Industry workspace

Biotech AI Platform

A workspace designed for people, LLMs, and agents — bringing together MCP servers, agent catalogs, and domain intelligence tailored for Biotech.

What you get

Curated agents, MCP servers, and playbooks mapped to real workflows — ready for enterprise adoption.

How it’s delivered

Versioned releases with clear ownership, governance controls, and audit-ready operational metadata.

Workspace summary

Default subscriptions and recommended starting points.

Agents

Catalog

Use cases

Catalog

Research

Playbooks

Governance

Policies

Case studies

Real outcomes in Biotech

Challenges, solutions, and measurable outcomes drawn from Colaberry industry work.

2 use cases

AI-accelerated drug discovery and lead compound optimization

Challenge

Drug discovery pipelines suffered from high attrition rates, with 90% of candidates failing in clinical trials due to inadequate target validation and ADMET prediction

Colaberry’s solution

Deployed graph neural networks for molecular property prediction and drug-target interaction modeling
Built automated ADMET screening pipelines using ensemble ML models trained on proprietary compound libraries
Integrated generative chemistry models for novel molecule design optimized for desired pharmacological profiles

Outcomes

40% reduction in early-stage screening time for lead compound identification
Hit rate for viable candidates improved from 2% to 8% in target validation phase
3 novel molecular scaffolds identified and advanced to preclinical evaluation

Scalable genomics pipeline for clinical-grade variant analysis

Challenge

Genomics research teams struggled with petabyte-scale sequencing data analysis, with manual bioinformatics pipelines creating bottlenecks in variant calling and annotation

Colaberry’s solution

Automated NGS data processing pipelines using cloud-native workflow orchestration with containerized analysis tools
Applied deep learning models for variant calling accuracy improvement and structural variant detection
Built knowledge graph integrating variant annotations from ClinVar, gnomAD, and proprietary datasets for clinical interpretation

Outcomes

Whole genome analysis turnaround reduced from 5 days to 8 hours
Variant calling concordance improved to 99.7% compared to gold-standard benchmarks
Clinical interpretation time reduced by 60% through automated annotation and prioritization

Industry delivery

Combine industry context, governed agents, and MCP integrations into repeatable playbooks that teams can deploy with confidence.