Author(s):
Prachi Parvatikar, Joy Hoskeri, Bhagirathi Hallali, Kusal K Das
Email(s):
prachisandeepk@gmail.com
DOI:
10.52711/0974-360X.2024.00218 
Address:
Prachi Parvatikar1*, Joy Hoskeri3, Bhagirathi Hallali4, Kusal K Das2
1Dept of Biotechnology, Allied Health Sciences, BLDE(DU), Vijayapura 586103, India.
2Laboratory of Vascular Physiology and Medicine, Dept of Physiology, Shri B.M. Patil Medical College, Hospital and Research centre.
3Dept of Bioinformatics, Karnataka State Akkamahadevi Women’s University, Vijyaypura, India.
4Dept of Computer Science, Govt Degree College, Raibag, Belgavi, India.
*Corresponding Author
Published In:
Volume - 17,
Issue - 3,
Year - 2024
ABSTRACT:
Proteochemometric (PCM) modelling is the new way of developing quantitative structure activity relationship models. It is computational method in which multiple ligands and multiple targets are used to predict bioactivity. PCM is based on three important components; the descriptors, bioactivity data and connecting link between descriptors and bioactivity data. In recent years PCM modelling has become more popular in drug discovery area as it has advantage of application of different descriptors, bioactivity data and machine learning algorithms. The performance of PCM is enhanced to traditional interaction pattern by application of different descriptors such as target descriptors and cross-term descriptors. So, in current review PCM and different descriptors used in PCM development and its application in various field of drug discovery has been discussed.
Cite this article:
Prachi Parvatikar, Joy Hoskeri, Bhagirathi Hallali, Kusal K Das. Proteochemometric (PCM) Modelling: A Machine Learning Technique for Drug Designing. Research Journal of Pharmacy and Technology. 2024; 17(3):1382-5. doi: 10.52711/0974-360X.2024.00218
Cite(Electronic):
Prachi Parvatikar, Joy Hoskeri, Bhagirathi Hallali, Kusal K Das. Proteochemometric (PCM) Modelling: A Machine Learning Technique for Drug Designing. Research Journal of Pharmacy and Technology. 2024; 17(3):1382-5. doi: 10.52711/0974-360X.2024.00218 Available on: https://rjptonline.org/AbstractView.aspx?PID=2024-17-3-69
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