Parallel strategies have been developed in parts of the code to enhance the scalability in tasks such as HTC or general organization of molecular databases. In this sense, we have developed modules of PySoftK using concurrency where independently executing tasks are created and queued to use the available resources. This approach ensures that the calculator module can run utilizing all available resources combining task assignment and core usage for parallel codes such as GFN2-xTB 43 while efficiently avoiding bottlenecks, as displayed in Figure . In this sense, the user can maximize the parallel performance of the code by tuning variables such as number of available processors against simultaneous task management.