CycloGen AI Discovery Pipeline
AI-Powered Drug Discovery Pipeline
CycloGen connects structural biology, AI and computational chemistry across early-stage discovery — from target identification through structure prediction, druggability analysis, molecular design and candidate prioritization for experimental validation.
Build computational evidence before committing candidates to experimental investigation.
Target & Structure
Import experimental structures or generate computational structural models and establish structural context for the target.
Druggability & Pockets
Analyze structural features and potential interaction sites relevant to therapeutic investigation.
Therapeutic Design
Generate and evaluate candidates: small molecules, nanobodies, antibodies, mini-binders, peptides, protein therapeutics.
Complex Modeling & Docking
Investigate candidate-target interactions using structural modeling, docking and interface-analysis workflows.
Candidate Intelligence
Compare computational evidence including structural quality, molecular interactions, and developability to prioritize candidates.
Experimental Handoff
Export selected computational candidates and supporting evidence for downstream experimental investigation.
Computational Prioritization
Reduce the Search Space Before the Wet Lab
Drug discovery can begin with an enormous number of possible molecules, sequences and therapeutic designs. The purpose of computational discovery is not simply to generate more candidates.
CycloGen helps scientists progressively evaluate and prioritize computational candidates before higher-cost experimental work begins.
Protect the Research Behind the Pipeline
Your Discovery Data Can Be Part of Your Intellectual Property
Drug discovery creates valuable intellectual property long before a therapeutic candidate reaches the clinic. Novel biological targets, unpublished protein sequences, molecular designs, antibody candidates, binding hypotheses, structural models and experimental results can represent years of research and significant investment.
Organizations handling confidential research should understand:
Where is the data processed?
Where is it stored?
How long is it retained?
Which subprocessors may handle it?
Which jurisdiction applies?
What contractual confidentiality protections exist?
Private AI Infrastructure
Your Science. Your Infrastructure.
Bring AI-assisted drug discovery closer to the infrastructure that protects your research.
CycloGen Private Deployment
- Customer-controlled computational environment
- Local compute options
- Greater control over research-data movement
- Integration with internal infrastructure
- Reduced dependence on external inference services
- Designed for proprietary research workflows
Externally Hosted Computation
- Research data may leave internal infrastructure
- Data-processing terms require review
- Retention policies vary
- Data residency may matter
- External availability can affect workflows
- Usage or compute limits may apply
Computational workflows can remain within infrastructure controlled by the organization, depending on deployment configuration.
Built for Discovery Organizations
Emerging Biotech
Access advanced computational discovery capabilities without having to build every AI and computational infrastructure component internally.
Pharmaceutical R&D
Deploy computational capabilities closer to proprietary research environments and established scientific workflows.
CROs
Extend computational discovery capabilities alongside existing experimental and research services.
One Platform. Different Discovery Environments.
Computational → Experimental
When Candidates Are Ready for the Lab
Computational prediction is the beginning of evidence, not the end. Selected candidates can move from CycloGen computational workflows into appropriately designed experimental programs.
For organizations that need experimental capabilities, CycloGen offers integrated discovery programs using laboratory services delivered through our CRO partner network.
From Computational Hypothesis to Experimental Decision
CycloGen is designed to help scientists reduce large computational design spaces into smaller, traceable sets of candidates for experimental investigation.
Computational prediction does not replace experimental validation. It helps scientists decide what deserves to be tested.