def parameterize_nonbonded(self, ff_q_params, ff_lj_params):
# dummy is either "a or "b"
q_params_a = self.ff.q_handle.partial_parameterize(
ff_q_params, self.mol_a)
q_params_b = self.ff.q_handle.partial_parameterize(
ff_q_params, self.mol_b)
lj_params_a = self.ff.lj_handle.partial_parameterize(
ff_lj_params, self.mol_a)
lj_params_b = self.ff.lj_handle.partial_parameterize(
ff_lj_params, self.mol_b)
q_params = jnp.concatenate([q_params_a, q_params_b])
lj_params = jnp.concatenate([lj_params_a, lj_params_b])
exclusion_idxs_a, scale_factors_a = nonbonded.generate_exclusion_idxs(
self.mol_a,
scale12=_SCALE_12,
scale13=_SCALE_13,
scale14=_SCALE_14)
exclusion_idxs_b, scale_factors_b = nonbonded.generate_exclusion_idxs(
self.mol_b,
scale12=_SCALE_12,
scale13=_SCALE_13,
scale14=_SCALE_14)
mutual_exclusions = []
mutual_scale_factors = []
NA = self.mol_a.GetNumAtoms()
NB = self.mol_b.GetNumAtoms()
for i in range(NA):
for j in range(NB):
mutual_exclusions.append([i, j + NA])
mutual_scale_factors.append([1.0, 1.0])
mutual_exclusions = np.array(mutual_exclusions)
mutual_scale_factors = np.array(mutual_scale_factors)
combined_exclusion_idxs = np.concatenate(
[exclusion_idxs_a, exclusion_idxs_b + NA,
mutual_exclusions]).astype(np.int32)
combined_scale_factors = np.concatenate([
np.stack([scale_factors_a, scale_factors_a], axis=1),
np.stack([scale_factors_b, scale_factors_b], axis=1),
mutual_scale_factors,
]).astype(np.float64)
combined_lambda_plane_idxs = None
combined_lambda_offset_idxs = None
beta = _BETA
cutoff = _CUTOFF # solve for this analytically later
qlj_params = jnp.concatenate(
[jnp.reshape(q_params, (-1, 1)),
jnp.reshape(lj_params, (-1, 2))],
axis=1)
return qlj_params, potentials.Nonbonded(
combined_exclusion_idxs,
combined_scale_factors,
combined_lambda_plane_idxs,
combined_lambda_offset_idxs,
beta,
cutoff,
)
def combine_potentials(ff_handlers, guest_mol, host_system, precision):
"""
This function is responsible for figuring out how to take two separate hamiltonians
and combining them into one sensible alchemical system.
Parameters
----------
ff_handlers: list of forcefield handlers
Small molecule forcefield handlers
guest_mol: Chem.ROMol
RDKit molecule
host_system: openmm.System
Host system to be deserialized
precision: np.float32 or np.float64
Numerical precision of the functional form
Returns
-------
tuple
Returns a list of lib.potentials objects, combined masses, and a list of
their corresponding vjp_fns back into the forcefield
"""
host_potentials, host_masses = openmm_deserializer.deserialize_system(
host_system, precision, cutoff=1.0)
host_nb_bp = None
combined_potentials = []
combined_vjp_fns = []
for bp in host_potentials:
if isinstance(bp, potentials.Nonbonded):
# (ytz): hack to ensure we only have one nonbonded term
assert host_nb_bp is None
host_nb_bp = bp
else:
combined_potentials.append(bp)
combined_vjp_fns.append([])
guest_masses = np.array([a.GetMass() for a in guest_mol.GetAtoms()],
dtype=np.float64)
num_guest_atoms = len(guest_masses)
num_host_atoms = len(host_masses)
combined_masses = np.concatenate([host_masses, guest_masses])
for handle in ff_handlers:
results = handle.parameterize(guest_mol)
if isinstance(handle, bonded.HarmonicBondHandler):
bond_idxs, (bond_params, vjp_fn) = results
bond_idxs += num_host_atoms
combined_potentials.append(
potentials.HarmonicBond(bond_idxs,
precision=precision).bind(bond_params))
combined_vjp_fns.append([(handle, vjp_fn)])
elif isinstance(handle, bonded.HarmonicAngleHandler):
angle_idxs, (angle_params, vjp_fn) = results
angle_idxs += num_host_atoms
combined_potentials.append(
potentials.HarmonicAngle(
angle_idxs, precision=precision).bind(angle_params))
combined_vjp_fns.append([(handle, vjp_fn)])
elif isinstance(handle, bonded.ProperTorsionHandler):
torsion_idxs, (torsion_params, vjp_fn) = results
torsion_idxs += num_host_atoms
combined_potentials.append(
potentials.PeriodicTorsion(
torsion_idxs, precision=precision).bind(torsion_params))
combined_vjp_fns.append([(handle, vjp_fn)])
elif isinstance(handle, bonded.ImproperTorsionHandler):
torsion_idxs, (torsion_params, vjp_fn) = results
torsion_idxs += num_host_atoms
combined_potentials.append(
potentials.PeriodicTorsion(
torsion_idxs, precision=precision).bind(torsion_params))
combined_vjp_fns.append([(handle, vjp_fn)])
elif isinstance(handle, nonbonded.AM1CCCHandler):
charge_handle = handle
guest_charge_params, guest_charge_vjp_fn = results
elif isinstance(handle, nonbonded.LennardJonesHandler):
guest_lj_params, guest_lj_vjp_fn = results
lj_handle = handle
else:
print("Warning: skipping handler", handle)
pass
# process nonbonded terms
combined_nb_params, (charge_vjp_fn, lj_vjp_fn) = nonbonded_vjps(
guest_charge_params, guest_charge_vjp_fn, guest_lj_params,
guest_lj_vjp_fn, host_nb_bp.params)
# these vjp_fns take in adjoints of combined_params and returns derivatives
# appropriate to the underlying handler
combined_vjp_fns.append([(charge_handle, charge_vjp_fn),
(lj_handle, lj_vjp_fn)])
# tbd change scale 14 for electrostatics
guest_exclusion_idxs, guest_scale_factors = nonbonded.generate_exclusion_idxs(
guest_mol, scale12=1.0, scale13=1.0, scale14=0.5)
# allow the ligand to be alchemically decoupled
# a value of one indicates that we allow the atom to be adjusted by the lambda value
guest_lambda_offset_idxs = np.ones(len(guest_masses), dtype=np.int32)
# use same scale factors until we modify 1-4s for electrostatics
guest_scale_factors = np.stack([guest_scale_factors, guest_scale_factors],
axis=1)
combined_lambda_offset_idxs = np.concatenate(
[host_nb_bp.get_lambda_offset_idxs(), guest_lambda_offset_idxs])
combined_exclusion_idxs = np.concatenate([
host_nb_bp.get_exclusion_idxs(), guest_exclusion_idxs + num_host_atoms
])
combined_scales = np.concatenate(
[host_nb_bp.get_scale_factors(), guest_scale_factors])
combined_beta = 2.0
combined_cutoff = 1.0 # nonbonded cutoff
combined_potentials.append(
potentials.Nonbonded(
combined_exclusion_idxs,
combined_scales,
combined_lambda_offset_idxs,
combined_beta,
combined_cutoff,
precision=precision,
).bind(combined_nb_params))
return combined_potentials, combined_masses, combined_vjp_fns
def parameterize_nonbonded(self, ff_q_params, ff_lj_params):
num_guest_atoms = self.guest_topology.get_num_atoms()
guest_qlj, guest_p = self.guest_topology.parameterize_nonbonded(
ff_q_params, ff_lj_params)
if isinstance(guest_p, potentials.NonbondedInterpolated):
assert guest_qlj.shape[0] == num_guest_atoms * 2
is_interpolated = True
else:
