def benchmark(reference_system, positions, platform_name=None, nsteps=500, timestep=1.0*unit.femtoseconds, factory_args=None):
"""
Benchmark performance of alchemically modified system relative to original system.
Parameters
----------
reference_system : simtk.openmm.System
The reference System object to compare with
positions : simtk.unit.Quantity with units compatible with nanometers
The positions to assess energetics for.
platform_name : str, optional, default=None
The name of the platform to use for benchmarking.
nsteps : int, optional, default=500
Number of molecular dynamics steps to use for benchmarking.
timestep : simtk.unit.Quantity with units compatible with femtoseconds, optional, default=1*femtoseconds
Timestep to use for benchmarking.
factory_args : dict(), optional, default=None
Arguments passed to AbsoluteAlchemicalFactory.
"""
from alchemy import AbsoluteAlchemicalFactory, AlchemicalState
# Create a factory to produce alchemical intermediates.
print("Creating alchemical factory...")
initial_time = time.time()
factory = AbsoluteAlchemicalFactory(reference_system, **factory_args)
final_time = time.time()
elapsed_time = final_time - initial_time
print("AbsoluteAlchemicalFactory initialization took %.3f s" % elapsed_time)
# Create an alchemically-perturbed state corresponding to nearly fully-interacting.
# NOTE: We use a lambda slightly smaller than 1.0 because the AlchemicalFactory does not use Custom*Force softcore versions if lambda = 1.0 identically.
lambda_value = 1.0 - 1.0e-6
alchemical_state = AlchemicalState(lambda_electrostatics=lambda_value, lambda_sterics=lambda_value, lambda_torsions=lambda_value)
platform = None
if platform_name:
platform = openmm.Platform.getPlatformByName(platform_name)
temperature = 300.*unit.kelvin
collision_rate = 90./unit.picoseconds
# Create the perturbed system.
print("Creating alchemically-modified state...")
initial_time = time.time()
alchemical_system = factory.createPerturbedSystem(alchemical_state)
final_time = time.time()
elapsed_time = final_time - initial_time
# Compare energies.
print("Computing reference energies...")
reference_integrator = openmm.LangevinIntegrator(temperature, collision_rate, timestep)
if platform:
reference_context = openmm.Context(reference_system, reference_integrator, platform)
else:
reference_context = openmm.Context(reference_system, reference_integrator)
reference_context.setPositions(positions)
reference_state = reference_context.getState(getEnergy=True)
reference_potential = reference_state.getPotentialEnergy()
print(reference_potential)
print("Computing alchemical energies...")
alchemical_integrator = openmm.LangevinIntegrator(temperature, collision_rate, timestep)
if platform:
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator, platform)
else:
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator)
alchemical_context.setPositions(positions)
alchemical_state = alchemical_context.getState(getEnergy=True)
alchemical_potential = alchemical_state.getPotentialEnergy()
print(reference_potential)
# Make sure all kernels are compiled.
reference_integrator.step(2)
alchemical_integrator.step(2)
# Time simulations.
print("Simulating reference system...")
initial_time = time.time()
reference_integrator.step(nsteps)
reference_state = reference_context.getState()
#reference_state = reference_context.getState(getEnergy=True)
#reference_potential = reference_state.getPotentialEnergy()
final_time = time.time()
reference_time = final_time - initial_time
print("Simulating alchemical system...")
initial_time = time.time()
alchemical_integrator.step(nsteps)
reference_state = alchemical_context.getState()
#alchemical_state = alchemical_context.getState(getEnergy=True)
#alchemical_potential = alchemical_state.getPotentialEnergy()
final_time = time.time()
alchemical_time = final_time - initial_time
seconds_per_day = (1.*unit.day)/(1.*unit.seconds)
print("TIMINGS")
print("reference system : %12.3f s for %8d steps (%12.3f ms/step; %12.3f ns/day)" % (reference_time, nsteps, reference_time/nsteps*1000, nsteps*timestep*(seconds_per_day/reference_time)/unit.nanoseconds))
print("alchemical system : %12.3f s for %8d steps (%12.3f ms/step; %12.3f ns/day)" % (alchemical_time, nsteps, alchemical_time/nsteps*1000, nsteps*timestep*(seconds_per_day/alchemical_time)/unit.nanoseconds))
print("alchemical simulation is %12.3f x slower than unperturbed system" % (alchemical_time / reference_time))
def benchmark(reference_system,
positions,
platform_name=None,
nsteps=500,
timestep=1.0 * unit.femtoseconds,
factory_args=None):
"""
Benchmark performance of alchemically modified system relative to original system.
Parameters
----------
reference_system : simtk.openmm.System
The reference System object to compare with
positions : simtk.unit.Quantity with units compatible with nanometers
The positions to assess energetics for.
platform_name : str, optional, default=None
The name of the platform to use for benchmarking.
nsteps : int, optional, default=500
Number of molecular dynamics steps to use for benchmarking.
timestep : simtk.unit.Quantity with units compatible with femtoseconds, optional, default=1*femtoseconds
Timestep to use for benchmarking.
factory_args : dict(), optional, default=None
Arguments passed to AbsoluteAlchemicalFactory.
"""
from alchemy import AbsoluteAlchemicalFactory, AlchemicalState
# Create a factory to produce alchemical intermediates.
print("Creating alchemical factory...")
initial_time = time.time()
factory = AbsoluteAlchemicalFactory(reference_system, **factory_args)
final_time = time.time()
elapsed_time = final_time - initial_time
print("AbsoluteAlchemicalFactory initialization took %.3f s" %
elapsed_time)
# Create an alchemically-perturbed state corresponding to nearly fully-interacting.
# NOTE: We use a lambda slightly smaller than 1.0 because the AlchemicalFactory does not use Custom*Force softcore versions if lambda = 1.0 identically.
lambda_value = 1.0 - 1.0e-6
alchemical_state = AlchemicalState(lambda_electrostatics=lambda_value,
lambda_sterics=lambda_value,
lambda_torsions=lambda_value)
platform = None
if platform_name:
platform = openmm.Platform.getPlatformByName(platform_name)
temperature = 300. * unit.kelvin
collision_rate = 90. / unit.picoseconds
# Create the perturbed system.
print("Creating alchemically-modified state...")
initial_time = time.time()
alchemical_system = factory.createPerturbedSystem(alchemical_state)
final_time = time.time()
elapsed_time = final_time - initial_time
# Compare energies.
print("Computing reference energies...")
reference_integrator = openmm.LangevinIntegrator(temperature,
collision_rate, timestep)
if platform:
reference_context = openmm.Context(reference_system,
reference_integrator, platform)
else:
reference_context = openmm.Context(reference_system,
reference_integrator)
reference_context.setPositions(positions)
reference_state = reference_context.getState(getEnergy=True)
reference_potential = reference_state.getPotentialEnergy()
print(reference_potential)
print("Computing alchemical energies...")
alchemical_integrator = openmm.LangevinIntegrator(temperature,
collision_rate, timestep)
if platform:
alchemical_context = openmm.Context(alchemical_system,
alchemical_integrator, platform)
else:
alchemical_context = openmm.Context(alchemical_system,
alchemical_integrator)
alchemical_context.setPositions(positions)
alchemical_state = alchemical_context.getState(getEnergy=True)
alchemical_potential = alchemical_state.getPotentialEnergy()
print(reference_potential)
# Make sure all kernels are compiled.
reference_integrator.step(2)
alchemical_integrator.step(2)
# Time simulations.
print("Simulating reference system...")
initial_time = time.time()
reference_integrator.step(nsteps)
reference_state = reference_context.getState()
#reference_state = reference_context.getState(getEnergy=True)
#reference_potential = reference_state.getPotentialEnergy()
final_time = time.time()
reference_time = final_time - initial_time
print("Simulating alchemical system...")
initial_time = time.time()
alchemical_integrator.step(nsteps)
reference_state = alchemical_context.getState()
#alchemical_state = alchemical_context.getState(getEnergy=True)
#alchemical_potential = alchemical_state.getPotentialEnergy()
final_time = time.time()
alchemical_time = final_time - initial_time
seconds_per_day = (1. * unit.day) / (1. * unit.seconds)
print("TIMINGS")
print(
"reference system : %12.3f s for %8d steps (%12.3f ms/step; %12.3f ns/day)"
% (reference_time, nsteps, reference_time / nsteps * 1000, nsteps *
timestep * (seconds_per_day / reference_time) / unit.nanoseconds))
print(
"alchemical system : %12.3f s for %8d steps (%12.3f ms/step; %12.3f ns/day)"
% (alchemical_time, nsteps, alchemical_time / nsteps * 1000, nsteps *
timestep * (seconds_per_day / alchemical_time) / unit.nanoseconds))
print("alchemical simulation is %12.3f x slower than unperturbed system" %
(alchemical_time / reference_time))
def testAlchemicalFactory(reference_system, coordinates, receptor_atoms, ligand_atoms, platform_name='Reference', annihilateElectrostatics=True, annihilateLennardJones=False):
"""
Compare energies of reference system and fully-interacting alchemically modified system.
ARGUMENTS
reference_system (simtk.openmm.System) - the reference System object to compare with
coordinates - the coordinates to assess energetics for
receptor_atoms (list of int) - the list of receptor atoms
ligand_atoms (list of int) - the list of ligand atoms to alchemically modify
"""
import simtk.unit as units
import simtk.openmm as openmm
import time
# Create a factory to produce alchemical intermediates.
print "Creating alchemical factory..."
initial_time = time.time()
factory = AbsoluteAlchemicalFactory(reference_system, ligand_atoms=ligand_atoms)
final_time = time.time()
elapsed_time = final_time - initial_time
print "AbsoluteAlchemicalFactory initialization took %.3f s" % elapsed_time
# Create an alchemically-perturbed state corresponding to nearly fully-interacting.
# NOTE: We use a lambda slightly smaller than 1.0 because the AlchemicalFactory does not use Custom*Force softcore versions if lambda = 1.0 identically.
lambda_value = 1.0 - 1.0e-6
alchemical_state = AlchemicalState(0.00, lambda_value, lambda_value, lambda_value)
alchemical_state.annihilateElectrostatics = annihilateElectrostatics
alchemical_state.annihilateLennardJones = annihilateLennardJones
#platform_name = 'Reference' # DEBUG
platform = openmm.Platform.getPlatformByName(platform_name)
# Create the perturbed system.
print "Creating alchemically-modified state..."
initial_time = time.time()
alchemical_system = factory.createPerturbedSystem(alchemical_state)
final_time = time.time()
elapsed_time = final_time - initial_time
# Compare energies.
timestep = 1.0 * units.femtosecond
print "Computing reference energies..."
reference_integrator = openmm.VerletIntegrator(timestep)
reference_context = openmm.Context(reference_system, reference_integrator, platform)
reference_context.setPositions(coordinates)
reference_state = reference_context.getState(getEnergy=True)
reference_potential = reference_state.getPotentialEnergy()
print "Computing alchemical energies..."
alchemical_integrator = openmm.VerletIntegrator(timestep)
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator, platform)
alchemical_context.setPositions(coordinates)
alchemical_state = alchemical_context.getState(getEnergy=True)
alchemical_potential = alchemical_state.getPotentialEnergy()
delta = alchemical_potential - reference_potential
print "reference system : %24.8f kcal/mol" % (reference_potential / units.kilocalories_per_mole)
print "alchemically modified : %24.8f kcal/mol" % (alchemical_potential / units.kilocalories_per_mole)
print "ERROR : %24.8f kcal/mol" % ((alchemical_potential - reference_potential) / units.kilocalories_per_mole)
print "elapsed alchemical time %.3f s" % elapsed_time
return delta
def benchmark(
reference_system,
coordinates,
receptor_atoms,
ligand_atoms,
platform_name="CUDA",
annihilateElectrostatics=True,
annihilateLennardJones=False,
nsteps=500,
):
"""
Benchmark performance relative to unmodified system.
ARGUMENTS
reference_system (simtk.openmm.System) - the reference System object to compare with
coordinates - the coordinates to assess energetics for
receptor_atoms (list of int) - the list of receptor atoms
ligand_atoms (list of int) - the list of ligand atoms to alchemically modify
"""
import simtk.unit as units
import simtk.openmm as openmm
import time
# Create a factory to produce alchemical intermediates.
print "Creating alchemical factory..."
initial_time = time.time()
factory = AbsoluteAlchemicalFactory(reference_system, ligand_atoms=ligand_atoms)
final_time = time.time()
elapsed_time = final_time - initial_time
print "AbsoluteAlchemicalFactory initialization took %.3f s" % elapsed_time
# Create an alchemically-perturbed state corresponding to nearly fully-interacting.
# NOTE: We use a lambda slightly smaller than 1.0 because the AlchemicalFactory does not use Custom*Force softcore versions if lambda = 1.0 identically.
lambda_value = 1.0 - 1.0e-6
alchemical_state = AlchemicalState(0.00, lambda_value, lambda_value, lambda_value)
alchemical_state.annihilateElectrostatics = annihilateElectrostatics
alchemical_state.annihilateLennardJones = annihilateLennardJones
platform = openmm.Platform.getPlatformByName(platform_name)
# Create the perturbed system.
print "Creating alchemically-modified state..."
initial_time = time.time()
alchemical_system = factory.createPerturbedSystem(alchemical_state)
final_time = time.time()
elapsed_time = final_time - initial_time
# Compare energies.
timestep = 1.0 * units.femtosecond
print "Computing reference energies..."
reference_integrator = openmm.VerletIntegrator(timestep)
reference_context = openmm.Context(reference_system, reference_integrator, platform)
reference_context.setPositions(coordinates)
reference_state = reference_context.getState(getEnergy=True)
reference_potential = reference_state.getPotentialEnergy()
print "Computing alchemical energies..."
alchemical_integrator = openmm.VerletIntegrator(timestep)
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator, platform)
alchemical_context.setPositions(coordinates)
alchemical_state = alchemical_context.getState(getEnergy=True)
alchemical_potential = alchemical_state.getPotentialEnergy()
delta = alchemical_potential - reference_potential
# Make sure all kernels are compiled.
reference_integrator.step(1)
alchemical_integrator.step(1)
# Time simulations.
import time
print "Simulating reference system..."
initial_time = time.time()
reference_integrator.step(nsteps)
reference_state = reference_context.getState(getEnergy=True)
reference_potential = reference_state.getPotentialEnergy()
final_time = time.time()
reference_time = final_time - initial_time
print "Simulating alchemical system..."
initial_time = time.time()
alchemical_integrator.step(nsteps)
alchemical_state = alchemical_context.getState(getEnergy=True)
alchemical_potential = alchemical_state.getPotentialEnergy()
final_time = time.time()
alchemical_time = final_time - initial_time
print "TIMINGS"
print "reference system : %12.3f s for %8d steps (%12.3f ms/step)" % (
reference_time,
nsteps,
reference_time / nsteps * 1000,
)
print "alchemical system : %12.3f s for %8d steps (%12.3f ms/step)" % (
alchemical_time,
nsteps,
alchemical_time / nsteps * 1000,
)
print "alchemical simulation is %12.3f x slower than unperturbed system" % (alchemical_time / reference_time)
return delta
def overlap_check(reference_system, positions, platform_name=None, precision=None, nsteps=50, nsamples=200, factory_args=None, cached_trajectory_filename=None):
"""
Test overlap between reference system and alchemical system by running a short simulation.
Parameters
----------
reference_system : simtk.openmm.System
The reference System object to compare with
positions : simtk.unit.Quantity with units compatible with nanometers
The positions to assess energetics for.
platform_name : str, optional, default=None
The name of the platform to use for benchmarking.
nsteps : int, optional, default=50
Number of molecular dynamics steps between samples.
nsamples : int, optional, default=100
Number of samples to collect.
factory_args : dict(), optional, default=None
Arguments passed to AbsoluteAlchemicalFactory.
cached_trajectory_filename : str, optional, default=None
If specified, attempt to cache (or reuse) trajectory.
