Discovering Dually Active Anti-cancer Compounds with a Hybrid AI-structure-based Approach

Cancer’s persistent growth often relies on its
ability to
maintain telomere length and tolerate the accumulation of DNA damage.
This study explores a computational approach to identify compounds
that can simultaneously target both G-quadruplex (G4) structures and
poly(ADP-ribose) polymerase (PARP)1 enzyme, offering a potential multipronged
attack on cancer cells. We employed a hybrid virtual screening (VS)
protocol, combining the power of machine learning with traditional
structure-based methods. PyRMD, our AI-powered tool, was first used
to analyze vast chemical libraries and to identify potential PARP1
inhibitors based on known bioactivity data. Subsequently, a structure-based
VS approach selected compounds from these identified inhibitors for
their G4 stabilization potential. This two-step process yielded 50
promising candidates, which were then experimentally validated for
their ability to inhibit PARP1 and stabilize G4 structures. Ultimately,
four lead compounds emerged as promising candidates with the desired
dual activity and demonstrated antiproliferative effects against specific
cancer cell lines. This study highlights the potential of combining
Artificial Intelligence and structure-based methods for the discovery
of multitarget anticancer compounds, offering a valuable approach
for future drug development efforts.