A Grid Computing-Based Monte Carlo Docking Simulations Approach for Computational Chiral Discrimination

2005 ◽  
pp. 446-455 ◽  
Author(s):  
Youngjin Choi ◽  
Sung-Ryul Kim ◽  
Suntae Hwang ◽  
Karpjoo Jeong
Keyword(s):  
Monte Carlo ◽  
Grid Computing ◽  
Chiral Discrimination ◽  
Docking Simulations

Monte Carlo docking simulations of cyclomaltoheptaose and dimethyl cyclomaltoheptaose with paclitaxel

2002 ◽  
Vol 337 (6) ◽  
pp. 549-555 ◽  
Author(s):  
Hyunmyung Kim ◽  
Jungwon Choi ◽  
Hyun-Won Kim ◽  
Seunho Jung
Keyword(s):  
Monte Carlo ◽  
Docking Simulations

MONTE CARLO SIMULATION ON COMPUTATIONAL FINANCE FOR GRID COMPUTING

2012 ◽  
Vol 03 (03) ◽  
pp. 1250010 ◽  
Author(s):  
NIKOLAOS P. PREVE ◽  
EMMANUEL N. PROTONOTARIOS
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Grid Computing ◽  
Monte Carlo Methods ◽  
Stock Prices ◽  
Exact Result ◽  
Simulation Method ◽  
Deterministic Algorithm ◽  
Simulation Based ◽  
Computer Resources

Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo methods are often used in simulating complex systems. Because of their reliance on repeated computation of random or pseudo-random numbers, these methods are most suited to calculation by a computer and tend to be used when it is infeasible or impossible to compute an exact result with a deterministic algorithm. In finance, Monte Carlo simulation method is used to calculate the value of companies, to evaluate economic investments and financial derivatives. On the other hand, Grid Computing applies heterogeneous computer resources of many geographically disperse computers in a network in order to solve a single problem that requires a great number of computer processing cycles or access to large amounts of data. In this paper, we have developed a simulation based on Monte Carlo method which is applied on grid computing in order to predict through complex calculations the future trends in stock prices.

scholarly journals Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253760
Author(s):  
Gwangho Lee ◽  
Gun Hyuk Jang ◽  
Ho Young Kang ◽  
Giltae Song
Keyword(s):  
Monte Carlo ◽  
Binding Affinity ◽  
Protein Interaction ◽  
Score Function ◽  
Target Protein ◽  
Dimensional Structure ◽  
Tree Search ◽  
Specific Sequence ◽  
Monte Carlo Tree Search ◽  
Docking Simulations

Oligonucleotide-based aptamers, which have a three-dimensional structure with a single-stranded fragment, feature various characteristics with respect to size, toxicity, and permeability. Accordingly, aptamers are advantageous in terms of diagnosis and treatment and are materials that can be produced through relatively simple experiments. Systematic evolution of ligands by exponential enrichment (SELEX) is one of the most widely used experimental methods for generating aptamers; however, it is highly expensive and time-consuming. To reduce the related costs, recent studies have used in silico approaches, such as aptamer-protein interaction (API) classifiers that use sequence patterns to determine the binding affinity between RNA aptamers and proteins. Some of these methods generate candidate RNA aptamer sequences that bind to a target protein, but they are limited to producing candidates of a specific size. In this study, we present a machine learning approach for selecting candidate sequences of various sizes that have a high binding affinity for a specific sequence of a target protein. We applied the Monte Carlo tree search (MCTS) algorithm for generating the candidate sequences using a score function based on an API classifier. The tree structure that we designed with MCTS enables nucleotide sequence sampling, and the obtained sequences are potential aptamer candidates. We performed a quality assessment using the scores of docking simulations. Our validation datasets revealed that our model showed similar or better docking scores in ZDOCK docking simulations than the known aptamers. We expect that our method, which is size-independent and easy to use, can provide insights into searching for an appropriate aptamer sequence for a target protein during the simulation step of SELEX.

Sign in / Sign up

Export Citation Format

Share Document