Network Pharmacology + Mass Spectrometry Service

You have a list of predicted targets from your network pharmacology analysis. Now you need to know which ones are real. That gap — between computational prediction and experimental proof — is where most drug discovery projects stall. Our network pharmacology mass spectrometry service closes it, combining computational network analysis with proteomics- and metabolomics-based validation to deliver verified target lists you can publish, patent, or advance into follow-up studies.

For related capabilities, see our chemoproteomics service for affinity-based target identification approaches.

How it works:

1. Computational network analysis — We start with your existing network pharmacology data or build one from scratch using STRING, STITCH, and DisGeNET, applying network propagation algorithms to prioritize the most likely targets.
2. Experimental design consultation — Your compound type, sample availability, and research question determine the best MS strategy: chemoproteomics, thermal shift profiling, or affinity-based approaches.
3. MS-based target identification — We run the selected method on Orbitrap or timsTOF platforms to capture proteins that bind your compound or show compound-dependent stability shifts.
4. Multi-omics data integration — When the question calls for it, we layer untargeted metabolomics on top of proteomics to capture both protein-level and metabolite-level effects from the same experiment.
5. Network reconstruction and pathway analysis — Identified targets go back onto biological networks for enrichment analysis, PPI construction, and pathway mapping in your disease context.
6. Validated target report — You get a ranked target list with confidence scores, raw MS data, network visualizations, and a written interpretation ready for manuscripts or grant applications.

Single-target biochemical assays were never designed to capture the polypharmacology that network pharmacology aims to characterize. Mass spectrometry changes that.

Which Method Fits Your Project

For a deeper look at label-free thermal profiling approaches, see our thermal shift proteomics service.

1

Computational Network Analysis

We start with your existing network pharmacology data or build one from scratch using STRING, STITCH, and DisGeNET. Network propagation algorithms prioritize the most likely targets from the predicted network, reducing the list to a manageable set for experimental validation.

2

Experimental Design Consultation

Your compound type, sample availability, and research question determine the best MS strategy. We recommend the optimal approach — chemoproteomics for single compounds with handles, thermal profiling for label-free detection, or DARTS for quick validation — and plan the control conditions, replicates, and orthogonal confirmation strategy.

3

MS-Based Target Identification

We run the selected method on Orbitrap or timsTOF platforms. For thermal profiling, samples are heated across a temperature gradient and soluble fractions analyzed by LC-MS/MS. For chemoproteomics, compound-bound beads capture interacting proteins for digestion and identification. Each experiment quantifies 5,000–8,000 proteins.

4

Multi-Omics Data Integration

When the question calls for it, we layer untargeted metabolomics on top of proteomics to capture both protein-level and metabolite-level effects from the same experiment. This dual-readout approach reveals not just which proteins are bound, but what metabolic consequences follow — essential for understanding functional impact.

5

Network Reconstruction and Pathway Analysis

Identified targets go back onto biological networks for enrichment analysis, PPI construction, and pathway mapping in your disease context. We build compound-target-pathway networks in Cytoscape, with node attributes encoding confidence scores, differential expression, and pathway membership.

6

Validated Target Report

You receive a ranked target list with confidence scores (high/medium/putative), raw MS data, network visualizations, cross-comparison tables of predicted vs experimentally identified targets, and a written interpretation with recommendations for follow-up validation — ready for manuscripts or grant applications.

For label-free target identification, see our PISA service for thermal stabilization-based profiling.

For related multi-omics capabilities, see our drug-target network analysis service.

Important planning considerations:

• Cell or tissue requirements: For cell-based experiments, 1×10⁷ cells per condition (minimum 3 conditions: control, low-dose, high-dose). For tissue samples, 50–100 mg per condition.
• Controls: Include vehicle-only control (DMSO or appropriate solvent) and a positive control compound with known targets where applicable.
• Replicates: Minimum 3 biological replicates per condition; 2–3 technical replicates per sample.
• Bioinformatics: All projects include standard network analysis (PPI construction, pathway enrichment, GO analysis). Advanced options — multi-omics integration, network propagation, machine learning-based target prioritization — are available on request.

Our bioinformatics pipeline turns raw MS data into target insights you can act on:

What you receive:

• Ranked target list with confidence scores (high/medium/putative)
• PPI network visualization with annotated target nodes
• Pathway enrichment bar charts and network maps
• Cross-comparison table: predicted vs experimentally identified targets
• Written interpretation with recommendations for follow-up validation

For complementary approaches, see our native metabolomics for ligand discovery service.

What sets us apart:

• End-to-end integration — We handle both the computational prediction and experimental validation, so you don't have to manage the handoff between a bioinformatics vendor and a proteomics lab.
• Method flexibility — We pick the MS approach that fits your compound and question, rather than forcing everything through one workflow.
• Natural product specialization — Our team works routinely with complex mixtures: extract fractionation, compound identification, and multi-constituent target deconvolution.
• Publication support — Results are formatted for journal submission, including methods sections, figure preparation, and supplementary data tables.