Introduction to Network Pharmacology
Network Pharmacology is an innovative approach that integrates systems biology, bioinformatics, and computational modeling to explore the complex interactions between drugs, targets, pathways, and diseases. By analyzing drug-target networks, protein-protein interactions, and multi-omics data, network pharmacology provides a holistic perspective on drug action, offering insights into mechanisms of action, biomarker discovery, and potential drug repurposing.
At Bio-seva, our network pharmacology services utilize advanced algorithms, machine learning, and large-scale databases to identify key molecular interactions, predict therapeutic targets, and optimize drug design. This approach enhances precision medicine, supports rational drug development, and accelerates the discovery of novel therapeutic strategies in various disease areas.
Drug-Target Prediction
Our drug-target prediction service leverages AI-driven algorithms, molecular modeling, and large-scale bioinformatics databases to identify potential drug-target interactions. By integrating chemical properties, biological networks, and omics data, we enhance drug discovery and repurposing efforts with high accuracy and efficiency.
Drug-Disease Association Analysis
By systematically analyzing drug-disease associations, we uncover novel therapeutic relationships based on genomic, transcriptomic, proteomic, and clinical datasets. This approach aids in drug repurposing, biomarker identification, and precision medicine applications, offering new avenues for treatment strategies.
Pathway Enrichment & Mechanistic Studies
Our pathway enrichment and mechanistic studies identify key molecular pathways and biological mechanisms involved in drug responses and disease progression. Through advanced computational analysis, we provide insights into drug action, toxicity prediction, and therapeutic targets, aiding in the rational design of novel interventions.
Network Topology Analysis
We perform network topology analysis to study the structural and functional properties of biological and pharmacological networks. By analyzing protein-protein interactions (PPIs), gene regulatory networks, and drug-target networks, we identify critical nodes and hubs, enhancing our understanding of complex biological systems and drug mechanisms.
Molecular Docking and Dynamics
Our molecular docking services predict binding affinities and interactions between small molecules and target proteins, facilitating lead compound screening and optimization. Using structure-based drug design (SBDD) and ligand-based approaches, we help accelerate drug discovery with reliable binding mode predictions. We also apply molecular dynamics (MD) simulations to study biomolecular behavior at atomic resolution.
Structural Prediction
Our structural prediction services use AI-driven and homology modeling techniques to determine protein structures, ligand conformations, and macromolecular assemblies. By predicting 3D structures of target proteins and drug candidates, we aid in understanding functional mechanisms and structure-based drug design.