention from researchers across the globe. Phytochemicals are increasingly being used to provide scaffolds to design drugs for AD. In silico techniques, have proven to be a game-changer in this drug design and development process. In this review, the authors have focussed on current advances in the field of in silico medicine, applied to phytochemicals, to discover novel drugs to prevent or cure AD. After giving a brief context of the etiology and available drug targets for AD, authors have discussed the latest advances and techniques in computational drug design of AD from phytochemicals. Some of the prototypical studies in this area are discussed in detail. In silico phytochemical analysis is a tool of choice for researchers all across the globe and helps integrate chemical biology with drug design.

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Faculty Opinions recommendation of A small molecule targeting the multifactorial nature of Alzheimer's disease.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1094921.549872 ◽

2007 ◽

Author(s):

Dennis Hall

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Small Molecule

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Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach

Current Alzheimer Research ◽

10.2174/1567205016666190103154855 ◽

2019 ◽

Vol 16 (3) ◽

pp. 193-208 ◽

Cited By ~ 2

Author(s):

Yan Hu ◽

Guangya Zhou ◽

Chi Zhang ◽

Mengying Zhang ◽

Qin Chen ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Small Molecule ◽

Characteristic Curve ◽

Machine Learning Algorithms ◽

Learner Model ◽

Mao B ◽

Number Of Patients ◽

Sar Model ◽

Set Up

Background: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer’s drugs has become one of the most popular medical topics. Methods: In this study, in order to build a predicting model for Alzheimer’s drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. Results: The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. Conclusion: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer’s drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.

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In silico screening of pyridoxine carbamates for Anti-Alzheimer’s activities

Central Nervous System Agents in Medicinal Chemistry ◽

10.2174/1871524920666201119144535 ◽

2020 ◽

Vol 20 ◽

Author(s):

Dnyaneshwar Baswar ◽

Abha Sharma ◽

Awanish Mishra

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

In Silico ◽

Neurodegenerative Disorder ◽

Biological Activities ◽

Cholinergic Neurons ◽

In Silico Screening ◽

In Silico Studies ◽

Bbb Permeability ◽

Ld50 Value

Background: Alzheimer’s disease (AD), an irreversible complex neurodegenerative disorder, is most common type of dementia, with progressive loss of cholinergic neurons. Based on the multi- factorial etiology of Alzheimer’s disease, novel ligands strategy appears as up-coming approach for the development of newer molecules against AD. This study is envisaged to investigate anti-Alzheimer’s potential of 10 synthesized compounds. The screening of compounds (1-10) was carried out using in silico techniques. Methods: For in silico screening of physicochemical properties of compounds molinspiration property engine v.2018.03, Swiss ADME online web-server and pkCSM ADME were used. For pharmacodynamic prediction PASS software while toxicity profile of compounds were analyzed through ProTox-II online software. Simultaneously, molecular docking analysis was performed on mouse AChE enzyme (PDB ID:2JGE, obtained from RSCB PDB) using Auto Dock Tools 1.5.6. Results: Based on in silico studies, compound 9 and 10 have been found to have better drug likeness, LD50 value, and better anti-Alzheimer’s, nootropic activities. However, these compounds had poor blood brain barrier (BBB) permeability. Compound 4 and 9 were predicted with better docking score for AChE enzyme. Conclusion: The outcome of in silico studies have suggested, out of various substitutions at different positions of pyridoxine-carbamate, compound 9 have shown promising drug likeness, with better safety and efficacy profile for anti-Alzheimer’s activity. However, BBB permeability appears as one the major limitation of all these compounds. Further studies are required to confirm its biological activities.

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