https://www.cnblogs.com/JustHaveFun-SAN/archive/2013/03/28/2987826.html

Monte Carlo methods (or Monte Carlo experiments) are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results i.e. by running simulations many times over in order to calculate those same probabilities heuristically just like actually playing and recording your results in a real casino situation: hence the name. They are often used in physical and mathematical problems and are most suited to be applied when it is impossible to obtain a closed-form expression or infeasible to apply a deterministic algorithm. Monte Carlo methods are mainly used in three distinct problems: optimization, numerical integration and generation of samples from a probability distribution.

1. SIMONA http://int.kit.edu/nanosim/simona

2. ProtoMS https://www.essexgroup.soton.ac.uk/ProtoMS/download.html  and http://chryswoods.com/main/software.html 

3. BOSS and MCPRO http://zarbi.chem.yale.edu/software.html

4. Sire  https://siremol.org/pages/tutorials.html  and http://chryswoods.com/main/software.html 

5. PELE https://bio.tools/PELE-MSM， https://github.com/pele-python/mcpele，https://www.nostrumbiodiscovery.com/technologies/peleplat/

6. faunus  http://faunus.sourceforge.net/

7. MCS http://robotics.stanford.edu/~itayl/mcs/

8. PROFASI  https://www.atp.lu.se/cbbp/software/profasi/request   https://www.cecam.org/workshop-details/200

9. MCPU https://faculty.chemistry.harvard.edu/shakhnovich/software

10.  py-MCMD combination of  GOMC and NAMD https://py-mcmd.readthedocs.io/en/latest/index.html

      This Python code enables hybrid molecular dynamics/Monte Carlo (MD/MC) simulations using NAMD and the GPU Optimized Monte Carlo (GOMC) software