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make_plots.py
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make_plots.py
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import os
import sys
import traceback
import time
import numpy as np
import argparse
import unittest
import ctypes
import random
import tensorflow as tf
from openeye import oechem
from openeye.oechem import OEMol, OEParseSmiles, OEAddExplicitHydrogens, OEGetIsotopicWeight, OEGetAverageWeight
from openeye import oeomega
from openeye.oechem import OEFloatArray
from openforcefield.utils import get_data_filename, generateTopologyFromOEMol
from openforcefield.typing.engines.smirnoff import get_molecule_parameterIDs, ForceField
from timemachine import observable
from timemachine import minimizer
from timemachine.constants import BOLTZ
from timemachine import system_builder
from timemachine.cpu_functionals import custom_ops
from simtk import openmm
from tensorflow.python.client import device_lib
num_train_samples = 24
num_test_samples = int(0.333*num_train_samples)
ksize = 200 # reservoir size FIXME
batch_size = 4 # number of GPUs
obs_steps = 4000
train_steps = 2000
def get_available_gpus():
local_device_protos = device_lib.list_local_devices()
return [x.name for x in local_device_protos if x.device_type == 'GPU']
def get_abc_coefficents(
masses,
dt,
friction,
temperature):
"""
Get a,b,c coefficients of the integrator.
"""
vscale = np.exp(-dt*friction)
if friction == 0:
fscale = dt
else:
fscale = (1-vscale)/friction
kT = BOLTZ * temperature
nscale = np.sqrt(kT*(1-vscale*vscale)) # noise scale
invMasses = (1.0/masses).reshape((-1, 1))
sqrtInvMasses = np.sqrt(invMasses)
coeff_a = vscale
coeff_bs = fscale*invMasses
coeff_cs = nscale*sqrtInvMasses
return coeff_a, coeff_bs, coeff_cs
def estimate_buffer_size(epsilon, coeff_a):
"""
Estimate optimal size of the buffer to achieve convergence
"""
return np.int64(np.log(epsilon)/np.log(coeff_a)+1)
def get_masses(mol):
masses = []
for atom in mol.GetAtoms():
elem = atom.GetAtomicNum()
mass = atom.GetIsotope()
masses.append(OEGetIsotopicWeight(elem, mass))
return np.array(masses)
def mol_coords_to_numpy_array(mol):
coords = OEFloatArray(mol.GetMaxAtomIdx() * 3)
mol.GetCoords(coords)
arr = np.ctypeslib.as_array(ctypes.cast(int(coords.PtrCast()), ctypes.POINTER(ctypes.c_float)), shape=(len(coords),))
return np.array(arr.reshape((-1, 3)))
def write_xyz(ofs, mol, coords):
mol.SetCoords(OEFloatArray(coords.reshape(-1)))
oechem.OEWriteMolecule(ofs, mol)
def initialize_system(
nrglist,
total_params,
masses,
mol,
dt,
temperature):
num_atoms = mol.NumAtoms()
nrg_funcs = [
custom_ops.HarmonicBondGPU_double,
custom_ops.HarmonicAngleGPU_double,
custom_ops.PeriodicTorsionGPU_double,
custom_ops.LennardJonesGPU_double,
custom_ops.ElectrostaticsGPU_double
]
nrgs = []
for func, group in zip(nrg_funcs, nrglist):
nrgs.append(func(*group))
# dt = 0.0025
# friction = 10.0
# temperature = 300
# gradient descent
dt = dt
friction = 10.0
temperature = temperature
a,b,c = get_abc_coefficents(masses, dt, friction, temperature)
buf_size = estimate_buffer_size(1e-10, a)
# print("BUFFER_SIZE", buf_size)
x0 = mol_coords_to_numpy_array(mol)/10
