def generate_waterbox_nb_args() -> NonbondedArgs: system, positions, box, _ = builders.build_water_system(3.0) bps, masses = openmm_deserializer.deserialize_system(system, cutoff=1.2) nb = bps[-1] params = nb.params conf = positions.value_in_unit(unit.nanometer) N = conf.shape[0] beta = nb.get_beta() cutoff = nb.get_cutoff() lamb = 0.0 charge_rescale_mask = onp.ones((N, N)) lj_rescale_mask = onp.ones((N, N)) lambda_plane_idxs = np.zeros(N, dtype=int) lambda_offset_idxs = np.zeros(N, dtype=int) args = ( conf, params, box, lamb, charge_rescale_mask, lj_rescale_mask, beta, cutoff, lambda_plane_idxs, lambda_offset_idxs, ) return args
def test_jax_nonbonded_block(): """Assert that nonbonded_block and nonbonded_on_specific_pairs agree""" system, positions, box, _ = builders.build_water_system(3.0) bps, masses = openmm_deserializer.deserialize_system(system, cutoff=1.2) nb = bps[-1] params = nb.params conf = positions.value_in_unit(unit.nanometer) N = conf.shape[0] beta = nb.get_beta() cutoff = nb.get_cutoff() split = 70 def u_a(x, box, params): xi = x[:split] xj = x[split:] pi = params[:split] pj = params[split:] return nonbonded_block(xi, xj, box, pi, pj, beta, cutoff) i_s, j_s = np.indices((split, N - split)) indices_left = i_s.flatten() indices_right = j_s.flatten() + split def u_b(x, box, params): vdw, es = nonbonded_v3_on_specific_pairs(x, params, box, indices_left, indices_right, beta, cutoff) return np.sum(vdw + es) onp.testing.assert_almost_equal(u_a(conf, box, params), u_b(conf, box, params))
def test_nonbonded_with_box_smaller_than_cutoff(self): np.random.seed(4321) precision = np.float32 cutoff = 1 size = 33 padding = 0.1 _, coords, box, _ = builders.build_water_system(6.2) coords = coords / coords.unit coords = coords[:size] N = coords.shape[0] lambda_plane_idxs = np.random.randint(low=-2, high=2, size=N, dtype=np.int32) lambda_offset_idxs = np.random.randint(low=-2, high=2, size=N, dtype=np.int32) # Down shift box size to be only a portion of the cutoff charge_params, ref_potential, test_potential = prepare_water_system( coords, lambda_plane_idxs, lambda_offset_idxs, p_scale=1.0, cutoff=cutoff ) def run_nonbonded(precision, potential, x, box, params, lamb, steps=100): test_impl = test_potential.unbound_impl(precision) x = (x.astype(np.float32)).astype(np.float64) params = (params.astype(np.float32)).astype(np.float64) assert x.ndim == 2 # N = x.shape[0] # D = x.shape[1] assert x.dtype == np.float64 assert params.dtype == np.float64 for _ in range(steps): _ = test_impl.execute_selective(x, params, box, lamb, True, True, True, True) # With the default box, all is well run_nonbonded(precision, ref_potential, coords, box, charge_params, 0.0, steps=2) db_cutoff = (cutoff + padding) * 2 # Make box with diagonals right at the limit box = np.eye(3) * db_cutoff run_nonbonded(precision, ref_potential, coords, box, charge_params, 0.0) # Non Orth Box, should fail box = np.ones_like(box) * (db_cutoff ** 2) with self.assertRaises(RuntimeError) as raised: run_nonbonded(precision, ref_potential, coords, box, charge_params, 0.0) assert "non-ortholinear box" in str(raised.exception) # Only populate the diag with values that are too low box = np.eye(3) * (db_cutoff * 0.3) with self.assertRaises(RuntimeError) as raised: run_nonbonded(precision, ref_potential, coords, box, charge_params, 0.0) assert "more than half" in str(raised.exception)