"""
# Create a fully-interacting alchemical state.
factory = AbsoluteAlchemicalFactory(reference_system, **factory_args)
alchemical_state = AlchemicalState()
alchemical_system = factory.createPerturbedSystem(alchemical_state)
temperature = 300.0 * unit.kelvin
collision_rate = 5.0 / unit.picoseconds
timestep = 2.0 * unit.femtoseconds
kT = (kB * temperature)
# Select platform.
platform = None
if platform_name:
platform = openmm.Platform.getPlatformByName(platform_name)
# Create integrators.
reference_integrator = openmm.LangevinIntegrator(temperature, collision_rate, timestep)
alchemical_integrator = openmm.VerletIntegrator(timestep)
# Create contexts.
if platform:
reference_context = openmm.Context(reference_system, reference_integrator, platform)
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator, platform)
else:
reference_context = openmm.Context(reference_system, reference_integrator)
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator)
ncfile = None
if cached_trajectory_filename:
cache_mode = 'write'
# Try reading from cache
from netCDF4 import Dataset
if os.path.exists(cached_trajectory_filename):
try:
ncfile = Dataset(cached_trajectory_filename, 'r')
if (ncfile.variables['positions'].shape == (nsamples, reference_system.getNumParticles(), 3)):
# Read the cache if everything matches
cache_mode = 'read'
except:
pass
if cache_mode == 'write':
# If anything went wrong, create a new cache.
try:
(pathname, filename) = os.path.split(cached_trajectory_filename)
if not os.path.exists(pathname): os.makedirs(pathname)
ncfile = Dataset(cached_trajectory_filename, 'w', format='NETCDF4')
ncfile.createDimension('samples', 0)
ncfile.createDimension('atoms', reference_system.getNumParticles())
ncfile.createDimension('spatial', 3)
ncfile.createVariable('positions', 'f4', ('samples', 'atoms', 'spatial'))
except Exception as e:
logger.info(str(e))
logger.info('Could not create a trajectory cache (%s).' % cached_trajectory_filename)
ncfile = None
# Collect simulation data.
reference_context.setPositions(positions)
du_n = np.zeros([nsamples], np.float64) # du_n[n] is the
print()
import click
with click.progressbar(range(nsamples)) as bar:
for sample in bar:
if cached_trajectory_filename and (cache_mode == 'read'):
# Load cached frames.
positions = unit.Quantity(ncfile.variables['positions'][sample,:,:], unit.nanometers)
reference_context.setPositions(positions)
else:
# Run dynamics.
reference_integrator.step(nsteps)
# Get reference energies.
reference_state = reference_context.getState(getEnergy=True, getPositions=True)
reference_potential = reference_state.getPotentialEnergy()
if np.isnan(reference_potential/kT):
raise Exception("Reference potential is NaN")
# Get alchemical energies.
alchemical_context.setPositions(reference_state.getPositions(asNumpy=True))
alchemical_state = alchemical_context.getState(getEnergy=True)
alchemical_potential = alchemical_state.getPotentialEnergy()
if np.isnan(alchemical_potential/kT):
raise Exception("Alchemical potential is NaN")
du_n[sample] = (alchemical_potential - reference_potential) / kT
if cached_trajectory_filename and (cache_mode == 'write') and (ncfile is not None):
ncfile.variables['positions'][sample,:,:] = reference_state.getPositions(asNumpy=True) / unit.nanometers
# Clean up.
del reference_context, alchemical_context
if cached_trajectory_filename and (ncfile is not None):
ncfile.close()
# Discard data to equilibration and subsample.
from pymbar import timeseries
[t0, g, Neff] = timeseries.detectEquilibration(du_n)
indices = timeseries.subsampleCorrelatedData(du_n, g=g)
du_n = du_n[indices]
# Compute statistics.
from pymbar import EXP
[DeltaF, dDeltaF] = EXP(du_n)