intg = custom_ops.Integrator_double(
dt,
buf_size,
num_atoms,
total_params,
a,
b,
c
)
context = custom_ops.Context_double(
nrgs,
intg
)
# print("x0", x0.shape)
x0 = minimizer.minimize_newton_cg(nrgs, x0, total_params)
return nrgs, intg, context, x0
def run_once(nrgs, context, intg, x0, n_steps, total_params, ksize, inference):
# print("x0", x0.shape)
x0 = minimizer.minimize_newton_cg(nrgs, x0, total_params)
origin = np.sum(x0, axis=0)/x0.shape[0]
num_atoms = x0.shape[0]
num_steps = n_steps
intg.reset()
intg.set_coordinates(x0.reshape(-1).tolist())
intg.set_velocities(np.zeros_like(x0).reshape(-1).tolist())
start_time = time.time()
k = ksize
reservoir = []
for step in range(n_steps):
# coords = intg.get_coordinates()
# dxdps = intg.get_dxdp()
# reservoir.append((np.array(coords).reshape((num_atoms, 3)), np.array(dxdps).reshape((total_params, num_atoms, 3))))
if step < k:
coords = intg.get_coordinates()
dxdps = intg.get_dxdp()
reservoir.append((np.array(coords).reshape((num_atoms, 3)), np.array(dxdps).reshape((total_params, num_atoms, 3))))
else:
j = random.randint(0, step)
if j < k:
coords = intg.get_coordinates()
dxdps = intg.get_dxdp()
reservoir[j] = (np.array(coords).reshape((num_atoms, 3)), np.array(dxdps).reshape((total_params, num_atoms, 3)))
context.step(inference)
confs = []
for r in reservoir:
confs.append(r[0])
confs = np.array(confs)
dxdps = []
for r in reservoir:
dxdps.append(r[1])
dxdps = np.array(dxdps)
return confs, dxdps
def batch(iterable, n=1):
l = len(iterable)
for ndx in range(0, l, n):
yield iterable[ndx:min(ndx + n, l)]
def make_plot(test_smiles):
for smi_idx, smiles in enumerate(test_smiles):
mol = OEMol()
OEParseSmiles(mol, smiles)
OEAddExplicitHydrogens(mol)
masses = get_masses(mol)
num_atoms = mol.NumAtoms()
# if num_atoms < 30:
# print("skipping due to being too small")
# continue
print("processing", smiles, smi_idx, "/", len(test_smiles))
omegaOpts = oeomega.OEOmegaOptions()
omegaOpts.SetMaxConfs(1)
omega = oeomega.OEOmega(omegaOpts)
omega.SetStrictStereo(False)
if not omega(mol):
assert 0
topology = generateTopologyFromOEMol(mol)
reference_forcefield_file = 'forcefield/smirnoff99Frosst_perturbed.offxml'
ff = ForceField(get_data_filename(reference_forcefield_file))
params = system_builder.construct_energies(ff, mol, am1_charges=True)
total_params = params[1]
nrgs, intg, context, x0 = initialize_system(
nrglist=params[0],
total_params=total_params,
masses=masses,
mol=mol,
dt=0.001,
temperature=50) # YTZ FIXME depending on if we're doing validation or test.
ref_vals = []
sample_size = 10
for _ in range(sample_size):
confs, _ = run_once(nrgs, context, intg, x0, obs_steps, total_params, ksize, inference=True)
tf.reset_default_graph()
sess = tf.Session()
x_test = tf.convert_to_tensor(confs)
obs0_rij = observable.radius_of_gyration(x_test, confs.shape[1])
ref_vals.append(np.mean(sess.run(obs0_rij)))
params = system_builder.construct_energies(ff, mol, am1_charges=False)
total_params = params[1]
nrgs, intg, context, x0 = initialize_system(
nrglist=params[0],
total_params=total_params,
masses=masses,
mol=mol,
dt=0.001,
temperature=50) # YTZ FIXME depending on if we're doing validation or test.
