ABSTRACT
Computer simulations have become one of the principal tools in theoretical studies of physical, chemical, and biological systems, and with the fast advancement of computational resources, simulation techniques have emerged as indispensable scientific and engineering tools. In particular, two simulation techniques established in the 1960s are now commonly applied to many aspects of medicinal chemistry and biophysics, where computer analyses and simulations can augment or explain experimental observations. These techniques are the Monte Carlo algorithms (1-9) and the molecular dynamics simulation techniques (10-32). Monte Carlo calculations represent an entirely different type of simulation than those based on molecular dynamics. The name “Monte Carlo” comes from the random-chance nature of the simulations, akin to the games of chance at Monaco’s gambling resort. Monte Carlo simulations are stochastic and use random numbers to sample from a probability distribution, usually the classical Boltzmann distribution, to obtain for instance thermodynamic properties, minimumenergy structures and/or rate coefficients, or to sample conformers as part of a global conformer search algorithm. The molecular dynamics (MD) simulation technique on the other hand is a deterministic method, where the time evolution of a system is determined by Newton’s equations of motion (i.e., positions, velocities, and accelerations of atoms). Hence, MD simulations not only provide information on a molecular level (i.e., in space) but also on the dynamics of a system (i.e., behavior in time). Experiments often do not provide the molecular information available from simulations. Therefore as schematically shown in Fig. 1, theoreticians, computational scientists, and experimentalists can have a synergistic interaction, leading to new insights into complex biological systems. Chemical and physical
Figure 1 Synergistic interaction between experimentalists, computational chemists, and theoreticians.
