Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS
The discovery of small molecules with therapeutic potential is a
long-standing challenge in chemistry and biology. Researchers have increasingly
leveraged novel computational techniques to streamline the drug development
process to increase hit rates and reduce the costs associated with bringing a
drug to market. To this end, we introduce a quantum-classical generative model
that seamlessly integrates the computational power of quantum algorithms
trained on a 16-qubit IBM quantum computer with the established reliability of
classical methods for designing small molecules. Our hybrid generative model
was applied to designing new KRAS inhibitors, a crucial target in cancer
therapy. We synthesized 15 promising molecules during our investigation and
subjected them to experimental testing to assess their ability to engage with
the target. Notably, among these candidates, two molecules, ISM061-018-2 and
ISM061-22, each featuring unique scaffolds, stood out by demonstrating
effective engagement with KRAS. ISM061-018-2 was identified as a broad-spectrum
KRAS inhibitor, exhibiting a binding affinity to KRAS-G12D at $1.4 \mu M$.
Concurrently, ISM061-22 exhibited specific mutant selectivity, displaying
heightened activity against KRAS G12R and Q61H mutants. To our knowledge, this
work shows for the first time the use of a quantum-generative model to yield
experimentally confirmed biological hits, showcasing the practical potential of
quantum-assisted drug discovery to produce viable therapeutics. Moreover, our
findings reveal that the efficacy of distribution learning correlates with the
number of qubits utilized, underlining the scalability potential of quantum
computing resources. Overall, we anticipate our results to be a stepping stone
towards developing more advanced quantum generative models in drug discovery.
Mohammad Ghazi Vakili Christoph Gorgulla AkshatKumar Nigam Dmitry Bezrukov Daniel Varoli Alex Aliper Daniil Polykovsky Krishna M. Padmanabha Das Jamie Snider Anna Lyakisheva Ardalan Hosseini Mansob Zhong Yao Lela Bitar Eugene Radchenko Xiao Ding Jinxin Liu Fanye Meng Feng Ren Yudong Cao Igor Stagljar Alán Aspuru-Guzik Alex Zhavoronkov