The Research & Development (R&D) phase of drug development is a lengthy and
costly process. To revolutionize this process, we introduce our new concept
QMLS to shorten the whole R&D phase to three to six months and decrease the
cost to merely fifty to eighty thousand USD. For Hit Generation, Machine
Learning Molecule Generation (MLMG) generates possible hits according to the
molecular structure of the target protein while the Quantum Simulation (QS)
filters molecules from the primary essay based on the reaction and binding
effectiveness with the target protein. Then, For Lead Optimization, the
resultant molecules generated and filtered from MLMG and QS are compared, and
molecules that appear as a result of both processes will be made into dozens of
molecular variations through Machine Learning Molecule Variation (MLMV), while
others will only be made into a few variations. Lastly, all optimized molecules
would undergo multiple rounds of QS filtering with a high standard for reaction
effectiveness 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 machine
learning combined with quantum simulations. In this paper we will go over the
detailed design and framework of QMLS, including MLMG, MLMV, and QS.Comment: 13 pages, 6 figure