QSAR-based Virtual Screening Service
Quantitative structure-activity relationship (QSAR)-based virtual screening is a vital tool within the realm of computer-aided drug design (CADD). It enables the prediction of the biological activity of compounds based on their chemical structures and properties. At CD ComputaBio, we offer advanced QSAR-based virtual screening services to accelerate the drug discovery process and enhance the chances of finding effective therapeutic candidates.
Introduction to QSAR-based Virtual Screening
Quantitative structure-activity relationship (QSAR) analysis is a ligand-based drug design method. It seeks statistical correlations between chemical structures and biological activities (such as pIC50, pEC50, Ki) or categorical properties (such as activity, toxicity) using regression and classification methods. In recent years, QSAR has continuously developed, enabling modeling and virtual screening of ultra-large datasets containing thousands of diverse chemical structures, becoming an important tool for efficiently screening lead compounds in drug development.
Fig.1 The identification of mitotic kinesin Eg5 inhibitors. (Bodun D S, et al., 2023)
Our Services
CD ComputaBio, with its deep technical expertise in computational biology and drug design, provides clients with professional QSAR-based virtual screening services. We utilize constructed QSAR models to perform virtual screening on large-scale compound libraries, rapidly identifying potential active compounds.
By Molecular Types
Workflow of QSAR-based Virtual Screening
Data Collection - Relevant datasets are collected from reliable external sources, and the collected data are organized and integrated to eliminate or correct inconsistent data, ensuring data quality.
QSAR Model Building and Validation - Using the organized data, QSAR models are built. Through statistical analysis and cross-validation, the models are ensured to have good fitting degrees and predictive abilities for compound biological activities.
Initial Screening - Using the validated QSAR models, compounds predicted to be active are screened from large compound libraries (e.g., containing 10⁵ to 10⁷ compounds).
Refined Screening - If needed, QSAR models are further optimized to improve screening efficiency and accuracy. Additionally, hit compounds are comprehensively evaluated using other computational methods (such as molecular docking).
Results Delivery - Provide a detailed virtual screening results report, deliver a refined compound list to the client, and suggest subsequent experimental validation.
Methods for QSAR-based Virtual Screening
QSAR has undergone several transformations, ranging from the dimensionality of the molecular descriptors (from 1D to nD) and different methods for finding a correlation between the chemical structures and the biological property. Based on this, CD ComputaBio offers virtual screening services utilizing both 2D-QSAR and 3D-QSAR approaches.
2D-QSAR Modeling
Utilizing descriptors derived from the two-dimensional structural features of compounds, such as topological indices and molecular fingerprints, quantitative models are constructed to establish relationships between chemical structures and biological activities through statistical methods like multiple linear regression and support vector machines.
3D-QSAR Modeling
By analyzing the three-dimensional structural characteristics and spatial arrangements of compounds, including molecular shapes, electrostatic potentials, and hydrogen bond interactions, models are built using methods like CoMFA and CoMSIA, correlating spatial similarities with biological activity data.
Please feel free to contact CD ComputaBio, and let our professional team customize personalized virtual screening solutions for you, helping you overcome scientific research challenges and achieve groundbreaking results.
References:
- Bodun, D S.; et al. QSAR-based virtual screening of traditional Chinese medicine for the identification of mitotic kinesin Eg5 inhibitors[J]. Computational Biology and Chemistry. 2023, 104: 107865.
- Mafethe, O.; et al. Pharmacophore model-based virtual screening workflow for discovery of inhibitors targeting Plasmodium falciparum Hsp90[J]. ACS omega. 2023, 8(41): 38220-38232.
* For Research Use Only.
