Chemical proteomics for direct target engagement

Identify protein–ligand interactions at scale to establish a robust molecular basis for target validation.

Orthogonal confirmation (ABPP, TPP, DARTS)

Complementary assays confirm binding and functional modulation, improving the quality of early leads.

Bioinformatics-driven candidate prioritization

Perform data mining, enrichment analysis, and functional clustering to focus resources on the most promising targets.

Value for pharma

Reduces uncertainty from hit identification to validated targets, shortening early discovery timelines.

Metabolomics to map host–microbiota crosstalk

Quantify microbial metabolites and assess their regulatory influence on host physiology.

Proteomic profiling of signaling and immune responses

Track host signaling shifts and immune modulation resulting from microbial community changes.

AI-powered analytics for complex cohorts

Enable clustering, functional annotation, and predictive modeling across large, heterogeneous datasets.

Value for pharma

Supports microbiome-based therapeutics by connecting community dynamics to host outcomes with molecular evidence.

Generative models for novel chemotypes

Propose candidate molecules optimized for target-specific binding features under defined constraints.

Virtual screening and molecular docking

Rapidly predict binding affinity and conformational fit to narrow experimental workloads.

In silico ADMET and toxicity profiling

Evaluate bioactivity, toxicity, and pharmacokinetics to rank and prioritize leads.

Value for pharma

Shortens design cycles and improves the probability of success in lead optimization.

Integrated target validation and MoA elucidation

Use chemical proteomics, degrader screening, and interactome analysis to confirm mechanism of action and downstream effects.

Multi-omics for deep candidate profiling

Cross-validate therapeutic relevance with multi-layer datasets to de-risk progression.

AI-guided lead optimization

Balance efficacy–safety trade-offs using predictive modeling to reduce late-stage attrition.

Value for pharma

Provides decision-grade evidence for pipeline advancement and regulatory-facing documentation.

Self-assembled delivery systems for precision therapeutics

Enhance stability, circulation time, and targeting efficiency across diverse modalities.

Physicochemical characterization for biodistribution

Assess particle size, zeta potential, and delivery kinetics to predict in vivo performance.

Proteomic and transcriptomic validation in target tissues

Confirm molecular effects of delivery vehicles within intended tissues.

Value for pharma

Couples rigorous characterization with biological validation to build confidence ahead of preclinical and clinical translation.