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Computational Biology and the Future of Drug Discovery

Zain Tariq | January 30, 2023

The process of discovering a new drug is often long and expensive. The British Journal of Pharmaceuticals states that drug development can take 12-15 years and around 1 billion dollars from discovery to public availability. The timeframe and costs make each new drug a significant investment for pharmaceutical companies and drug researchers. Thus, scientists such as those in the Meiler Lab at Vanderbilt, are always looking for better and more efficient methods for drug discovery.

The first step in the drug discovery and development process is the discovery of a potential drug. This step involves finding and testing potential cures to a specific disease to make sure they don’t have unintended interactions in the body. Although this process sounds simple, it can often be the most time-consuming because of the millions upon millions of different compounds and interactions that need to be checked and tested to discover a new drug. 

Take, for example, the story of the development of a new drug for the inhibition of cytohesins, guanine nucleotide exchange factors that, when inhibited, can increase the liver’s resistance to insulin, according to a study in 2006. When scientists wanted to find a new version of the cytohesin inhibitor SecinH3, they began with the screening of over 2.6 million compounds. In the end, 145 drugs were selected for testing, and only 26 were found to be better inhibitors than SecinH3. Going from over 2.6 million compounds to only 26 is a task that is only possible after years of research, and recently, advances in computing.

At Vanderbilt, the Meiler Lab focuses on combining biology and computing to create more efficient methods of drug discovery. The Meiler Lab uses High Thorough-Put Screening (HTS) to analyze millions of compounds for a specific biological marker. A biological “marker” can be a functional group or structure that enables a drug to be a biological inhibitor or promoter. One computational method used for the selection of a biological marker is a model called Geometric Modeling, which uses a grid to map a target protein and then goes through a molecular database to find potential ligands.

Figure 1. Example of Computational Drug Discovery where a molecule was found and then checked against a database eventually identifying an antagonist/inhibitory molecule.

The Meiler Lab also specializes in using RosettaLigand, a software that randomly orients a molecule around a docking position to find the orientation that requires the least energy. This  orientation “energy” is basically a score of how hard it is for the molecule to bind to the protein in the body. A high orientation energy means docking is unlikely, while a low orientation energy means the molecule is viable to combine with a protein and function biologically. Here, computer algorithms play a vital role in simulating the “docking” procedure and subsequently analyzing the forces between functional groups to determine if the final product is energetically favorable. RosettaLigand was used in 2009 by Dr. Kaufmann to inhibit beta-amyloid deposit formation, a primary cause of Alzheimer’s.

The goal of researchers like Dr. Meiler is to find more efficient methods of searching, categorizing, and understanding the proteins/molecules that are key to a healthy body. As discussed in this article, the drug discovery process can be long and expensive. However, the application of computer science to biology is quickly making the discovery process easier, and will hopefully lead to better treatments for a variety of diseases in the near future.