Our technology-driven era gave birth to a stand-alone field in sciences, namely computational sciences where computers and mathematical models are used to solve scientific and medical problems such as drug design/delivery, personalized medicine, nutrigenetics, and building artificial intelligence machines for decision making in treatments and surgeries.

In this article, each of the applications will be described, followed by a summary of the related research conducted at the College of Medicine and Health Sciences (CMHS), United Arab Emirates University (UAEU). - Computer-aided drug design (CADD) accelerates the drug discovery process in all its phases. It is used to analyze target structures, detect binding sites, generate candidate compounds, dock a molecule to its receptor, evaluate binding affinities, and optimize the druggability and ADMET (absorption, distribution, metabolism, and excretion - toxicity) properties.

In silico studies are used to filter large libraries of molecules to a refined list of molecules that is then passed for experimental testing. CADD is thus an effective tool for substantial savings in cost and time. - Personalized medicine is an ever-growing field where patients are treated on a personalized level based on their genome.

The idea of one medication fits all is replaced by the use of different drugs for different patients to maximize the curing levels while minimizing the side effects per patient. - Nutrigenetics is the study of the correlations among diet, genes and health. It is currently being used in preventive personalized treatment, where the diet intake is customized per patient according to his/her own genes and metabolism to prevent diseases. - Artificial intelligence (AI) is about training machines or robots to take autonomous decisions. The training happens through machine learning algorithms which heavily rely on computational sciences. In the health sector, AI is used to facilitate, diagnosis, surgeries, and even management-related tasks.

For example, AI robots are currently trained to analyze radiology imaging to efficiently diagnose a large number of cancer and Covid-19 cases. These applications involve big data generated and processed through exascale computing (1018 floating point operations per second). As of November 2020, the top 10 supercomputers in the world to perform such simulations are in Japan, USA, China, Germany, and KSA (https://www.top500.org/lists/top500/2020/11/). These machines are exceptional in their sizes (up to 7,630,848 cores) and speed performance (up to 148.8 petaflops).

At CMHS, UAEU computational research is conducted in various projects. Dr. Alya A. Arabi, from the Department of Biochemistry at CMHS, is working on health-related projects using computational chemistry. She is exploring the effect of external factors such as i) electric fields and ii) intercalators on mutations in the DNA, a project that can lead to personalized treatment. She is also interested in bioisosterism in drug design, a project that can feed an artificial intelligence model to predict new drug molecules.

Dr. Alya, a Passionate researcher says: I always strive to using the state-of-the-art technology in computational chemistry to find solutions for chronic diseases. Because of the rapid growth of Computational Sciences in recent years, it has become an indispensable tool that complements theory and experiment in myriad ground-breaking health-related research projects such as artificial intelligence, personalized medicine and beyond . In particular, Dr. Arabi is considering the effect of strong electric fields on the kinetic rates of tautomerizations in DNA base pairs, and on the binding affinities of environmental pollutants as intercalators that sandwich between DNA base pairs. It was found that electric fields up to 109 V/m can increase the rates of tautomerization by a factor of 4.

This study could lead to the personalized treatment of cancer patient using electric fields. In the drug design project, she uses computational chemistry and quantum theories to explains how substituted bioisosteres can, despite their structural, physical and chemical differences, have the same biological activity. It was found that the bioisosteric moieties within a molecule have average electrons densities that are similar up to three decimal places. This property can be used as a feature, among others, in artificial intelligence to discover new drug molecules.