AI/ML-ready Datasets for Peptide Permeability & Oral Bioavailability or Macrocyclic Peptide Screening Libraries to Enable Oral, Cell-permeable Macrocycle Design

AstraZeneca is seeking datasets or physical/virtual libraries of macrocyclic peptides that can help map and model the property space required for oral and cell permeable macrocyclic peptides. They particularly welcome: 

• Annotated in vitro/vivo datasets describing permeability, oral bioavailability, enzymatic stability or other relevant properties of peptide macrocycles—including both proprietary and published collections
• Libraries of macrocyclic peptides which are suitable for screening, ideally with reference activity which would enable AI-led property mapping and predictive modelling. Physical compound sets are preferred but virtual libraries may be accepted for use once training datasets have been established
• Novel or proprietary methods for predicting, quantifying, or modelling the physicochemical space that governs permeability and oral absorption in macrocyclic peptides

Submissions should either provide experimental proof-of-concept, published evidence, or actionable approaches for generating datasets or screening tools that address these key parameters. Solutions that enable parallel evaluation of multiple macrocyclic peptides, or which deliver mechanistic or predictive insights into the determinants of oral and cell permeability, are particularly encouraged. Please see page [NUMBER] of the submission form [LINK] for further details on the solution criteria.

Out of scope: Proposals that are hypothetical, lack experimental feasibility, or involve low-throughput experimental or predictive modelling approaches that are not scalable for parallel evaluation of large compound sets will be given a lower priority.

Submission Information

AstraZeneca invites applications from both academic and biotech organizations. Applicants should complete the submission form [LINK] which should contain a brief, non‑confidential overview of your proposal, demonstrating how the RFP requirements are satisfied by your approach. Proposals should outline the resources required to solve the problem. To submit your proposal, please visit our website, register, and submit your application form under the appropriate Connect campaign.

Opportunity for Collaboration

• Co-development opportunity with AstraZeneca 
• Access to comprehensive screening data: contributors will receive complete physicochemical, permeability, and stability profiles for all compounds screened through AstraZeneca’s validated cascade 
• AI/ML model insights: collaborators gain access to predictive models and property-activity relationships derived from their contributed data, enhancing their own research capabilities 
• Co-publication opportunities: joint publication rights for significant findings and model developments arising from collaborative datasets 
• Expanded dataset value: data becomes part of a larger, more statistically robust dataset, increasing its scientific impact and citation potential