Mdsimaid: An Automatic Recommender For Optimization Of Fast Electrostatic Algorithms For Molecular Simulations

by Alice N. Ko Molecular dynamics (MD) for modeling the behavior of biological molecules is an important but di#cult task. This thesis presents an approach that incorporates ideas from recommender systems and automatic empirical optimization of software to simplify the tasks of setting up a computer model for MD and for choosing the optimal algorithm and parameters. It is intended to act as a guide for beginners to perform molecular modeling and also to present a concrete example; a program written in Python called MDSimAid that helps users to prepare molecular systems for MD simulations. MDSimAid uses a MD package called CHARMM (Chemistry at HARvard Molecular Mechanics) and ProtoMol (a framework for molecular dynamics simulations developed in our laboratory) to prepare the molecular systems for simulations, allowing users to bypass the need to understand the specific format and terminology as well as the protocol required by the simulation software. The motivation is to let biologists do the biology and let computer scientists handle the programming and algorithms. MDSimAid interacts with ProtoMol to decide which is the best algorithm to handle electrostatics and the optimal parameters for the algorithm in order to attain the highest accuracy in the shortest time possible. MDSimAid provides real time analysis and has the parameter-tuning and datamining abilities to find the optimal values for the parameters