assert guest_qlj.shape[0] == num_guest_atoms
is_interpolated = False
# see if we're doing parameter interpolation
assert guest_qlj.shape[1] == 3
assert guest_p.get_beta() == self.host_nonbonded.get_beta()
assert guest_p.get_cutoff() == self.host_nonbonded.get_cutoff()
hg_exclusion_idxs = np.concatenate([
self.host_nonbonded.get_exclusion_idxs(),
guest_p.get_exclusion_idxs() + self.num_host_atoms
])
hg_scale_factors = np.concatenate([
self.host_nonbonded.get_scale_factors(),
guest_p.get_scale_factors()
])
hg_lambda_offset_idxs = np.concatenate([
self.host_nonbonded.get_lambda_offset_idxs(),
guest_p.get_lambda_offset_idxs()
])
hg_lambda_plane_idxs = np.concatenate([
self.host_nonbonded.get_lambda_plane_idxs(),
guest_p.get_lambda_plane_idxs()
])
if is_interpolated:
# with parameter interpolation
hg_nb_params_src = jnp.concatenate(
[self.host_nonbonded.params, guest_qlj[:num_guest_atoms]])
hg_nb_params_dst = jnp.concatenate(
[self.host_nonbonded.params, guest_qlj[num_guest_atoms:]])
hg_nb_params = jnp.concatenate(
[hg_nb_params_src, hg_nb_params_dst])
nb = potentials.NonbondedInterpolated(
hg_exclusion_idxs,
hg_scale_factors,
hg_lambda_plane_idxs,
hg_lambda_offset_idxs,
guest_p.get_beta(),
guest_p.get_cutoff(),
)
return hg_nb_params, nb
else:
# no parameter interpolation
hg_nb_params = jnp.concatenate(
[self.host_nonbonded.params, guest_qlj])
return hg_nb_params, potentials.Nonbonded(
hg_exclusion_idxs,
hg_scale_factors,
hg_lambda_plane_idxs,
hg_lambda_offset_idxs,
guest_p.get_beta(),
guest_p.get_cutoff(),
)
def prepare_water_system(x, lambda_plane_idxs, lambda_offset_idxs, p_scale,
cutoff):
assert x.ndim == 2
N = x.shape[0]
# D = x.shape[1]
assert N % 3 == 0
params = np.stack(
[
(np.random.rand(N).astype(np.float64) - 0.5) *
np.sqrt(ONE_4PI_EPS0), # q
np.random.rand(N).astype(np.float64) / 5.0, # sig
np.random.rand(N).astype(np.float64), # eps
],
axis=1,
)
params[:, 1] = params[:, 1] / 2
params[:, 2] = np.sqrt(params[:, 2])
scales = []
exclusion_idxs = []
for i in range(N // 3):
O_idx = i * 3 + 0
H1_idx = i * 3 + 1
H2_idx = i * 3 + 2
exclusion_idxs.append([O_idx, H1_idx]) # 1-2
exclusion_idxs.append([O_idx, H2_idx]) # 1-2
exclusion_idxs.append([H1_idx, H2_idx]) # 1-3
scales.append([1.0, 1.0])
scales.append([1.0, 1.0])
scales.append([np.random.rand(), np.random.rand()])
scales = np.array(scales, dtype=np.float64)
exclusion_idxs = np.array(exclusion_idxs, dtype=np.int32)
beta = 2.0
test_potential = potentials.Nonbonded(exclusion_idxs, scales,
lambda_plane_idxs,
lambda_offset_idxs, beta, cutoff)
charge_rescale_mask = np.ones((N, N))
for (i, j), exc in zip(exclusion_idxs, scales[:, 0]):
charge_rescale_mask[i][j] = 1 - exc
charge_rescale_mask[j][i] = 1 - exc
lj_rescale_mask = np.ones((N, N))
for (i, j), exc in zip(exclusion_idxs, scales[:, 1]):
lj_rescale_mask[i][j] = 1 - exc
lj_rescale_mask[j][i] = 1 - exc
ref_total_energy = functools.partial(
nonbonded.nonbonded_v3,
charge_rescale_mask=charge_rescale_mask,
lj_rescale_mask=lj_rescale_mask,
beta=beta,
cutoff=cutoff,
lambda_plane_idxs=lambda_plane_idxs,
lambda_offset_idxs=lambda_offset_idxs,
runtime_validate=False,
)
return params, ref_total_energy, test_potential
def from_rdkit(cls, mol, ff_handlers):
"""
Initialize a system from an RDKit ROMol.
