AI-assisted models to predict chemotherapy drugs modified with C₆₀ fullerene derivatives

Jonathan-Siu-Loong Robles-Hernández, Dora Iliana Medina, Katerin Aguirre-Hurtado, Marlene Bosquez, Roberto Salcedo and Alan Miralrio

Beilstein J. Nanotechnol. 2024, 15, 1170–1188. https://doi.org/10.3762/bjnano.15.95

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AI-assisted models to predict chemotherapy drugs modified with C₆₀ fullerene derivatives

Jonathan-Siu-Loong Robles-Hernández, Dora Iliana Medina, Katerin Aguirre-Hurtado, Marlene Bosquez, Roberto Salcedo and Alan Miralrio

Beilstein J. Nanotechnol. 2024, 15, 1170–1188. https://doi.org/10.3762/bjnano.15.95

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Robles-Hernández, J.-S.-L.; Medina, D. I.; Aguirre-Hurtado, K.; Bosquez, M.; Salcedo, R.; Miralrio, A. Beilstein J. Nanotechnol. 2024, 15, 1170–1188. doi:10.3762/bjnano.15.95

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TY  - JOUR
A1  - Robles-Hernández, Jonathan-Siu-Loong
A1  - Medina, Dora Iliana
A1  - Aguirre-Hurtado, Katerin
A1  - Bosquez, Marlene
A1  - Salcedo, Roberto
A1  - Miralrio, Alan
T1  - AI-assisted models to predict chemotherapy drugs modified with C60 fullerene derivatives
JF  - Beilstein Journal of Nanotechnology
PY  - 2024///
VL  - 15
SP  - 1170
EP  - 1188
SN  - 2190-4286
DO  - 10.3762/bjnano.15.95
PB  - Beilstein-Institut
JA  - Beilstein J. Nanotechnol.
UR  - https://doi.org/10.3762/bjnano.15.95
KW  - breast cancer
KW  - CXCR7
KW  - drug nanocarriers
KW  - QSAR
N2  - Employing quantitative structure–activity relationship (QSAR)/ quantitative structure–property relationship (QSPR) models, this study explores the application of fullerene derivatives as nanocarriers for breast cancer chemotherapy drugs. Isolated drugs and two drug–fullerene complexes (i.e., drug–pristine C60 fullerene and drug–carboxyfullerene C60–COOH) were investigated with the protein CXCR7 as the molecular docking target. The research involved over 30 drugs and employed Pearson’s hard–soft acid–base theory and common QSAR/QSPR descriptors to build predictive models for the docking scores. Energetic descriptors were computed using quantum chemistry at the density functional-based tight binding DFTB3 level. The results indicate that drug–fullerene complexes interact more with CXCR7 than isolated drugs. Specific binding sites were identified, with varying locations for each drug complex. Predictive models, developed using multiple linear regression and IBM Watson artificial intelligence (AI), achieved mean absolute percentage errors below 12%, driven by AI-identified key variables. The predictive models included mainly quantitative descriptors collected from datasets as well as computed ones. In addition, a water-soluble fullerene was used to compare results obtained by DFTB3 with a conventional density functional theory approach. These findings promise to enhance breast cancer chemotherapy by leveraging fullerene-based drug nanocarriers.
ER  -

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Lee, Y.; Min, J.; Kim, S.; Ko, J. AI‐Driven Microphysiological Systems for Advancing Nanoparticle Therapeutics. Advanced Intelligent Systems 2025. doi:10.1002/aisy.202500022
Obijiofor, O. C.; Novikov, A. S. Exploring the role of density functional theory in the design of gold nanoparticles for targeted drug delivery: a systematic review. Journal of molecular modeling 2025, 31, 186. doi:10.1007/s00894-025-06405-9
Qin, Y.; Xu, J.; Liang, Z.; Teng, H.; Zhan, D.; Xu, H. Raman Spectroscopy of Fullerenes: From C60 to Functionalized Derivatives. Molecules (Basel, Switzerland) 2025, 30, 738. doi:10.3390/molecules30030738

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