def test_barostat_zero_interval(): pressure = 1.0 * unit.atmosphere temperature = 300.0 * unit.kelvin initial_waterbox_width = 2.5 * unit.nanometer seed = 2021 np.random.seed(seed) mol_a = hif2a_ligand_pair.mol_a ff = hif2a_ligand_pair.ff complex_system, complex_coords, complex_box, complex_top = build_water_system( initial_waterbox_width.value_in_unit(unit.nanometer)) afe = AbsoluteFreeEnergy(mol_a, ff) unbound_potentials, sys_params, masses, coords = afe.prepare_host_edge( ff.get_ordered_params(), complex_system, complex_coords) # get list of molecules for barostat by looking at bond table harmonic_bond_potential = unbound_potentials[0] bond_list = get_bond_list(harmonic_bond_potential) group_indices = get_group_indices(bond_list) bound_potentials = [] for params, unbound_pot in zip(sys_params, unbound_potentials): bp = unbound_pot.bind(np.asarray(params)) bound_potentials.append(bp) u_impls = [] for bp in bound_potentials: bp_impl = bp.bound_impl(precision=np.float32) u_impls.append(bp_impl) with pytest.raises(RuntimeError): custom_ops.MonteCarloBarostat( coords.shape[0], pressure.value_in_unit(unit.bar), temperature.value_in_unit(unit.kelvin), group_indices, 0, u_impls, seed, ) # Setting it to 1 should be valid. baro = custom_ops.MonteCarloBarostat( coords.shape[0], pressure.value_in_unit(unit.bar), temperature.value_in_unit(unit.kelvin), group_indices, 1, u_impls, seed, ) # Setting back to 0 should raise another error with pytest.raises(RuntimeError): baro.set_interval(0)
def get_solvent_phase_system(mol, ff): masses = np.array([a.GetMass() for a in mol.GetAtoms()]) water_system, water_coords, water_box, water_topology = builders.build_water_system(3.0) water_box = water_box + np.eye(3) * 0.5 # add a small margin around the box for stability num_water_atoms = len(water_coords) afe = free_energy.AbsoluteFreeEnergy(mol, ff) ff_params = ff.get_ordered_params() ubps, params, masses, coords = afe.prepare_host_edge(ff_params, water_system, water_coords) host_coords = coords[:num_water_atoms] new_host_coords = minimizer.minimize_host_4d([mol], water_system, host_coords, ff, water_box) coords[:num_water_atoms] = new_host_coords return ubps, params, masses, coords, water_box
def test_nblist_box_resize(self): # test that running the coordinates under two different boxes produces correct results # since we should be rebuilding the nblist when the box sizes change. host_system, host_coords, box, _ = builders.build_water_system(3.0) host_fns, host_masses = openmm_deserializer.deserialize_system(host_system, cutoff=1.0) for f in host_fns: if isinstance(f, potentials.Nonbonded): test_nonbonded_fn = f host_conf = [] for x, y, z in host_coords: host_conf.append([to_md_units(x), to_md_units(y), to_md_units(z)]) host_conf = np.array(host_conf) lamb = 0.1 ref_nonbonded_fn = prepare_reference_nonbonded( test_nonbonded_fn.params, test_nonbonded_fn.get_exclusion_idxs(), test_nonbonded_fn.get_scale_factors(), test_nonbonded_fn.get_lambda_plane_idxs(), test_nonbonded_fn.get_lambda_offset_idxs(), test_nonbonded_fn.get_beta(), test_nonbonded_fn.get_cutoff(), ) big_box = box + np.eye(3) * 1000 # print(big_box, small_box) # (ytz): note the ordering should be from large box to small box. though in the current code # the rebuild is triggered as long as the box *changes*. for test_box in [big_box, box]: for precision, rtol, atol in [(np.float64, 1e-8, 1e-10), (np.float32, 1e-4, 3e-5)]: self.compare_forces( host_conf, test_nonbonded_fn.params, test_box, lamb, ref_nonbonded_fn, test_nonbonded_fn, rtol=rtol, atol=atol, precision=precision, )
def test_random_directory(self): with TemporaryDirectory(prefix="timemachine") as temp_dir: orig_dir = os.getcwd() os.chdir(temp_dir) try: # build a pair of alchemical ligands in a water box mol_a, mol_b, _, ff = ( hif2a_ligand_pair.mol_a, hif2a_ligand_pair.mol_b, hif2a_ligand_pair.core, hif2a_ligand_pair.ff, ) complex_system, complex_coords, complex_box, complex_top = build_water_system(2.6) # Creates a custom_ops.Context which triggers JIT minimize_host_4d([mol_a, mol_b], complex_system, complex_coords, ff, complex_box) finally: os.chdir(orig_dir)
