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. 2024 Nov 1;19(11):e0309477.
doi: 10.1371/journal.pone.0309477. eCollection 2024.

Regression analysis of topological indices for predicting efficacy of Alzheimer's drugs

Tahreem Ashraf  1 , Nazeran Idrees  1 , Melaku Berhe Belay  2   3
Affiliations

Regression analysis of topological indices for predicting efficacy of Alzheimer's drugs

Tahreem Ashraf et al. PLoS One. .

Abstract

Alzheimer's Disease(AD) is the most common type of dementia. It is a progressive disease beginning with mild memory loss and possibly leading to loss of the ability to carry on a conversation and respond to the environment. This study investigates the relationship between the chemical structure of potential AD drugs and their therapeutic efficacy using Multi-Criteria Decision-Making (MCDM) techniques including The approach for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) method. A comprehensive dataset comprising molecular descriptors and corresponding pharmacological properties, i.e., melting point, boiling point, molecular weight and density of AD drugs was compiled from diverse sources. Topological indices were calculated to capture the structural characteristics of these compounds. Application of TOPSIS and SAW through Entropy method helps obtain optimal drugs for curing AD. Quantitative Structure Property Relationships (QSPR) analysis has been done between properties and topological indices of AD's drug structures. Results revealed significant relations between specific topological indices and drug efficacy, providing insights into the structural features crucial for AD treatment efficacy. This approach offers a promising avenue for rational drug design and optimization in the quest for novel AD therapeutics.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1
Fig 2
Fig 2. AD’s topological indices.
Fig 3
Fig 3. Decision matrix Dij.
Fig 4
Fig 4. Normalized decision matrix Hij.
Fig 5
Fig 5. Entropy and weight calculations.
Fig 6
Fig 6. Normalized decision matrix Hij.
Fig 7
Fig 7. Weighted normalized decision matrix Xij.
Fig 8
Fig 8. Calculation of separating measures Pi+ and Pi-.
Fig 9
Fig 9. Calculation of Ideal measures Oi* and ranking of drugs using TOPSIS.
Fig 10
Fig 10. Decision matrix Dij for SAW.
Fig 11
Fig 11. Weighted normalized decision matrix Xij for SAW.
Fig 12
Fig 12. Calculation of ideal measures Oi* and Ranking of Drugs using SAW.
Fig 13
Fig 13
See this image and copyright information in PMC

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