# Tutorial: Your first GaudiMM calculation¶

Running a GaudiMM calculation is easy. All you need is a simple command:

gaudi run input_file.yaml


The question is… how do I create that input_file.yaml.

## How to create an input file from scratch¶

Input files in GaudiMM are formatted with YAML, which follows readable conventions that are still parseable by computers. To easily edit YAML files, we recommend using a text editor that supports syntax highlighting, such as Sublime Text 3, Atom or Visual Studio Code. If you use .yaml as the extension of the input filename, the editor will colorize the text automatically. Else, you can always configure it manually. Check the docs of your editor to do so. If you don’t want syntax highlighting it’s fine, GaudiMM will work the same.

Input files must contain the sections mentioned above: output, ga, similarity, genes, and objectives. Each of this sections it’s defined with a colon and a newline, and its contents will be inside an indented block. For readability, I usually insert a blank line between sections.

output:
contents of output

ga:
contents of ga

similarity:
contents of similarity

genes:
contents of genes

objectives:
contents of objectives


Note

The API documentation of gaudi.parse.Settings contains the full list of parameters for output and ga sections. Programmatically defined default values are always defined in gaudi.parse.Settings.default_values. The appropriate types and whether they are required or not are defined in gaudi.parse.Settings.schema. GaudiMM will check if the submitted values conform to these rules and report any possible mistakes.

### The output section¶

This section governs how the results and reports will be created. Everything is optional, since each key has a default value, but it is preferrable to at least specify the name of the job (otherwise, it will be set to five random characters), and the path where the result files will be written. Like this:

output:
name: some_example
path: results


Note

All the relative paths in GaudiMM are relative to the location of the input file, not the working directory. To ease this difference, we recommend running the jobs from the same folder where the input file is located. Of course, you can always use absolute paths.

### The ga section¶

This section hosts the parameters of the Genetic Algorithm GaudiMM uses. Unless you know what you are doing, the only values you should modify are population and generations. To see the appropriate values, refer to FAQ & Known issues. For example:

ga:
population: 200
generations: 100


### The similarity section¶

This section contains the parameters to the similarity operator, which, given two individuals with the same fitness, whether they can be considered the same solution or not. This section is deliberatedly loose: you define the Python function to call, together with its positional and keyword arguments.

For the time being, the only similarity function we ship is based on the RMSD of the two structures: gaudi.similarity.rmsd(). The arguments are which Molecule genes should be compared and the RMSD threshold to consider whether they are equivalent or not.

similarity:
module: gaudi.similarity.rmsd
args: [[Ligand], 1.0]
kwargs: {}


### The genes section¶

This section describes the components of the exploration stage of the algorithm; ie, the features of each Individual in the population. While the previous sections were dictionaries (this is, a collection key-value pairs), the genes and objectives section is actually a list of dictionaries. As a result, you need to specify them like this:

genes:
-   name: Protein
module: gaudi.genes.molecule
path: /path/to/protein.mol2

-   name: Torsion
module: gaudi.genes.torsion
target: Ligand
flexibility: 360


Notice the dash - next to name. This, and the extra indentation, define a list. Each element of this list is a new gene. Each gene must include two compulsory values:

• name. A unique identifier for this gene. If you add two genes with the same name, GaudiMM will complain.
• module. The Python import path to the module that contains the gene. All GaudiMM builtin genes are located at gaudi.genes.

All other parameters are determined by the chosen gene. Check the corresponding documentation for each one!

Note

How do I know which genes to use?
Unless you code a gene of your own to replace it, you will always need one or more gaudi.genes.molecule.Molecule genes. Then, choose the flexibility models you want to implement on top of such molecule. Several examples are provided in GaudiMM basics (start here!).

