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Ramin Ekhteiari Salmas, PhD





Ramin Ekhteiari Salmas, PhD

King's College London            Department of Chemistry          Britannia House                                            SE1 1DB; Trinity Street                   London; UK

Machine Learning and Simulation:

Molecular modelling techniques can be effective in researching biological systems at the atomic or molecular level. These technologies allow us to explore the chemical interactions that occur between protein atoms, resulting in secondary and tertiary structures. Classic molecular dynamics (MD) simulations, which generate thousands of trajectory frames in a given amount of time, are useful for sampling structural conformations and are required for protein conformational changes. Docking simulations, a low-time approach, can calculate the energy values released during protein-protein or ligand-protein binds. There are several approaches for calculating the free energy of biological systems, including Poisson-Boltzmann or generalised Born and surface area continuum solvation (MM/PBSA and MM/GBSA).  Monte Carlo (MC) techniques are time-independent simulations that sample various protein conformers using random processes. Our goal is to compute statistical thermodynamic terms while simulating atom dynamics using various simulation techniques or umbrella sampling. The umbrella sampling approach is highly suggested for investigating the structural changes of a system that result in signal transmission. Other statistical approaches used to analyse the dynamics and fluctuations of atoms include normal mode analysis (NMA) and principle component analysis (PCA).

Computer-Aided Drug Design (CADD):

Molecular modelling approaches are used in drug development research to digitally screen and rank massive ligand data sets based on their binding affinities inside targets. Docking simulations and quantitative structure-activity relationship models (QSAR models) are two of the most commonly used tools for this purpose. The ligands identified by these approaches may be promising candidates for further investigation in vitro and in vivo. Because virtual screening of vast data bases is less time-consuming and less expensive than experimental approaches, it has a better possibility of identifying novel strong ligands. It is strongly advised that the binding energy of novel compounds within targets chosen for in vitro or in vivo testing be anticipated theoretically before testing them experimentally. It can offer preliminary information on the characteristics of the ligands at that stage, which can be useful in determining how to choose samples and which samples should be included in the testing. In brief, in silico techniques are seen as a viable supplement to either in vivo or in vitro testing. It should be noted that a compound's strong docking score does not always imply that it is a powerful ligand for its target. The docking scores are calculated with several assumptions that might impact the final findings; therefore, they should be regarded as preliminary data that is transmitted for additional investigation. No docking score technique can simply assess a compound's activity or inactivity. I am very opposed to computed binding energies being disclosed without experimental approval; these findings can be problematic and may contrast with experimental evidence that will be presented in future research on those substances.

 Programming for biologists and physicians: 

I would so like to begin by writing a quote from Stephen Hawking:

"Whether you want to uncover the secrets of the universe, or you want to pursue a career in the 21st century, basic computer programming is an essential skill to learn."

class simulation(object):

    def __init__(self, time_step, MD_time):

        self.time_step = time_step

        self.MD_time = MD_time 

I would advise scientists working in molecular modelling domains to be proficient in at least one computer programming language, such as Python, C++, C, TCL, or FORTRAN. It should be noted that humans can be smarter than any computer but not quicker; thus, we must programme computers to work for us in order to improve the pace of our computations. Personally, I prefer working with an object-oriented language such as Python. Python can simply code all of the purposes necessary in bioinformatic investigations. In addition to the programming provided by these capabilities, we must remember to create new modules utilising our math expertise. We must remember that science will not exist until mathematical formulae are implemented. Mathematics is regarded as a powerful key in addressing difficulties in our research - it may help us think about how to deal with problems before we solve them. You are welcome to contact me if you want assistance in this area.