Computational Structural Biology

The field of computational biology has made significant progress.
It focuses on the influence of computational structural biology on protein structure prediction tools, macromolecular function and protein design, and drug discovery techniques. 

The availability of vast and heterogeneous data is rising, and computational biology can integrate, assess, and interpret these different information-rich resources effectively. Using vast genomic, transcriptomic, and proteomic data, as well as structural foot printing, computational biology has made significant steps toward more accurate multistate biological modelling. Moreover, it has developed algorithms for predicting molecular interactions and arranging them into interconnected biological pathways. 

Based on the goals and types of experimental data that are available, computational biology covers a wide range of topics, such as sequence and structure analysis and how they relate to function, evolution and population genomics, regulatory and metabolic networks, image analysis, and disease. Computational biology often solves problems by analysing huge amounts of genomic, proteomic, microarray, cell and tissue imaging, and clinical data to find significant statistical trends and link them to results. It uses high-throughput genomic and proteomic methods to combine data, find and validate biomarkers and possible treatment targets, and get results quickly into the clinic.