Abstract: Systems and methods are provided for implementing a machine learning (ML) model based compiler, language translator, and/or a decompiler. For example, the system may receive a first source code in a first programming language, tokenize the first source code file forming tokenized code, generate a sequence vector of tokenized code, and provide the sequence vector of tokenized code as input to a trained ML model compiler. The output of the trained ML model compiler may create a second executable file or the source code in a second programming language.

Abstract: Systems and methods are provided for implementing a machine learning (ML) model based compiler, language translator, and/or a decompiler. For example, the system may receive a first source code in a first programming language, tokenize the first source code file forming tokenized code, generate a sequence vector of tokenized code, and provide the sequence vector of tokenized code as input to a trained ML model compiler. The output of the trained ML model compiler may create a second executable file or the source code in a second programming language.

Abstract: Systems and methods disclosed herein may include a parallel-processing graph-database solution for protein-sequence analytics to determine a viable therapeutic for a given condition, and may include: determining a protein sequence for the given condition; using sequence database to compare a query sequence of the sequence of the given condition with sequences of other known proteins in the sequence database using the sequence database to determine a similarity of the query sequence with sequences of the other known proteins in the sequence database based on the comparison; and querying a graph database based on the similarity of sequences to identify potential therapeutics that could be have an inhibitory effect on the given condition.