MIA-QSAR based model for bioactivity prediction of flavonoid derivatives as acetylcholinesterase inhibitors.

Alzheimer's disease is a common form of dementia, which considered to be a major health concern. Multivariate Image Analysis - Quantitative Structure-Activity Relationship (MIA-QSAR) is a simple and quite accessible QSAR method for predicting biological activities of unstudied compounds based on 2D image analysis. This study focuses on constructing an efficient QSAR model using a dataset of 52 flavonoid derivatives (substituted with amino-alkyl, alkoxy, alkyl-amines, and piperidine groups) as active compounds against acetylcholinesterase inhibitors (AChE). The model was constructed by PLS (Partial Least Square) using NIPALS (Non-Linear iterative Partial Least Square) algorithm. The comparable values obtained from calibration of training set using five latent variables (R2 = 0.955) and external validation of test set (Q2 = 0.948) confirmed the precision in the prediction of bioactivities for the set of flavonoid derivatives used in designing the model.

• Publication year

  2018

• Venue

  Journal of Theoretical Biology

• Publication date

  2018-12-01

• Fields of study

  Medicine, Chemistry, Mathematics

• Identifiers
  DOI 10.1016/j.jtbi.2018.09.030PMID 30267791
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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