### The objectives section¶

Like the genes section, the objectives section is also a list of dictionaries, so they follow the same syntax:

-   name: Clashes
module: gaudi.objectives.contacts
which: clashes
weight: -1.0
probes: [Ligand]

-   name: LigScore
module: gaudi.objectives.ligscore
weight: -1.0
proteins: [Protein]
ligands: [Ligand]
method: pose


In addition to the required name and module parameters, each objective needs a weight parameter. If set to 1.0, the algorithm will maximize the score returned by the objective; if set to -1.0, it will be minimized. Theoretically, any other positive or negative float will work, but stick to the convention of using 1.0 or -1.0.

Any other parameters present in an objective are responsibility of that objective, and are specified in its corresponding documentation.

That’s it! Now save it with a memorable filename and run it!

## How to run your input file¶

Let’s get back to the beginning of the tutorial: all you need to do is typing:

gaudi run input_file.yaml


If everything is fine, you’ll see the following output in the console:

\$> gaudi run input_file.yaml

.g8"""bgd       db   7MMF'   7MF'7MM"""Yb. 7MMF'
.dP'     M      ;MM:    MM       M    MM    Yb. MM
dM'            ,V^MM.   MM       M    MM     Mb MM  7MMpMMMb.pMMMb.  7MMpMMMb.pMMMb.
MM             ,M  MM   MM       M    MM      MM MM    MM    MM    MM    MM    MM    MM
MM.    7MMF'  AbmmmqMA  MM       M    MM     ,MP MM    MM    MM    MM    MM    MM    MM
Mb.     MM   A'     VML YM.     ,M    MM    ,dP' MM    MM    MM    MM    MM    MM    MM
"bmmmdPY .AMA.   .AMMA.bmmmmd"'  .JMMmmmdP' .JMML..JMML  JMML  JMML..JMML  JMML  JMML.
------------------------------------------------------------------------------------------
GaudiMM: Genetic Algorithms with Unrestricted Descriptors for Intuitive Molecular Modeling
2017, InsiliChem · v0.0.2+251.g122cdf0.dirty

Launching job with...
Genes: Protein, Ligand, Rotamers, Torsion, Search
Objectives: Clashes, Contacts, HBonds, LigScore


After the first iteration is complete, the realtime report data will kick in:

gen progress    nevals  speed       eta     avg                                                 std                                                 min                                     max
0   4.76%       20      1.25 ev/s   0:16:34 [  3.080e+03  -2.690e+02   7.500e-01   1.179e+03]   [  1.027e+03   7.200e+01   8.292e-01   4.964e+02]   [ 584.881 -398.517    0.     144.78 ]   [  4.753e+03  -1.053e+02   3.000e+00   2.066e+03]
1   9.52%       60      1.28 ev/s   0:16:32 [  2.787e+03  -2.659e+02   1.400e+00   9.142e+02]   [ 1198.699    98.675     1.393   484.309]           [ 415.912 -398.517    0.      16.45 ]   [ 4538.766   -71.562     5.     1854.63 ]


The first time you see this it might result too confusing, especially if the terminal wraps long lines. Let’s describe each tab-separated column:

• gen. The current generation.
• progress. Percentage of completion of the job. This is estimated with the expected number of operations: $$(generations + 1) * lambda\_ * (cxpb + mutpb)$$
• nevals. Number of evaluations performed in current generation.
• speed. Estimated number of evaluations per second. This does not take into account the time spent in the variation stage.
• eta. Estimated time left.
• avg. Average of all the fitness values reported by each objective in the current generation. They are listed in the order given in the input file, and also reflected above, after the Launching job with… line.
• std. Same as avg, but for the standard deviation.
• max. The maximum fitness value reported by each objective in the current generation.
• min. Same as above, but for the minimum value.

If the setting check_every in the output section is greater than zero, GaudiMM will dump the current population every check_every generations. That way, you can assess the progress visually along the simulation.

Also, if you feel that the algorithm has progressed enough to satisfy your needs, you can cancel it prematurely with Ctrl+C. GaudiMM will detect the interruption and offer to dump the current state of the simulation:

^C[!]

Interruption detected. Write results so far? (y/N):


Answer y` and wait a couple of seconds while GaudiMM writes the results. To analyze them, check the following tutorial: