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Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment

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arxiv 2102.00879 v1 pith:IBX7DVPL submitted 2021-02-01 cs.NE physics.med-ph

Namid Stillman , Igor Balaz , Antisthenis Tsompanas , Marina Kovacevic , Sepinoud Azimi , Sebastien Lafond , Andrew Adamatzky , Sabine Hauert This is my paper · ORCID
classification cs.NEphysics.med-ph
keywords nanoparticleplatformanti-cancercancerevonanoscenariostreatmenttreatments
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We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments. EVONANO includes a simulator to grow tumours, extract representative scenarios, and then simulate nanoparticle transport through these scenarios to predict nanoparticle distribution. The nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer treatments. We demonstrate our platform with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour environments.

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