Improving Cancer Drug Design

Posted on Jul 2, 2014

Improving Cancer Drug Design

Argonne researchers were able to demonstrate the potential of using QMC in their studies of ellipticine, a promising drug for uterine cancer treatment.

Research in drug action at the molecular level depends primarily on understanding and defining the physical/chemical interaction between the drug and its receptor, and then understanding how to connect this interaction to the pharmacological response. Over the past couple of decades, the screening of molecules for their active principle has relied greatly on the ability to model candidate molecules before experimental consideration. This task can only be achieved with an ab initio quantum chemistry theory, such as QMC, that takes electron correlation effects into account.

For their groundbreaking studies of ellipticine, the Argonne research team used the scalable QMCPACK simulation package. With Mira, the Blue Gene Q at ALCF, the researchers were able to obtain a highly accurate binding energy calculation (at chemical accuracy of ~1 kcal/mol) of ellipticine to DNA (33.6 ±0.9 kcal/mol), while other traditional methods had predicted no binding (DFT: -5 kcal/mol). This case study shows the reliability and efficiency of QMC for characterizing the binding energies for biological systems, providing a critical input for improved drug modeling efforts.

“Application of Diffusion Monte Carlo to Materials Dominated by van der Waals Interactions”, Anouar Benali, Luke Shulenberger, Nichols A. Romero, Jeongnim Kim, and O. Anatole von Lilienfeld. Journal of Chemical Theory and Computation (2014). http://pubs.acs.org/doi/abs/10.1021/ct5003225