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Quantum-inspired approach to the de-novo drug design

The purpose of drug design is to identify novel molecules that bind to a specific protein (relevant to a specific disease) and block (or enhance) the protein activity, thus changing the course of the disease. Drug molecules also have other certain necessary properties such as selectivity and safety. Due to increasing costs of drug development and a high failure rate of potential drug candidates, there is a continuous need for development of new innovative medicines. For example, the FDA-approval rate for new drugs that enter clinical trials is only 19%. In the last couple of years, there has been an interest in finding drug candidates from novel chemistry, leading designers to explore larger and larger chemical spaces. However, for most computer systems it takes too long to explore a large chemical space, especially those including over 1 billion molecules. We present here the use of a quantum-inspired technology for searching large chemical spaces as the means to significantly accelerate the first step of any computational drug design campaign.


You can access the open free paper visiting https://chemrxiv.org/articles/A_Quantum-Inspired_Approach_to_De-Novo_Drug_Design/12229232/1


Design and optimization of targeted drug-like compounds is an important part of the early stage drug discovery process. In this paper, we describe the use of a novel technique for rapid design of lead-like compounds for the Dengue viral RNA-dependent-RNA polymerase (RdRp). Initially, a large (>billions) fragment-based chemical library is designed by mapping relevant pharmacophores to the target binding pocket. The de-novo synthesis of molecules from fragments is formulated as a quadratic unconstrained binary optimization problem that can be solved using the quantum-inspired Digital Annealer (DA), providing an opportunity to take advantage of this fledgling, groundbreaking technology. The DA constrains the search space of molecules with drug-like properties that match the binding pocket and then optimizes for synthetic feasibility and novelty, thus offering significant commercial advantages over existing techniques.




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