test_vals = []
for _ in range(sample_size):
confs, _ = run_once(nrgs, context, intg, x0, obs_steps, total_params, ksize, inference=True)
tf.reset_default_graph()
sess = tf.Session()
x_test = tf.convert_to_tensor(confs)
obs0_rij = observable.radius_of_gyration(x_test, confs.shape[1])
test_vals.append(np.mean(sess.run(obs0_rij)))
print("reference mean, std, se", np.mean(ref_vals), np.std(ref_vals), np.std(ref_vals)/np.sqrt(sample_size))
print("test mean, std", np.mean(test_vals), np.std(test_vals), np.std(test_vals)/np.sqrt(sample_size))
if __name__ == "__main__":
test_smiles = [
"COCCCCOC",
"CCCC",
"CCCCCCCCCCCCCC",
]
test_smiles = [
"C(C(C(O)O)O)O",
"C(C(CO)O)C(O)O",
"C(C(CO)O)O",
"C(C(O)O)(O)O",
"C(C(O)O)C(O)O",
"C(C(O)O)C(O)OCO",
"C(C(O)O)O",
"C(C(O)O)OCO",
"C(C(O)OC(O)O)O",
"C(C(O)OCO)O",
"C(CC(O)O)CO",
"C(CCO)CC(O)O",
"C(CCO)CCO",
"C(CCO)CO",
"C(CO)C(C(O)O)O",
"C(CO)C(CC(O)O)O",
"C(CO)C(CCO)O",
"C(CO)C(CO)O",
"C(CO)C(O)O",
"C(CO)C(O)OC(O)O",
"C(CO)C(O)OCO",
"C(CO)CO",
"C(CO)COCO",
"C(CO)O",
"C(COC(O)O)O",
"C(COCC(O)O)O",
"C(COCCO)O",
"C(COCO)C(O)O",
"C(COCO)O",
"C(O)(O)O",
"C(O)(O)OC(O)O",
"C(O)O",
"C(O)OC(C(O)O)O",
"C(O)OC(O)O",
"C(O)OC(O)OC(O)O",
"C(O)OC(O)OCO",
"C(O)OCO",
"CC",
"CC(C(C(C)(C)C)O)O",
"CC(C(C(C)(C)O)O)O",
"CC(C(C(C)(O)O)O)O",
"CC(C(C(C)O)O)O",
"CC(C(C(O)O)O)O",
"CC(C(C)(C(C)(C)C)O)O",
"CC(C(C)(C(C)(C)O)O)O",
"CC(C(C)(C(C)(O)O)O)O",
"CC(C(C)(C(C)O)O)O",
"CC(C(C)(C(O)O)O)O",
"CC(C(C)(C)C(C)(C)C)O",
"CC(C(C)(C)C(C)(C)O)O",
"CC(C(C)(C)C(C)(O)O)O",
"CC(C(C)(C)C(C)O)O",
"CC(C(C)(C)C(O)O)O",
"CC(C(C)(C)C)O",
"CC(C(C)(C)C)OC(C)O",
"CC(C(C)(C)C)OCO",
"CC(C(C)(C)CC(C)(C)C)O",
"CC(C(C)(C)CC(C)(C)O)O",
"CC(C(C)(C)CC(C)(O)O)O",
"CC(C(C)(C)CC(O)O)O",
"CC(C(C)(C)CCO)O",
"CC(C(C)(C)CO)O",
"CC(C(C)(C)COC)O",
"CC(C(C)(C)O)O",
"CC(C(C)(C)O)OC(C)O",
"CC(C(C)(C)O)OCO",
"CC(C(C)(C)OC(C)(C)C)O",
"CC(C(C)(C)OC(C)(C)O)O",
"CC(C(C)(C)OC(C)(O)O)O",
"CC(C(C)(C)OC(C)O)O",
"CC(C(C)(C)OC(O)O)O",
"CC(C(C)(C)OC)O",
"CC(C(C)(C)OCO)O",
"CC(C(C)(CC(C)(C)C)O)O",
"CC(C(C)(CC(C)(C)O)O)O",
"CC(C(C)(CC(C)(O)O)O)O",
"CC(C(C)(CC(O)O)O)O",
"CC(C(C)(CCO)O)O",
"CC(C(C)(CO)O)O",
"CC(C(C)(COC)O)O",
"CC(C(C)(O)O)(O)O",
"CC(C(C)(O)O)O",
"CC(C(C)(O)O)OC(C)O",
"CC(C(C)(O)O)OCO",
"CC(C(C)(O)OC(C)(C)C)O",
"CC(C(C)(O)OC(C)(C)O)O",
"CC(C(C)(O)OC(C)(O)O)O",
"CC(C(C)(O)OC(C)O)O",
"CC(C(C)(O)OC(O)O)O",
"CC(C(C)(O)OC)O",
"CC(C(C)(O)OCO)(O)O",
"CC(C(C)(O)OCO)O",
"CC(C(C)O)C(C)(C)C",
"CC(C(C)O)C(C)(C)O",
"CC(C(C)O)C(C)(O)O",
"CC(C(C)O)C(C)O",
"CC(C(C)O)C(O)O",
"CC(C(C)O)O",
"CC(C(C)OC(C)(C)C)O",
"CC(C(C)OC(C)(C)O)O",
"CC(C(C)OC(C)(O)O)O",
"CC(C(C)OC(C)O)O",
"CC(C(C)OC(O)O)O",
"CC(C(C)OC)O",
"CC(C(C)OCO)O",
"CC(C(CC(C)(C)C)O)O",
"CC(C(CC(C)(C)O)O)O",
"CC(C(CC(C)(O)O)O)O",
"CC(C(CC(O)O)O)O",
"CC(C(CCO)O)(O)O",
"CC(C(CCO)O)O",
"CC(C(CO)O)(O)O",
"CC(C(CO)O)O",
"CC(C(COC)O)O",
"CC(C(O)O)(O)O",
"CC(C(O)O)(O)OCO",
"CC(C(O)O)O",
"CC(C(O)O)OC(C)O",
"CC(C(O)O)OCO",
"CC(C(O)OC(C)(C)C)O",
"CC(C(O)OC(C)(C)O)O",
"CC(C(O)OC(C)(O)O)O",
"CC(C(O)OC(C)O)O",
"CC(C(O)OC(O)O)O",
"CC(C(O)OC)O",
"CC(C(O)OCO)(O)O",
"CC(C(O)OCO)O",
"CC(C)(C(C)(C)O)O",
"CC(C)(C(C)(C)OCO)O",
"CC(C)(C(C)(CCO)O)O",
"CC(C)(C(C)(CO)O)O",
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