def test_pre_equilibration(self): """Verify that equilibration of edges up front functions as expected""" complex_system, complex_coords, _, _, complex_box, _ = builders.build_protein_system( os.path.join(DATA_DIR, "hif2a_nowater_min.pdb")) # build the water system solvent_system, solvent_coords, solvent_box, _ = builders.build_water_system( 4.0) client = CUDAPoolClient(NUM_GPUS) model = RBFEModel( client=client, ff=hif2a_ligand_pair.ff, complex_system=complex_system, complex_coords=complex_coords, complex_box=complex_box, complex_schedule=construct_lambda_schedule(2), solvent_system=solvent_system, solvent_coords=solvent_coords, solvent_box=solvent_box, solvent_schedule=construct_lambda_schedule(2), equil_steps=10, prod_steps=100, ) mol_a = hif2a_ligand_pair.mol_a mol_b = hif2a_ligand_pair.mol_b core = hif2a_ligand_pair.core assert len(model._equil_cache) == 0 with TemporaryDirectory() as tempdir: cache_path = os.path.join(tempdir, "equil_cache.pkl") # If model.pre_equilibrate is false, its a noop model.equilibrate_edges([(mol_a, mol_b, core)], equilibration_steps=10, cache_path=cache_path) assert len(model._equil_cache) == 0 # Enable pre-equilibration model.pre_equilibrate = True model.equilibrate_edges([(mol_a, mol_b, core)], equilibration_steps=10, cache_path=cache_path) # Cache should contain starting coords for both solvent and complex stages assert len(model._equil_cache) == 2
def test_equilibrate_host(): host_system, host_coords, host_box, _ = builders.build_water_system(4.0) suppl = Chem.SDMolSupplier("tests/data/ligands_40.sdf", removeHs=False) mol = next(suppl) ff = Forcefield.load_from_file("smirnoff_1_1_0_ccc.py") coords, box = minimizer.equilibrate_host(mol, host_system, host_coords, 300, 1.0, ff, host_box, 25, seed=2022) assert coords.shape[0] == host_coords.shape[0] + mol.GetNumAtoms() assert coords.shape[1] == host_coords.shape[1] assert box.shape == host_box.shape
def test_write_single_topology_frame(): top = hif2a_ligand_pair.top assert isinstance(top, SingleTopology) ff_params = hif2a_ligand_pair.top.ff.get_ordered_params() solvent_system, solvent_coords, solvent_box, solvent_top = builders.build_water_system(4.0) unbound_potentials, sys_params, masses, coords = hif2a_ligand_pair.prepare_host_edge( ff_params, solvent_system, solvent_coords ) coords *= 10 # nm to angstroms with NamedTemporaryFile(suffix=".pdb") as temp: writer = PDBWriter([solvent_top, top.mol_a, top.mol_b], temp.name) with pytest.raises(ValueError): # Should fail, as incorrect number of coords writer.write_frame(coords) ligand_coords = convert_single_topology_mols(coords[len(solvent_coords) :], top) writer.write_frame(np.concatenate((coords[: len(solvent_coords)], ligand_coords), axis=0)) writer.close()
def test_nonbonded(self): np.random.seed(4321) for size in [33, 231, 1050]: _, coords, box, _ = builders.build_water_system(6.2) coords = coords / coords.unit coords = coords[:size] N = coords.shape[0] lambda_plane_idxs = np.random.randint(low=-2, high=2, size=N, dtype=np.int32) lambda_offset_idxs = np.random.randint(low=-2, high=2, size=N, dtype=np.int32) for precision, rtol, atol in [(np.float64, 1e-8, 3e-11), (np.float32, 1e-4, 3e-5)]: for cutoff in [1.0]: # E = 0 # DEBUG! charge_params, ref_potential, test_potential = prepare_water_system( coords, lambda_plane_idxs, lambda_offset_idxs, p_scale=1.0, cutoff=cutoff ) for lamb in [0.0, 0.1, 0.2]: print("lambda", lamb, "cutoff", cutoff, "precision", precision, "xshape", coords.shape) self.compare_forces( coords, charge_params, box, lamb, ref_potential, test_potential, rtol=rtol, atol=atol, precision=precision, )