Parameters
----------
mol: Chem.ROMol
RDKit ROMol. Should have graphical hydrogens in the topology.
ff_handlers: list of forcefield handlers.
openforcefield small molecule handlers.
"""
masses = np.array([a.GetMass() for a in mol.GetAtoms()], dtype=np.float64)
bound_potentials = []
for handle in ff_handlers:
results = handle.parameterize(mol)
if isinstance(handle, bonded.HarmonicBondHandler):
bond_params, bond_idxs = results
bound_potentials.append(potentials.HarmonicBond(bond_idxs).bind(bond_params))
elif isinstance(handle, bonded.HarmonicAngleHandler):
angle_params, angle_idxs = results
bound_potentials.append(potentials.HarmonicAngle(angle_idxs).bind(angle_params))
elif isinstance(handle, bonded.ProperTorsionHandler):
torsion_params, torsion_idxs = results
bound_potentials.append(potentials.PeriodicTorsion(torsion_idxs).bind(torsion_params))
elif isinstance(handle, bonded.ImproperTorsionHandler):
torsion_params, torsion_idxs = results
bound_potentials.append(potentials.PeriodicTorsion(torsion_idxs).bind(torsion_params))
elif isinstance(handle, nonbonded.AM1CCCHandler):
charge_handle = handle
charge_params = results
elif isinstance(handle, nonbonded.LennardJonesHandler):
lj_params = results
lj_handle = handle
else:
print("WARNING: skipping handler", handle)
pass
lambda_plane_idxs = np.zeros(len(masses), dtype=np.int32)
lambda_offset_idxs = np.zeros(len(masses), dtype=np.int32)
exclusion_idxs, scale_factors = nonbonded.generate_exclusion_idxs(
mol,
scale12=1.0,
scale13=1.0,
scale14=0.5
)
# use same scale factors until we modify 1-4s for electrostatics
scale_factors = np.stack([scale_factors, scale_factors], axis=1)
# (ytz) fix this later to not be so hard coded
alpha = 2.0 # same as ewald alpha
cutoff = 1.0 # nonbonded cutoff
qlj_params = jnp.concatenate([
jnp.reshape(charge_params, (-1, 1)),
jnp.reshape(lj_params, (-1, 2))
], axis=1)
bound_potentials.append(potentials.Nonbonded(
exclusion_idxs,
scale_factors,
lambda_plane_idxs,
lambda_offset_idxs,
alpha,
cutoff).bind(qlj_params))
return cls(masses, bound_potentials)
def combine(self, other):
"""
Combine two recipes together. self will keep its original indexing,
while other will be incremented. This method automatically increments
indices in the potential functions accordingly. For nonbonded terms, the recipe
does a straight forward concatenation of the lambda idxs.
Parameters
----------
other: Recipe
the right hand side recipe to combine with
Returns
-------
Recipe
combined recipe
"""
self_num_atoms = len(self.masses)
combined_masses = np.concatenate([self.masses, other.masses])
combined_bound_potentials = []
for bp in self.bound_potentials:
if isinstance(bp, potentials.Nonbonded):
