Computational biology involves the development and application of data-analytical and theoretical methods, mathematical modelling and computational simulation techniques to the study of biological systems. The field is broadly defined and includes foundations in biology, applied mathematics, statistics, biochemistry, chemistry, biophysics, molecular biology, genetics, genomics, computer science, and evolution.

Biological data has now become commonplace, particularly in molecular biology and genomics. Many databases exist, covering various information types: for example, DNA and protein sequences, molecular structures, phenotypes and biodiversity. Databases may contain empirical data (obtained directly from experiments), predicted data (obtained from analysis), or, most commonly, both. They may be specific to a particular organism, pathway or molecule of interest. Alternatively, they can incorporate data compiled from multiple other databases. These databases vary in their format, access mechanism, and whether they are public or not. Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. Bioinformatics tools aid in comparing, analyzing and interpreting genetic and genomic data and more generally in the understanding of the biological pathways and networks that are an important part of systems biology. In structural biology, it aids in the simulation and modeling of DNA, RNA, proteins as well as biomolecular interactions. Homology modeling, protein threading and de novo (from scratch) physics-based modeling are important techniques for predicting protein structure.

Computational biology is transforming modern drug discovery and development, which traditionally is a costly and difficult process. Having the right information available to you at every stage in the pipeline is vital to bring safe and effective therapies to the market. CD ComputaBio helps you simplify every stage of drug discovery and development with innovative software and validation solutions. On-demand, we provide concise tutorials on the fundamental concepts of pharmaceutical process development. We also share the most valuable educational content for all professionals and scholars. Manuals and tutorials with hands-on training are well organized. For all users browsing our website, free learning covering methods and applications of various computational techniques used in biopharmaceutical industries are also available.

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Molecular Docking Software

Small-molecules bind to proteins within surface cavities. The prediction of these interactions is done through docking simulations. Structure-based virtual screening (molecular docking) has been used to discover new ligands based on target structures. Docking methods are widely applied and accepted nowadays in drug design. Two approaches are particularly popular within the molecular docking community. One […]

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Molecular Dynamics Analysis Tutorial

Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules, i.e. to sample molecular conformations. This is also the route to relate the microscopic movements and positions of the atoms to the macroscopic or thermodynamic quantities that can be measured experimentally.There are two major simulation methods to sample […]

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Molecular Modeling Tutorial

Computational modelling has gained an increasingly important role in biochemical and biomolecular sciences over the past decades. This is related to significant developments in terms of methodology and software, as well as the amazing technological advances in computational hardware, and fruitful connections across different disciplines. Virtual libraries of several million compounds searching for potential new […]

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Transcriptome Data Analysis Tutorial

As the price of transcriptome sequencing drops, the number of samples to be sequenced is gradually increasing. WGCNA (weighted gene co-expression network analysis), an analysis method suitable for large samples, is used in diseases and other traits and genes. Correlation analysis and other aspects are more and more widely used. The biggest advantage of WGCNA […]

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