QSAR Approach for Drug Discovery Targeting the Glucagon Receptor Using Machine Learning
- Title
- QSAR Approach for Drug Discovery Targeting the Glucagon Receptor Using Machine Learning
- Creator
- Sharma V.; Sarkar O.; Mishra S.; Sinha S.
- Description
- Metabolic disorders like type 2 diabetes are increasing day by day so the study focusing drug discovery of glucagon receptor has become important.One of the method to study the binding strength between chemical compounds is Quantitative Structure-Activity Relationship (QSAR) which is discussed in this paper.We gathered a curated dataset of glucagon receptor ligands from the ChEMBL bio activity dataset and studied the physical and chemical properties of the molecules using factors like molecular weight and logarithm of the partition coefficient.Then Random forest regression model was applied for prediction of the binding strength of ligands. The efficiency information of ligand was extracted which contributed to study of the molecular features concerning the activity of glucagon receptor in a much easier manner. These findings highlight the potential of QSAR in elucidating the key determinants of ligated-receptor interactions and guiding the rational design of novel glucagon receptor modulators. The integration of computational approaches with experimental validation holds promise for accelerating the development of effective therapies for metabolic disorders, addressing unmet clinical needs in this field. 2023 IEEE.
- Source
- 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 702-706.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Computational modeling; Drug Discovery; Glucagon receptor; Ligand efficiency; Machine Learning; QSAR
- Coverage
- Sharma V., Christ (Deemed to Be University), Delhi NCR, India; Sarkar O., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sinha S., Kalinga Institute of Industrial Technology, Deemed to Be University, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038247-1
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Sharma V.; Sarkar O.; Mishra S.; Sinha S., “QSAR Approach for Drug Discovery Targeting the Glucagon Receptor Using Machine Learning,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19641.