# save these parameters for the merge part.
self_nb_params = bp.params
self_nb_exclusions = bp.get_exclusion_idxs()
self_nb_scale_factors = bp.get_scale_factors()
self_nb_cutoff = bp.get_cutoff()
self_nb_beta = bp.get_beta()
self_nb_lambda_plane_idxs = bp.get_lambda_plane_idxs()
self_nb_lambda_offset_idxs = bp.get_lambda_offset_idxs()
else:
combined_bound_potentials.append(bp)
for full_obp in other.bound_potentials:
# always deepcopy to prevent modifying original copy
full_obp = copy.deepcopy(full_obp)
# if this is a lambda potential we replace only the .u_fn part
if isinstance(full_obp, potentials.LambdaPotential):
full_obp.set_N(full_obp.get_N() + self_num_atoms)
obp = full_obp.get_u_fn()
else:
obp = full_obp
if isinstance(obp, potentials.HarmonicBond) or isinstance(obp, potentials.CoreRestraint):
idxs = obp.get_idxs()
idxs += self_num_atoms # modify inplace
elif isinstance(obp, potentials.HarmonicAngle):
idxs = obp.get_idxs()
idxs += self_num_atoms # modify inplace
elif isinstance(obp, potentials.PeriodicTorsion):
idxs = obp.get_idxs()
idxs += self_num_atoms # modify inplace
elif isinstance(obp, potentials.CentroidRestraint):
a_idxs = obp.get_a_idxs()
a_idxs += self_num_atoms # modify inplace
b_idxs = obp.get_b_idxs()
b_idxs += self_num_atoms # modify inplace
# adjust masses
obp.set_masses(np.concatenate([np.zeros(self_num_atoms), obp.get_masses()]))
elif isinstance(obp, potentials.Nonbonded):
assert self_nb_cutoff == obp.get_cutoff()
assert self_nb_beta == obp.get_beta()
combined_nb_params = jnp.concatenate([self_nb_params, obp.params])
combined_exclusion_idxs = np.concatenate([self_nb_exclusions, obp.get_exclusion_idxs() + self_num_atoms])
combined_scale_factors = np.concatenate([self_nb_scale_factors, obp.get_scale_factors()])
combined_lambda_offset_idxs = np.concatenate([self_nb_lambda_offset_idxs, obp.get_lambda_offset_idxs()])
combined_lambda_plane_idxs = np.concatenate([self_nb_lambda_plane_idxs, obp.get_lambda_plane_idxs()])
# (ytz): leave this in for now
# sanity check to ensure that the chain rules are working
dummy = np.ones_like(combined_nb_params)
obp = potentials.Nonbonded(
combined_exclusion_idxs,
combined_scale_factors,
combined_lambda_plane_idxs,
combined_lambda_offset_idxs,
self_nb_beta,
self_nb_cutoff).bind(combined_nb_params)
else:
raise Exception("Unknown functional form")
if isinstance(full_obp, potentials.LambdaPotential):
combined_bound_potentials.append(full_obp)
else:
combined_bound_potentials.append(obp)
return Recipe(combined_masses, combined_bound_potentials)
def deserialize_system(system, cutoff):
"""
Deserialize an OpenMM XML file
Parameters
----------
system: openmm.System
A system object to be deserialized
Returns
-------
list of lib.Potential, masses
Note: We add a small epsilon (1e-3) to all zero eps values to prevent
a singularity from occuring in the lennard jones derivatives
"""
masses = []
for p in range(system.getNumParticles()):
masses.append(value(system.getParticleMass(p)))
N = len(masses)
