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Computational molecular modeling is an important tool for detailing molecular recognition mechanisms between proteins and inhibitors, which may be key to understanding and developing new drugs to treat many diseases. It describes the structure of biological molecules and pathogens, so it has a fundamental role in pharmacology and medicine, which is one of the most important advances in planning and discovering new drugs.
To explore the energy landscape described by the molecular mechanic's force field, i.e., to sample molecular conformations, a simulation is required. There are two major simulation methods to sample biomolecular systems: molecular dynamics (MD) and Monte Carlo (MC). MD is based on Newton’s second law of motion, which relates the force, acted upon an atom to its acceleration. An MD simulation is set up by assigning initial velocities and positions to all atoms in the system. MC is a statistical technique where new conformations are generated by a random walk-in phase space by assigning random displacements to the internal degrees of freedom. An MC simulation consists of many moves, which is a recipe for how to sample specific degrees of freedom.
Fig.1 Simulation techniques such as MD and MC. (Samuel, 2017)
Molecular simulations have been used in numerous applications, to obtain the structure and dynamics of many biomolecules to elucidate biochemical functions, processes, and pathways. Another common application of molecular simulation is the estimation of binding free energies of small molecules, e.g., drugs, to their targets. Scientists routinely use such simulations to aid in their drug design pipeline.
Fig.2 Examples of complex systems that can be simulated by all atom MD. (Samuel, 2017)
Computational chemistry is an important and dynamic research field within chemistry. Covering the full spectrum of the molecular and material sciences from an in-silico perspective, it remains at the forefront and maintains huge importance in the latest developments in chemistry. Computational chemistry has contributed enormously to the understanding of chemical reactivity. It is a powerful tool to predict the physicochemical properties of molecules. Computational chemistry provides additional information that is not possible to obtain from experiments, so it is a valuable complement and helps to legitimize models or theories that have little opportunity to be contrasted with reality.
Computer-based drug design (CBDD) or computer-aided drug design (CADD) refer to the application of different computational methodologies and algorithms for developing bioactive compounds. CBDD can be classified into two main classes: structure-based drug design (SBDD) and ligand-based drug design (LBDD). SBDD is based on the knowledge of the 3D structure of the target protein, using virtual screening techniques to search for molecules having complementarities toward the selected target. For SBDD, molecular docking, virtual screening, and molecular dynamics are the most important underlying methodologies. LBDD does not require knowledge of a protein, instead of using the information provided by known active and inactive compounds to find potential hits by similarity searches or quantitative structure-activity relationship studies (QSAR). The latter is usually the selected methodology when there is no structural information available of the target system.
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