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The Research & Development (R&D) phase of drug development is a lengthy andcostly process. To revolutionize this process, we introduce our new conceptQMLS to shorten the whole R&D phase to three to six months and decrease thecost to merely fifty to eighty thousand USD. For Hit Generation, MachineLearning Molecule Generation (MLMG) generates possible hits according to themolecular structure of the target protein while the Quantum Simulation (QS)filters molecules from the primary essay based on the reaction and bindingeffectiveness with the target protein. Then, For Lead Optimization, theresultant molecules generated and filtered from MLMG and QS are compared, andmolecules that appear as a result of both processes will be made into dozens ofmolecular variations through Machine Learning Molecule Variation (MLMV), whileothers will only be made into a few variations. Lastly, all optimized moleculeswould undergo multiple rounds of QS filtering with a high standard for reactioneffectiveness and safety, creating a few dozen pre-clinical-trail-ready drugs.This paper is based on our first paper, where we pitched the concept of machinelearning combined with quantum simulations. In this paper we will go over thedetailed design and framework of QMLS, including MLMG, MLMV, and QS.

Implementation of The Future of Drug Discovery: QuantumBased Machine Learning Simulation (QMLS)