Molecular dynamics (MD) comprises a technique for computer simulation of complex systems modeled at the atomic scale. Molecular dynamics simulations were one of the first simulation methods that emerged and were used in pioneering applications, as well as dynamics of liquids by Alderman and Wainwright.
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MD simulation, which was first developed in the late 1970s, has become a useful tool for many applications in the field of physics and chemistry. Since the 1970s, this technique has been used ubiquitously to study biologically significant macromolecules, such as proteins and nucleic acids, for their structure and dynamics.
Using this technique to simulate systems that have approximately 50,000 to 100,000 atoms is now common, with even 500,000 atoms being possible to simulate. The improvements in this technology are related to high-performance computing, which has aided in the growth and versatility of MD simulation.
This technique was used to overcome a critical limitation of complex calculations required for quantum-mechanical motions as well as chemical reactions of large molecular systems.
These calculations are too complex and intensive for even supercomputers. Being computationally challenging, the emergence of molecular dynamics simulations overcame this obstacle due to using simple estimations that were based on Newtonian physics, including Newton's laws of motion, to simulate atomic motions. This approach aided in reducing the complexity and dependence on intensive computational calculations.
Computational Drug Discovery
The use of computational drug discovery has been a revolutionary tool for the field of pharmaceuticals, with this technology being used to speed up and optimize the design and development of a novel drug candidate.
Using classical MD simulations enables the implementation of computational structure-based drug design (SBDD) strategies that account for the structural flexibility of the drug-target model system. The widespread use of this technology can be useful for drug discovery, with interactions between a drug and its target being simulated and optimized. Subsequently, this may potentially increase the number of drugs provided with Food and Drug Administration (FDA) approval.
With the drug discovery and development process being a time-consuming and complex procedure that can take up to 12 years as well as having high expenditure, the use of computational drug discovery may aid in streamlining and optimizing this process and creating a higher success rate.
Molecular Dynamics Simulations in Drug Discovery
Molecular dynamics simulations can provide information on protein and ligand interactions, which can be vital for increasing comprehension of a drug target's structure-function relationship. This can be useful for guiding the drug design process, as once a simulation is constructed, researchers can fully comprehend how a drug can interfere with the pathophysiology of a biological target.
Increasing the understanding of drug interactions can optimize the weeding process of novel drug candidates and ensure drugs created are more effective for their disease counterparts. Using MD simulations in drug discovery can also improve the success rate for drugs being approved and aid in exploring drug interactions for more challenging biological targets, such as within the brain, for neurodegenerative diseases.
This approach can be more useful for finding treatments and cures for rare orphan disorders, which can aid in reducing the disease burden for patient populations worldwide.
Current limitations
While molecular dynamics simulations have been successful in drug discovery, there are still two significant challenges for this technique. These two challenges include the force fields used in this process that can be further refined and high computational demands that prevent routine simulations that are larger than a microsecond in length. These limitations can result in cases where there is an inadequate sampling of conformational states.
An example of the limitation of high computational demands that lead to a time-consuming process includes a one-microsecond simulation of a relatively small system (estimated to be 25,000 atoms) running on 24 processors, which can take several months to complete.
This can be inconvenient for researchers and pharmaceutical companies attempting to optimize their drug candidates for functional interaction with a biological target. However, this period may be reduced with the development of technology, which can increase the speed of processors, such as through graphene.
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Future Outlook
The use of molecular dynamics (MD) simulations can be revolutionary for drug discovery, with simulations of atoms being the future of drug development. Using this technology has become widespread in many applications, and simulating drug interactions with their biological targets may increase their success once they are created.
This is beneficial for pharmaceutical companies, which may reduce the time they spend on novel drug candidates and the expenditure required for developing these drugs. This is also beneficial for patients waiting for a drug to be developed for their particular disease, including rare diseases without a cure.
Overall, the future of drug discovery, which can be more streamlined for a more effective drug development process, such as through MD simulations, may aid in decreasing the global disease burden.
Sources:
- Atanasov, A.G. et al. (2021) "Natural products in drug discovery: Advances and opportunities," Nature Reviews Drug Discovery, 20(3), pp. 200–216. Available at: https://doi.org/10.1038/s41573-020-00114-z.
- Bunker, A. and Róg, T. (2020) "Mechanistic understanding from molecular dynamics simulation in Pharmaceutical Research 1: Drug Delivery," Frontiers in Molecular Biosciences, 7. Available at: https://doi.org/10.3389/fmolb.2020.604770.
- De Vivo, M. et al. (2016) "Role of molecular dynamics and related methods in Drug Discovery," Journal of Medicinal Chemistry, 59(9), pp. 4035–4061. Available at: https://doi.org/10.1021/acs.jmedchem.5b01684.
- Durrant, J.D. and McCammon, J.A. (2011) "Molecular dynamics simulations and Drug Discovery," BMC Biology, 9(1). Available at: https://doi.org/10.1186/1741-7007-9-71.
- Gelpi, J. et al. (2015) "Molecular dynamics simulations: Advances and applications," Advances and Applications in Bioinformatics and Chemistry, p. 37. Available at: https://doi.org/10.2147/aabc.s70333.
- Liu, X. et al. (2017) "Molecular dynamics simulations and novel drug discovery," Expert Opinion on Drug Discovery, 13(1), pp. 23–37. Available at: https://doi.org/10.1080/17460441.2018.1403419.
Further Reading