Exploring the role of topological descriptors to predict physicochemical properties of anti-HIV drugs by using supervised machine learning algorithms

In order to explore the role of topological indices for predicting physio-chemical properties of anti-HIV drugs, this research uses python program-based algorithms to compute topological indices as well as machine learning algorithms. Degree-based topological indices are calculated using Python algo...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:BMC chemistry Ročník 18; číslo 1; s. 167 - 22
Hlavní autoři: Ahmed, Wakeel, Zaman, Shahid, Asif, Eizzah, Ali, Kashif, Mahmoud, Emad E., Asheboss, Mamo Abebe
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 12.09.2024
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Algorithms
Analysis
Anti-HIV agents
Anti-HIV-1 drugs
Antiviral agents
Biological activity
Biological effects
Biological properties
Chemical properties
Chemistry
Chemistry and machine learning
Chemistry and Materials Science
Chemistry/Food Science
Data mining
Drug discovery
Drug therapy
Drugs
Effectiveness
Forecasts and trends
HIV
HIV (Viruses)
Human immunodeficiency virus
Machine learning
Machine learning algorithm
Molecular structure
Physicochemical properties
Python
Python algorithm
QSPR analysis
Supervised learning
Topological indices
Topology
Iran
Anti-HIV-1 drugs
QSPR analysis
Python algorithm
Topological indices
Machine learning algorithm
ISSN:2661-801X, 2661-801X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In order to explore the role of topological indices for predicting physio-chemical properties of anti-HIV drugs, this research uses python program-based algorithms to compute topological indices as well as machine learning algorithms. Degree-based topological indices are calculated using Python algorithm, providing important information about the structural behavior of drugs that are essential to their anti-HIV effectiveness. Furthermore, machine learning algorithms analyze the physio-chemical properties that correspond to anti-HIV activities, making use of their ability to identify complex trends in large, convoluted datasets. In addition to improving our comprehension of the links between molecular structure and effectiveness, the collaboration between machine learning and QSPR research further highlights the potential of computational approaches in drug discovery. This work reveals the mechanisms underlying anti-HIV effectiveness, which paves the way for the development of more potent anti-HIV drugs. This work reveals the mechanisms underlying anti-HIV efficiency, which paves the way for the development of more potent anti-HIV drugs which demonstrates the invaluable advantages of machine learning in assessing drug properties by clarifying the biological processes underlying anti-HIV behavior, which paves the way for the design and development of more effective anti-HIV drugs.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2661-801X
2661-801X
DOI:10.1186/s13065-024-01266-4