Table 1:

Selected studies in nanomedicine development using machine learning, their dataset source, and curation strategy.

Study	Dataset source	Curation strategy	Training data size	Algorithm	Ref.
Novel excipient candidates	DrugBank, Drugs@FDA*,	High throughput screening for drug- excipient interactions, Cheminformatics computed by RDkit, and molecular dynamics studies	2.1 million drug-excipient pairings	Random forest	29
Nanoparticle protein corona	Literature, UniProt	Data mining for nanoparticle properties and classification, physiochemical descriptions of protein corona.	56 papers with 178 independent proteins	Random forest	14
Biological activity prediction	Literature, in-house screening and imaging	Structure-activity relationship, image analysis	960 SNAs with 17,000 MALDI-MS data points; 1620 samples for immune responses and 301 samples for organ burdens; 1301 micrometastases for image analysis	Random forest, XGBoost; Support Vector Machine	95, 96, 97

^*Excipients can be sourced under Inactive Ingredient Search for Approved Drug Product