Autism is a developmental disorder that affects around 62.1 million people globally. Estimates of its prevalence have been on the rise. Recent research suggests that in the United States alone, the cost of caring for individuals with autism could reach $461 billion by 2025, including medical expenses. Autism results from a combination of genetic and environmental factors, and molecular diagnosis can often be challenging. Therefore, there is a need for more reliable biomarkers to assist in clinical evaluation. Here, we employed a bioinformatics technique, Gene Set Enrichment Analysis (GSEA), that allows for the evaluation of whether specific genes associated with autism are related to common biological pathways and particular molecular processes using data extracted from genetic biobanks. Thus, it was possible to validate 910 genes related to autism by means of GSEA. The generated data indicated genetic convergence in a molecular pathway, suggesting that the disordered activation of the RAS-MAPK and PI3K-AKT signaling cascades converges in the mTOR pathway. Cell typification in silico indicated high expression in striated neurons, type D1 (p=5,947e-04) and type D2 (p=1,292e-05). In conclusion, our molecular pathway data can be used to assess, using computer modeling, whether new drug candidates for treating autism interact with proteins involved in the mTOR pathway, thus optimizing the screening of new drugs. In addition, with the evidence of such biomarkers and the development of easily accessible laboratory tests, in the future, the early clinical diagnosis of autism could be significantly improved.