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

2021/02/01 by Namid Stillman, Igor Balaž, Stillman, Namid +13
Biochemistry, Genetics and Molecular Biology · Computer Science · #Bioinformatics and Genomic Networks #Computational Drug Discovery Methods #FOS: Computer and information sciences #FOS: Physical sciences #Genetics, Bioinformatics, and Biomedical Research #Medical Physics (physics.med-ph) #Neural and Evolutionary Computing (cs.NE)

paper · pdf · doi:10.48550/arxiv.2102.00879

openalex publication_date 2021/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present the EVONANO platform for the evolution of nanomedicines with\napplication to anti-cancer treatments. EVONANO includes a simulator to grow\ntumours, extract representative scenarios, and then simulate nanoparticle\ntransport through these scenarios to predict nanoparticle distribution. The\nnanoparticle designs are optimised using machine learning to efficiently find\nthe most effective anti-cancer treatments. We demonstrate our platform with two\nexamples optimising the properties of nanoparticles and treatment to\nselectively kill cancer cells over a range of tumour environments.\n

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