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We introduce an innovative hybrid quantum-classical generative approach for designing new ligands and apply it to a protein target involved in cancer. Our method synergistically combines the strengths of Long Short-Term Memory (LSTM) networks, Quantum Circuit Born Machines (QCBMs), the STructure Obtained from NEtwork Decoding of SELF-referencIng Embedded Strings (STONED-SELFIES) algorithm, the VirtualFlow 2.0 platform, and the Chemistry42 platform.
Christoph Gorgulla, Lead Scientist, St. Jude Children’s Research Hospital
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