Software for Drug Discovery and Protein Engineering: A Comparison Between the Alternatives and Recent Advancements in Computational Biology

“Omic” technologies (such as genomics, transcriptomics, proteomics, and metabolomics) generate huge databases that demand computational approaches to state novel conclusions. With the advent of machine learning and artificial intelligence algorithms, the analysis of biological data and protein engineering has taken a step forward. Different virtual screening servers and standalone software paved their importance in the initial phase of drug discovery, aiding in drug repurposing and high-throughput screening. Besides, interaction networks, often encountered in polypharmacology and network pharmacology, guide a researcher in target fishing and developing drug combinations. Visualization and prediction of molecular structures, modeling antibodies, and peptides including homology modeling are crucial to bioinformaticians and clinical biologists. Biological network analysis, pharmacophore modeling, molecular docking, and dynamics simulation are broadly exploited in the domain of computational biology and elucidate the mechanisms underlying biomolecular interactions, consequently revealing the orchestra of biological pathways. Considering the intended purposes, advantages, and limitations of the existing software, this chapter highlights only a fraction of popular platforms and encourages the readers to explore other alternatives in various domains of drug discovery and protein engineering.