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Jakarta Post

Finding new drugs via computer

  • Mei Pakpahan

    The Jakarta Post

Bandung   /   Wed, January 22, 2014   /  12:30 pm
Finding new drugs via computer Digital PDB models, such as this one of green fluorescent proteins found in jellyfish, are used to develop new drugs.(Worldwide Protein Data Bank)

Digital PDB models, such as this one of green fluorescent proteins found in jellyfish, are used to develop new drugs.(Worldwide Protein Data Bank)

Scientists today are not restricted to lab experimentation to develop drugs; they can also use computer simulations.

Society owes a debt of thanks to John von Neumann and Stanislaw Ulam, two mathematicians who solved the puzzling problem of neutron behavior using computer simulations back in the Second World War.

This was the birth of computer simulation and it soon became popular in business and industry.

Today, computer simulation also has an important role in pharmaceutical research. Before conducting experiments with new drugs, scientists, including mathematicians, physicists, medicinal chemists and computer scientists collaborate to simulate how the new formula of drugs will interact with targeted diseases.

All the known three-dimensional structures of proteins including bacteria and viruses are now stored in a huge database collection, and listed as protein data bank (.pdb) files.

This pdb data has been obtained by X-ray crystallography or NMR spectroscopy experimentally and submitted by biologists and biochemists from around the world. The pdb files and all their information are accessible and downloadable for free from websites.

There are also numbers of accessible chemical compounds including drugs repositories online, allowing for virtual screening (VS) in terms of drugs discovery.

VS is defined as a fast automatic technique to evaluate very large libraries of chemical compounds using computer programs. The purpose is simple: to discover new drug candidates for many biologically relevant targets.

Unlike the conventional method, VS uses computer algorithms to filter chemical compounds and then select potential drug candidates rapidly. In general, VS is divided into two methods: structured-based screening and ligand-based screening. Ligand refers to a small molecule, and can be a drug that binds to a protein as its receptor.

Structured-based screening or docking predicts the interaction geometries between drug and targeted protein or disease and calculates the affinity of drugs binding to their targets.

Ligand-based screening performs a similarity search, compares molecular-similarity analyses of the chemical compounds with known and unknown substances. In a nutshell, if the structure of a drug target is known, docking can be performed.

However, if the only clue we have is that there is a known compound that shows activity and potency for an unknown structure of a drug target, then ligand-based screening should be performed.

For many years medicinal chemists have struggled with identifying and deciding which compounds to synthesize. Many drug candidates fail in clinical trials because they are unrelated to the potency against the intended drug target. Traditional drug discovery methods also rely on the trial-and-error testing of chemical compounds on animals and analyzing the chemical effects occurring in the animals. But now computers can help to assess and rank the drug candidates, design new drugs and reduce the cost and time of experimentation.

The computer also helps to give insights into the interaction between a drug and its target, helping the scientist to understand the behavior of these molecules within a nanosecond timescale in which the human eye cannot record details.

The performance of the computer is also taken into account.

Performing a computer simulation in nanoseconds can take days or even months depending on how large the molecules are, how long the simulation is, how efficient the program that is being used for the simulation, and how good the infrastructure is.

This also has been a challenge in the high-performance computing (HPC) research field.

Another challenge is storage and data management. With the hugely increased number of chemical compounds and list of protein structures found, the storage capacity also needs to be upgraded.

Large amounts of data will cause problems in managing data for query times. However, great efforts have been made to improve the ability of computer simulation. Many interdisciplinary studies have made attempts to produce better algorithms and methods and infrastructure in order to optimize the computational drug-discovery processes.

Nonetheless, apart from these challenges, the computer has positively affected modern pharmaceutical research.

More than one branch of knowledge can now take a part in improving the human capacity to fight diseases, even those without a direct knowledge of conducting chemical experimentation or medicinal chemistry.

The future is bright; perhaps someday we will identify a new drug that can cure a disease within a few seconds. This is just the beginning.

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