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

Namid R. Stillman, Igor Balaz, Michail-Antisthenis Tsompanas, Marina Kovacevic, Sepinoud Azimi, Sébastien Lafond, Andrew Adamatzky, Sabine Hauert*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)
1 Downloads (Pure)


We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments. Our work aims to decrease both the time and cost required to develop nanoparticle designs. EVONANO includes a simulator to grow tumours, extract representative scenarios, and simulate nanoparticle transport through these scenarios in order to predict nanoparticle distribution. The nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer treatments. We demonstrate EVONANO with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour environments. Our platform shows how in silico models that capture both tumour and tissue-scale dynamics can be combined with machine learning to optimise nanomedicine.
Original languageEnglish
Article number150
Journalnpj Computational Materials
Issue number1
Publication statusPublished - 21 Sept 2021
MoE publication typeA1 Journal article-refereed


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