QSAR-based Target Identification

Quantitative structural activity relationship (QSAR) is not only a predictive tool, but also serves as a valuable analytical tool to help understand the mechanisms of drug-receptor interactions and specific biological responses by analyzing significant patterns between descriptors and biological activities. QSAR is an important guide for target identification, drug design, and other life science research, and provides a powerful theoretical tool and technical support for scientific research by revealing the intrinsic link between molecular structure and biological activity. CD ComputaBio provides comprehensive QSAR analysis services, including full-process support from dataset preparation to model validation, to help researchers conduct drug discovery and development quickly and efficiently.

Fig 1. Traditional and deep QSAR models. (TROPSHA, A.; et al, 2024)

Introduction of Quantitative Structural Activity Relationship

QSAR is a vital technique in computational drug design, essential for understanding the relationship between molecular structures and biological activities. By correlating experimental activities with molecular descriptors, QSAR models predict new compound activities, aiding in drug design and synthesis. This technique also extracts patterns linking structure to biological activity, providing insights into drug-receptor interactions and other biological responses. Such capabilities are crucial for target identification and optimization, streamlining the drug development process.

Steps Involved in QSAR Analysis

Good QSAR modeling depends primarily on the accurate and insightful selection and analysis of molecular descriptors that effectively capture the structural characteristics affecting biological activity. By carefully following these steps, researchers can develop reliable QSAR models to predict the activities of novel compounds.

1Chemical data base
2Molecular modeling and optimization
3Descriptor generation and calculation
4Building QSAR model
5Validation of QSAR model
6Interpretation and application

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Data Set Preparation

Choose a set of compounds with known biological activities.
Gather experimental biological activity data for these compounds.
Clean and preprocess the data to handle missing values, outliers, and ensure consistency.

Calculation and Selection of Molecular Descriptors

Compute various molecular descriptors, including quantum chemical, molecular fingerprints, topological indices, geometrical and so on.
Use statistical or machine learning methods to select the most relevant descriptors. This reduces dimensionality and avoids the inclusion of noisy, irrelevant, or redundant variables.

Correlation Model Development

Choose a suitable modeling technique such as linear regression, multiple linear regression, partial least squares, principal component analysis, machine learning models.
Develop a mathematical correlation between the molecular descriptors (independent variables) and the biological activity (dependent variable).

Model Evaluation and Validation

Use techniques like cross-validation to assess the robustness of the model.
Test the model on an independent data set not used in model building to evaluate its predictive power.
Assess the model’s performance using statistical parameters such as coefficient of determination (R²), cross-validated R² (Q2), root-mean-square error of prediction and various other metrics.

CD ComputaBio harnesses QSAR methodologies to streamline and optimize the identification of potential drug targets, contributing to the advancement of effective therapeutic agents. We look forward to working with you to support your drug research and development projects. If you are interested in our services or have any questions, please feel free to contact us.

References

TROPSHA, A.; et al. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nature Reviews Drug Discovery. 2024, 23(2): 141-155.
RUDRAPAL, M; et al. Virtual Screening, Molecular Docking and QSAR Studies in Drug Discovery and Development Programme. Journal of Drug Delivery and Therapeutics. 2020, 10: 225-233.

For research use only. Not intended for any clinical use.

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