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htmd.adaptive.adaptivegoal module#

class htmd.adaptive.adaptivegoal.AdaptiveGoal#

Bases: AdaptiveMD

Adaptive class which uses a Markov state model for respawning

AdaptiveMD uses Markov state models to choose respawning poses for the next epochs. In more detail, it projects all currently retrieved simulations according to the specified projection, clusters those and then builds a Markov model using the discretized trajectories. From the Markov model it then chooses conformations from the various states based on the chosen criteria which will be used for starting new simulations.

Parameters:

  • app (SimQueue object, default=None) – A SimQueue class object used to retrieve and submit simulations

  • project (str, default='adaptive') – The name of the project

  • nmin (int, default=0) – Minimum number of running simulations

  • nmax (int, default=1) – Maximum number of running simulations

  • nepochs (int, default=1000) – Stop adaptive once we have reached this number of epochs

  • nframes (int, default=0) – Stop adaptive once we have simulated this number of aggregate simulation frames.

  • inputpath (str, default='input') – The directory used to store input folders

  • generatorspath (str, default='generators') – The directory containing the generators

  • dryrun (boolean, default=False) – A dry run means that the adaptive will retrieve and generate a new epoch but not submit the simulations

  • updateperiod (float, default=0) – When set to a value other than 0, the adaptive will run synchronously every updateperiod seconds

  • coorname (str, default='input.coor') – Name of the file containing the starting coordinates for the new simulations

  • lock (bool, default=False) – Lock the folder while adaptive is ongoing

  • datapath (str, default='data') – The directory in which the completed simulations are stored

  • filter (bool, default=True) – Enable or disable filtering of trajectories.

  • filtersel (str, default='not water') – Atom selection string for filtering. See more here

  • filteredpath (str, default='filtered') – The directory in which the filtered simulations will be stored

  • projection (Projection object, default=None) – A Projection class object or a list of objects which will be used to project the simulation data before constructing a Markov model

  • truncation (str, default=None) – Method for truncating the prob distribution (None, ‘cumsum’, ‘statecut’

  • statetype (('micro', 'cluster', 'macro'), str, default='micro') – What states (cluster, micro, macro) to use for calculations.

  • macronum (int, default=8) – The number of macrostates to produce

  • skip (int, default=1) – Allows skipping of simulation frames to reduce data. i.e. skip=3 will only keep every third frame

  • lag (int, default=1) – The lagtime used to create the Markov model. Units are in frames.

  • clustmethod (ClusterMixin class, default=<class ‘htmd.clustering.kcenters.KCenter’>) – Clustering algorithm used to cluster the contacts or distances

  • method (str, default='1/Mc') – Criteria used for choosing from which state to respawn from

  • ticalag (int, default=20) – Lagtime to use for TICA in frames. When using skip remember to change this accordinly.

  • ticadim (int, default=3) – Number of TICA dimensions to use. When set to 0 it disables TICA

  • contactsym (str, default=None) – Contact symmetry

  • save (bool, default=False) – Save the model generated

  • goalfunction (function, default=None) – This function will be used to convert the goal-projected simulation data to a ranking whichcan be used for the directed component of FAST.

  • ucscale (float, default=0.5) – Scaling factor for undirected component. Directed component scaling automatically calculated as (1-uscale)

  • nosampledc (bool, default=False) – Spawn only from top DC conformations without sampling

  • autoscale (bool, default=False) – Automatically scales exploration and exploitation ratios depending on how stuck the adaptive is at a given goal score.

Example

>>> crystalSS = MetricSecondaryStructure().project(Molecule('crystal.pdb'))[0]
>>>
>>> # First argument of a goal function always has to be a Molecule object
>>> def ssGoal(mol):
>>>     proj = MetricSecondaryStructure().project(mol)
>>>     ss_score = np.sum(proj == crystalSS, axis=1) / proj.shape[1]  # How many predicted SS match
>>>     return ss_score
>>>
>>> ag = AdaptiveGoal()
>>> ag.generatorspath = '../generators/'
>>> ag.nmin = 2
>>> ag.nmax = 3
>>> ag.projection = [MetricDistance('name CA', 'resname MOL', periodic='selections'), MetricDihedral()]
>>> ag.goalfunction = ssGoal
>>> ag.app = LocalGPUQueue()
>>> ag.run()
>>>
>>> # Or alternatively if we have a multi-argument goal function
>>> def ssGoalAlt(mol, ss):
>>>     proj = MetricSecondaryStructure().project(mol)
>>>     ss_score = np.sum(proj == ss, axis=1) / proj.shape[1]
>>>     return ss_score
>>> from joblib import delayed
>>> ag.goalfunction = delayed(ssGoalAlt)(crystalSS)
>>> ag.app = LocalGPUQueue()
>>> ag.run()
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