FAQ & Known issues

Known issues

Most problems we have faced have to do with the installation and Chimera-Conda incompatibilities. Those are covered in the pychimera documentation, so please check that list before raising an issue in this repository!

How can I cite GaudiMM? What licenses apply?

GaudiMM is scientific software, funded by public research grants. If you make use of GaudiMM in scientific publications, please cite it. It will help measure the impact of our research and future funding!

@preamble{ " \newcommand{\noop}[1]{} " }
   title = {GaudiMM: A Modular Multi-Objective Platform for Molecular Modeling},
   author = {Rodr{\'i}guez-Guerra Pedregal, Jaime and Sciortino, Giuseppe and Guasp, Jordi and Municoy, Mart{\'i} and Mar{\'e}chal, Jean-Didier},
   year = {\noop{2017}submitted},

GaudiMM itself is licensed under Apache License 2.0, but includes work from other developers, whose licenses apply. Please check the LICENSE file in the root directory for further details.

How many generations / Which population size should I pick?

This depends on the complexity of your search space (related to the number and type of genes in use), and the evaluation power of the chosen objectives. A simple job would work OK with 50 generations and populations of 100 individuals, while more complex ones would require 500 generations for populations of 1000 indivuals. This also depends on the values of mu and lambda parameters.

The output produces a lot of similar results and I want to focus on diversity

You should play with the cutoff value of the RMSD similarity section. If the RMSD of two potentially similar solutions is under the cutoff, one of them is discarded. However, this is only applied if the score of two solutions are the same; ie, if there is a draw.

To force draws, one can reduce the number of decimal positions returned by every objective, which is controlled by the precision parameter. It is setup globally in the output section, but can also be overriden by any objective. By default, precision is 3. Also, if you are using the gaudi.genes.search.Search gene, make sure its precision parameter is small enough so you don’t lose exploration efforts in too similar positions.

The output produces very different results and I am interested in clustered solutions

In this case, one should increase the precision value (both globally and in the gaudi.genes.search.Search gene, if it applies) and reduce the RMSD similarity cutoff.

What is the difference between atom_names and atom_types in some objectives or genes?

atom_names refers to the name attribute of chimera.Atom objects, while atom_types is applied with idatmType attributes. Normally, the name attribute is picked directly from the molecule file (PDB, mol2), while the idatmType is assigned algorithmically by UCSF Chimera.


Any further questions? Feel free to submit your inquiries to our issues page!