# Raise an exception if the error is larger than 3kT.
MAX_DEVIATION = 3.0 # kT
if (dDeltaF > MAX_DEVIATION):
report = "DeltaF = %12.3f +- %12.3f kT (%5d samples, g = %6.1f)" % (DeltaF, dDeltaF, Neff, g)
raise Exception(report)
return
def overlap_check(
reference_system,
positions,
receptor_atoms,
ligand_atoms,
platform_name=None,
annihilate_electrostatics=True,
annihilate_sterics=False,
precision=None,
nsteps=50,
nsamples=200,
):
"""
Test overlap between reference system and alchemical system by running a short simulation.
Parameters
----------
reference_system : simtk.openmm.System
The reference System object to compare with
positions : simtk.unit.Quantity with units compatible with nanometers
The positions to assess energetics for.
receptor_atoms : list of int
The list of receptor atoms.
ligand_atoms : list of int
The list of ligand atoms to alchemically modify.
platform_name : str, optional, default=None
The name of the platform to use for benchmarking.
annihilate_electrostatics : bool, optional, default=True
If True, electrostatics will be annihilated; if False, decoupled.
annihilate_sterics : bool, optional, default=False
If True, sterics will be annihilated; if False, decoupled.
nsteps : int, optional, default=50
Number of molecular dynamics steps between samples.
nsamples : int, optional, default=100
Number of samples to collect.
"""
# Create a fully-interacting alchemical state.
factory = AbsoluteAlchemicalFactory(reference_system, ligand_atoms=ligand_atoms)
alchemical_state = AlchemicalState()
alchemical_system = factory.createPerturbedSystem(alchemical_state)
temperature = 300.0 * unit.kelvin
collision_rate = 5.0 / unit.picoseconds
timestep = 2.0 * unit.femtoseconds
kT = kB * temperature
# Select platform.
platform = None
if platform_name:
platform = openmm.Platform.getPlatformByName(platform_name)
# Create integrators.
reference_integrator = openmm.LangevinIntegrator(temperature, collision_rate, timestep)
alchemical_integrator = openmm.VerletIntegrator(timestep)
# Create contexts.
if platform:
reference_context = openmm.Context(reference_system, reference_integrator, platform)
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator, platform)
else:
reference_context = openmm.Context(reference_system, reference_integrator)
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator)
# Collect simulation data.
reference_context.setPositions(positions)
du_n = np.zeros([nsamples], np.float64) # du_n[n] is the
for sample in range(nsamples):
# Run dynamics.
reference_integrator.step(nsteps)
# Get reference energies.
reference_state = reference_context.getState(getEnergy=True, getPositions=True)
reference_potential = reference_state.getPotentialEnergy()
# Get alchemical energies.
alchemical_context.setPositions(reference_state.getPositions())
alchemical_state = alchemical_context.getState(getEnergy=True)
alchemical_potential = alchemical_state.getPotentialEnergy()
du_n[sample] = (alchemical_potential - reference_potential) / kT
# Clean up.
del reference_context, alchemical_context
# Discard data to equilibration and subsample.
from pymbar import timeseries
[t0, g, Neff] = timeseries.detectEquilibration(du_n)
indices = timeseries.subsampleCorrelatedData(du_n, g=g)
du_n = du_n[indices]
# Compute statistics.
from pymbar import EXP
[DeltaF, dDeltaF] = EXP(du_n)
# Raise an exception if the error is larger than 3kT.
MAX_DEVIATION = 3.0 # kT
if dDeltaF > MAX_DEVIATION:
report = "DeltaF = %12.3f +- %12.3f kT (%5d samples, g = %6.1f)" % (DeltaF, dDeltaF, Neff, g)
raise Exception(report)
return
def benchmark(
reference_system,
positions,
receptor_atoms,
ligand_atoms,
platform_name=None,
annihilate_electrostatics=True,
annihilate_sterics=False,
nsteps=500,
timestep=1.0 * unit.femtoseconds,
):
"""
Benchmark performance of alchemically modified system relative to original system.
