Smart big data app for determining modeling of human genome, virus and medicinal compounds to healing any disease especially for Covid-19 by meta-deep AI reinforcement learning using core engine container system

Solving health problems is part of the enigma in finding candidate solutions, one of which is drugs for certain diseases, especially Covid-19, which include those the extensive and complete research of which involves the Human Genome, viruses, and medicinal compounds. Some of the obstacles are the limited knowledge as an approach that to date tends to use the conventional Machine Learning (CML) algorithms and the analysis of human DNA codes, viruses, and medicinal compound codes which still incline to be partial, resulting in discrete conclusions and premature modeling patterns. In this study, a computer simulation model prototyping approach such as in silico as the basis of modeling with meta-deep AI Reinforcement Learning algorithms is employed which provides leniency or flexibility in the machine learning process by integrating Deep Q-Learning, supervised, unsupervised, and other metaheuristic algorithms to obtain automatic and optimal modeling results from convergent learning through natural interactions between the elements involved, i.e., the Human Genome, viruses and medicinal compounds. In this regard, these elements are observed completely or impartially. Pieces of DNA are supported by the use of the core engine container system-based big data technology or another technology equivalent to it. In accordance with the test results, it is expected that a modeling of interaction patterns between very stable and significant elements from the results of the analysis in a computer that leads to the results for optimizing the health of the human body for general cases or for healing any disease, especially Covid-19, is obtained.