Multiple sampling strategies and scoring for docking

Multiple sampling strategies and scoring functions can be used for integration in Opendock. In terms of sampling, this framework currently supports simulated annealing, Monte Carlo optimization, and can also support methods such as genetic algorithms and particle swarm optimization to optimize the positions of small molecules.

1. Using multiple sampling strategies

In the following example, a GeneticAlgorithmSampler is used for sampling. Similarly, the ligand and receptor objects are required. Here, 100 chromosomes are created and the scoring function is VinaSF class.

from opendock.sampler.ga import GeneticAlgorithmSampler

# define scoring function
sf = VinaSF(receptor, ligand)
vs = sf.scoring()
print("Vina Score ", vs)

# ligand center
xyz_center = ligand._get_geo_center().detach().numpy()[0]
print("Ligand XYZ COM", xyz_center)

# initialize GA
GA = GeneticAlgorithmSampler(ligand, receptor, sf,
                             box_center=xyz_center,
                             box_size=[20, 20, 20], )
# run ga sampling
init_lig_cnfrs =[torch.Tensor(ligand.init_cnfrs.detach().numpy())]
ligand.cnfrs_,receptor.cnfrs_= ga._random_move(init_lig_cnfrs ,receptor.cnfrs_)
ga.sampling(100)

collected_cnfrs += ga.ligand_cnfrs_history_
collected_scores += ga.ligand_scores_history_

# make clustering
...
# final scoring and ranking
...
# save ligand conformations
write_ligand_traj(_cnfrs_list, ligand,
                  os.path.join(configs['out'], 'test_ga.pdbqt'),
                  information={"VinaScore": _scores},
                  )

2. Using multiple scoring functions

Sometimes, it could be better to define some hybrid scoring functions for more accurate sampling and docking. In the following example, two scoring functions VinaSF and DeepRmsdSF are implemented and combined together by different weights. This scoring function (hybrid scoring function by VinaSF and DeepRmsdSF) could be used to guide pose optimization or global docking.

# define scoring function
sf1 = VinaSF(receptor, ligand)
vs = sf1.scoring()
print("Vina Score ", vs)

# define scoring function
sf2 = DeepRmsdSF(receptor, ligand)
vs = sf2.scoring()
print("DeepRMSD Score ", vs)

# combined scoring function
sf = HybridSF(receptor, ligand, scorers=[sf1, sf2], weights=[0.8, 0.2])
vs = sf.scoring()
print("HybridSF Score ", vs)

The following hybrid scoring function could be used for sampling.

from opendock.scorer.hybrid import HybridSF

# sf is the hybrid scoring function
sf = HybridSF(receptor, ligand, scorers=[sf1, sf2], weights=[0.8, 0.2])

# ligand center of the initial input ligand pose
xyz_center = ligand._get_geo_center().detach().numpy()[0]
print("Ligand XYZ COM", xyz_center)

# define sampler
print("Cnfrs: ",ligand.cnfrs_, receptor.cnfrs_)
mc = MonteCarloSampler(ligand, receptor, scoring_function=sf,
                       box_center=xyz_center,
                       box_size=[20, 20, 20],
                       random_start=True,
                       minimizer=adam_minimizer,
                       )
init_score = mc._score(ligand.cnfrs_, receptor.cnfrs_)
print("Initial Score", init_score)
#sampling
...

For this tutorial, all the basic material are provided and can be found in the opendock/opendock/protocol directory

You can find this script in the example folder of OpenDock available on Github. To execute it from a command line, go to your terminal/console/command prompt window. Navigate to the examples folder by typing

$ cd opendock/example/1gpn
$ python multiple_sampling_strategies_example.py -c vina.config #Using multiple sampling strategies
$ python multiple_scoring_functions_example.py -c vina.config #Using multiple scoring functions