Integrating Molecular Evolution and Computational Biology: Bridging Disciplines for Future Research

Molecular evolution, conventionally rooted in classical evolutionary theory and comparative biology, has entered a transformative era driven by advances in genomics, bioinformatics, and computational modeling. This review traces the conceptual foundations of molecular evolution, beginning with the central dogma and codon degeneracy, and explores how variations such as single nucleotide variants (SNVs) shape protein structure and function. It highlights the evolutionary implications of codon usage bias, substitution models, and the mutation and selection balance in across genomes. Recent advances in artificial intelligence (AI), machine learning, biostatistics, and mathematical modeling have revolutionized our understanding of molecular evolution. AI-driven approaches and mathematical algorithms enhance analyses of genetic variation, protein evolution, and evolutionary dynamics. Updated computational platforms such as IQ-TREE 2, RAxML-NG, BEAST 2, PAML, and HyPhy, along with R and Python-based pipelines, have revolutionized evolutionary studies by enabling accurate modeling of mutation dynamics, phylogenetic reconstructions, and selection analyses.Additionally, the chemistry of amino acid exchangeability introduces new perspectives in evolutionary studies. This convergence of computational biology with mathematics, chemistry, and data science has transformed evolutionary biology into a multidisciplinary and collaborative research area to solve long standing biological queries. This opens up opportunities for a successful career in multidisciplinary research in evolutionary biology.