# this should not be a dict since we may have more than one instance of a given
# force.
bps = []
for force in system.getForces():
if isinstance(force, mm.HarmonicBondForce):
bond_idxs = []
bond_params = []
for b_idx in range(force.getNumBonds()):
src_idx, dst_idx, length, k = force.getBondParameters(b_idx)
length = value(length)
k = value(k)
bond_idxs.append([src_idx, dst_idx])
bond_params.append((k, length))
bond_idxs = np.array(bond_idxs, dtype=np.int32)
bond_params = np.array(bond_params, dtype=np.float64)
bps.append(potentials.HarmonicBond(bond_idxs).bind(bond_params))
if isinstance(force, mm.HarmonicAngleForce):
angle_idxs = []
angle_params = []
for a_idx in range(force.getNumAngles()):
src_idx, mid_idx, dst_idx, angle, k = force.getAngleParameters(
a_idx)
angle = value(angle)
k = value(k)
angle_idxs.append([src_idx, mid_idx, dst_idx])
angle_params.append((k, angle))
angle_idxs = np.array(angle_idxs, dtype=np.int32)
angle_params = np.array(angle_params, dtype=np.float64)
bps.append(potentials.HarmonicAngle(angle_idxs).bind(angle_params))
if isinstance(force, mm.PeriodicTorsionForce):
torsion_idxs = []
torsion_params = []
for t_idx in range(force.getNumTorsions()):
a_idx, b_idx, c_idx, d_idx, period, phase, k = force.getTorsionParameters(
t_idx)
phase = value(phase)
k = value(k)
torsion_params.append((k, phase, period))
torsion_idxs.append([a_idx, b_idx, c_idx, d_idx])
torsion_idxs = np.array(torsion_idxs, dtype=np.int32)
torsion_params = np.array(torsion_params, dtype=np.float64)
bps.append(
potentials.PeriodicTorsion(torsion_idxs).bind(torsion_params))
if isinstance(force, mm.NonbondedForce):
num_atoms = force.getNumParticles()
charge_params = []
lj_params = []
for a_idx in range(num_atoms):
charge, sig, eps = force.getParticleParameters(a_idx)
charge = value(charge) * np.sqrt(constants.ONE_4PI_EPS0)
sig = value(sig)
eps = value(eps)
# increment eps by 1e-3 if we have eps==0 to avoid a singularity in parameter derivatives
# override default amber types
# this doesn't work for water!
# if eps == 0:
# print("Warning: overriding eps by 1e-3 to avoid a singularity")
# eps += 1e-3
# charge_params.append(charge_idx)
charge_params.append(charge)
lj_params.append((sig, eps))
charge_params = np.array(charge_params, dtype=np.float64)
# print("Protein net charge:", np.sum(np.array(global_params)[charge_param_idxs]))
lj_params = np.array(lj_params, dtype=np.float64)
# 1 here means we fully remove the interaction
# 1-2, 1-3
# scale_full = insert_parameters(1.0, 20)
# 1-4, remove half of the interaction
# scale_half = insert_parameters(0.5, 21)
exclusion_idxs = []
scale_factors = []
all_sig = lj_params[:, 0]
all_eps = lj_params[:, 1]
# validate exclusions/exceptions to make sure they make sense
for a_idx in range(force.getNumExceptions()):
# tbd charge scale factors
src, dst, new_cp, new_sig, new_eps = force.getExceptionParameters(
a_idx)
new_sig = value(new_sig)
new_eps = value(new_eps)
src_sig = all_sig[src]
dst_sig = all_sig[dst]
src_eps = all_eps[src]
dst_eps = all_eps[dst]
expected_sig = (src_sig + dst_sig) / 2
expected_eps = np.sqrt(src_eps * dst_eps)
exclusion_idxs.append([src, dst])
# sanity check this (expected_eps can be zero), redo this thing
# the lj_scale factor measures how much we *remove*
if expected_eps == 0:
if new_eps == 0:
lj_scale_factor = 1
else:
raise RuntimeError(
"Divide by zero in epsilon calculation")
else:
lj_scale_factor = 1 - new_eps / expected_eps
scale_factors.append(lj_scale_factor)
# tbd fix charge_scale_factors using new_cp
if new_eps != 0:
np.testing.assert_almost_equal(expected_sig, new_sig)
exclusion_idxs = np.array(exclusion_idxs, dtype=np.int32)
lambda_plane_idxs = np.zeros(N, dtype=np.int32)
lambda_offset_idxs = np.zeros(N, dtype=np.int32)
# cutoff = 1000.0
nb_params = np.concatenate(
[np.expand_dims(charge_params, axis=1), lj_params], axis=1)
# optimizations
nb_params[:, 1] = nb_params[:, 1] / 2
nb_params[:, 2] = np.sqrt(nb_params[:, 2])
beta = 2.0 # erfc correction
# use the same scale factors for electrostatics and lj
scale_factors = np.stack([scale_factors, scale_factors], axis=1)
bps.append(
potentials.Nonbonded(exclusion_idxs, scale_factors,
lambda_plane_idxs, lambda_offset_idxs,
beta, cutoff).bind(nb_params))
# nrg_fns.append(('Exclusions', (exclusion_idxs, scale_factors, es_scale_factors)))
return bps, masses