Parameters
----------
reference_system : simtk.openmm.System
The reference System object to compare with
positions : simtk.unit.Quantity with units compatible with nanometers
The positions to assess energetics for.
receptor_atoms : list of int
The list of receptor atoms.
ligand_atoms : list of int
The list of ligand atoms to alchemically modify.
platform_name : str, optional, default=None
The name of the platform to use for benchmarking.
annihilate_electrostatics : bool, optional, default=True
If True, electrostatics will be annihilated; if False, decoupled.
annihilate_sterics : bool, optional, default=False
If True, sterics will be annihilated; if False, decoupled.
nsteps : int, optional, default=500
Number of molecular dynamics steps to use for benchmarking.
timestep : simtk.unit.Quantity with units compatible with femtoseconds, optional, default=1*femtoseconds
Timestep to use for benchmarking.
"""
# Create a factory to produce alchemical intermediates.
logger.info("Creating alchemical factory...")
initial_time = time.time()
factory = AbsoluteAlchemicalFactory(
reference_system,
ligand_atoms=ligand_atoms,
annihilate_electrostatics=annihilate_electrostatics,
annihilate_sterics=annihilate_sterics,
)
final_time = time.time()
elapsed_time = final_time - initial_time
logger.info("AbsoluteAlchemicalFactory initialization took %.3f s" % elapsed_time)
# Create an alchemically-perturbed state corresponding to nearly fully-interacting.
# NOTE: We use a lambda slightly smaller than 1.0 because the AlchemicalFactory does not use Custom*Force softcore versions if lambda = 1.0 identically.
lambda_value = 1.0 - 1.0e-6
alchemical_state = AlchemicalState(
lambda_coulomb=lambda_value, lambda_sterics=lambda_value, lambda_torsions=lambda_value
)
platform = None
if platform_name:
platform = openmm.Platform.getPlatformByName(platform_name)
# Create the perturbed system.
logger.info("Creating alchemically-modified state...")
initial_time = time.time()
alchemical_system = factory.createPerturbedSystem(alchemical_state)
final_time = time.time()
elapsed_time = final_time - initial_time
# Compare energies.
logger.info("Computing reference energies...")
reference_integrator = openmm.VerletIntegrator(timestep)
if platform:
reference_context = openmm.Context(reference_system, reference_integrator, platform)
else:
reference_context = openmm.Context(reference_system, reference_integrator)
reference_context.setPositions(positions)
reference_state = reference_context.getState(getEnergy=True)
reference_potential = reference_state.getPotentialEnergy()
logger.info("Computing alchemical energies...")
alchemical_integrator = openmm.VerletIntegrator(timestep)
if platform:
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator, platform)
else:
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator)
alchemical_context.setPositions(positions)
alchemical_state = alchemical_context.getState(getEnergy=True)
alchemical_potential = alchemical_state.getPotentialEnergy()
delta = alchemical_potential - reference_potential
# Make sure all kernels are compiled.
reference_integrator.step(1)
alchemical_integrator.step(1)
# Time simulations.
logger.info("Simulating reference system...")
initial_time = time.time()
reference_integrator.step(nsteps)
reference_state = reference_context.getState(getEnergy=True)
reference_potential = reference_state.getPotentialEnergy()
final_time = time.time()
reference_time = final_time - initial_time
logger.info("Simulating alchemical system...")
initial_time = time.time()
alchemical_integrator.step(nsteps)
alchemical_state = alchemical_context.getState(getEnergy=True)
alchemical_potential = alchemical_state.getPotentialEnergy()
final_time = time.time()
alchemical_time = final_time - initial_time
logger.info("TIMINGS")
logger.info(
"reference system : %12.3f s for %8d steps (%12.3f ms/step)"
% (reference_time, nsteps, reference_time / nsteps * 1000)
)
logger.info(
"alchemical system : %12.3f s for %8d steps (%12.3f ms/step)"
% (alchemical_time, nsteps, alchemical_time / nsteps * 1000)
)
logger.info("alchemical simulation is %12.3f x slower than unperturbed system" % (alchemical_time / reference_time))
return delta