MOF-LENS is a bio-inspired optimisation framework developed in Applied Data Science and Artificial Intelligence at SRH University Heidelberg for accelerated discovery of metal-organic framework nanocarriers for doxorubicin delivery in cancer therapy. The method integrates structural descriptors, chemical fingerprints, and the Lotus Effect Algorithm to identify promising MOFs that balance drug loading, pH-responsive release, chemical affinity, and biocompatibility.
Why does it matter?
- Searches a very large MOF design space more efficiently than manual screening.
- Targets DOX-compatible pore size and controlled release behaviour under acidic tumour conditions.
- Supports reproducible, data-driven candidate selection instead of isolated trial-and-error screening.
Potential applications
- MOF nanocarrier discovery for doxorubicin delivery.
- Targeted cancer drug delivery and pH-responsive release design.
- Future retargeting to other therapeutics, such as paclitaxel, with limited parameter changes.