Analysis Crystal Structure of Sars-cov-2 Nsp3 Macrodomain Based on Optimal Multi Level of Deep Neurocomputing Technique

In an attempt to improve the analysis crystal structure of sars-cov-2 nsp3 macrodomain, a new deep learning neural network architecture called (DLSTM) is established in this work which combines a novel meta-heuristic optimization algorithm called (Lion-AYAD ) and deterministic structure network (DSN) with Determined set of rules (Knowledge Constructions (KC)) for each protein’s generation from synthesis tRNA based on the location of each component (i.e., U, C, G and A) in the triples of tRNA and other KC related to SMILE Structures. LSTM is one of the deep learning algorithms (DLA) from type neurocomputing contain specific feature not found on other DLA is memory also it proves their ability to give results with high accuracy in prediction problem but on other side LSTM required to determined many parameters based on try and error concept and have high complexity of computation therefore This work attempting to solve this gap through suggest new tool to determine the structure of network and parameters through one optimization algorithm called Lion-AYAD. that searching of the optimal (objective function, #Hidden Layers, #nodes in each Layers and wights for four gate unit in each layers) called DSN. With trained bidirectional DLSTM on the DNA sequence to generated protein get very pragmatic results from determined which protein active and inactive in injury sars-cov-2. on other side trained bidirectional DLSTM on SMILES to analysis crystal structure of sars-cov-2 nsp3 macrodomain get very high reconstruction rates of the test set molecules were achieved 95%. In general Lion-AYAD is one of optimization algorithm determined the set of rules to avoiding incorrect interactions of materials, finally add the KC that include apply four rules through synthesis each triplet tRNA to generated proteins and five Rules through synthesis each SMILE Structure.