def test_predict(self): """Just to verify that we can handle the most basic RBFE prediction""" # Use the Simple Charges to verify determinism of model. Needed as one endpoint uses the ff definition forcefield = Forcefield.load_from_file("smirnoff_1_1_0_sc.py") complex_system, complex_coords, _, _, complex_box, _ = builders.build_protein_system( os.path.join(DATA_DIR, "hif2a_nowater_min.pdb")) # build the water system solvent_system, solvent_coords, solvent_box, _ = builders.build_water_system( 4.0) client = CUDAPoolClient(NUM_GPUS) model = RBFEModel( client=client, ff=forcefield, complex_system=complex_system, complex_coords=complex_coords, complex_box=complex_box, complex_schedule=construct_lambda_schedule(2), solvent_system=solvent_system, solvent_coords=solvent_coords, solvent_box=solvent_box, solvent_schedule=construct_lambda_schedule(2), equil_steps=10, prod_steps=100, ) ordered_params = forcefield.get_ordered_params() mol_a = hif2a_ligand_pair.mol_a mol_b = hif2a_ligand_pair.mol_b core = hif2a_ligand_pair.core ddg, results = model.predict(ordered_params, mol_a, mol_b, core) self.assertEqual(len(results), 2) self.assertIsInstance(ddg, float)
def test_relative_free_energy(): # test that we can properly build a single topology host guest system and # that we can run a few steps in a stable way. This tests runs both the complex # and the solvent stages. suppl = Chem.SDMolSupplier("tests/data/ligands_40.sdf", removeHs=False) all_mols = [x for x in suppl] mol_a = all_mols[1] mol_b = all_mols[4] core = np.array([ [0, 0], [2, 2], [1, 1], [6, 6], [5, 5], [4, 4], [3, 3], [15, 16], [16, 17], [17, 18], [18, 19], [19, 20], [20, 21], [32, 30], [26, 25], [27, 26], [7, 7], [8, 8], [9, 9], [10, 10], [29, 11], [11, 12], [12, 13], [14, 15], [31, 29], [13, 14], [23, 24], [30, 28], [28, 27], [21, 22], ]) complex_system, complex_coords, _, _, complex_box, _ = builders.build_protein_system( "tests/data/hif2a_nowater_min.pdb") # build the water system. solvent_system, solvent_coords, solvent_box, _ = builders.build_water_system( 4.0) ff = Forcefield.load_from_file("smirnoff_1_1_0_ccc.py") ff_params = ff.get_ordered_params() seed = 2021 lambda_schedule = np.linspace(0, 1.0, 4) equil_steps = 1000 prod_steps = 1000 single_topology = topology.SingleTopology(mol_a, mol_b, core, ff) rfe = free_energy.RelativeFreeEnergy(single_topology) def vacuum_model(ff_params): unbound_potentials, sys_params, masses, coords = rfe.prepare_vacuum_edge( ff_params) x0 = coords v0 = np.zeros_like(coords) client = CUDAPoolClient(1) box = np.eye(3, dtype=np.float64) * 100 harmonic_bond_potential = unbound_potentials[0] group_idxs = get_group_indices(get_bond_list(harmonic_bond_potential)) x0 = coords v0 = np.zeros_like(coords) client = CUDAPoolClient(1) temperature = 300.0 pressure = 1.0 integrator = LangevinIntegrator(temperature, 1.5e-3, 1.0, masses, seed) barostat = MonteCarloBarostat(x0.shape[0], pressure, temperature, group_idxs, 25, seed) model = estimator.FreeEnergyModel(unbound_potentials, client, box, x0, v0, integrator, lambda_schedule, equil_steps, prod_steps, barostat) return estimator.deltaG(model, sys_params)[0] dG = vacuum_model(ff_params) assert np.abs(dG) < 1000.0 def binding_model(ff_params): dGs = [] for host_system, host_coords, host_box in [ (complex_system, complex_coords, complex_box), (solvent_system, solvent_coords, solvent_box), ]: # minimize the host to avoid clashes host_coords = minimizer.minimize_host_4d([mol_a], host_system, host_coords, ff, host_box) unbound_potentials, sys_params, masses, coords = rfe.prepare_host_edge( ff_params, host_system, host_coords) x0 = coords v0 = np.zeros_like(coords) client = CUDAPoolClient(1) harmonic_bond_potential = unbound_potentials[0] group_idxs = get_group_indices( get_bond_list(harmonic_bond_potential)) temperature = 300.0 pressure = 1.0 integrator = LangevinIntegrator(temperature, 1.5e-3, 1.0, masses, seed) barostat = MonteCarloBarostat(x0.shape[0], pressure, temperature, group_idxs, 25, seed) model = estimator.FreeEnergyModel( unbound_potentials, client, host_box, x0, v0, integrator, lambda_schedule, equil_steps, prod_steps, barostat, ) dG, _ = estimator.deltaG(model, sys_params) dGs.append(dG) return dGs[0] - dGs[1] dG = binding_model(ff_params) assert np.abs(dG) < 1000.0
def main(args, stage): # benzene = Chem.AddHs(Chem.MolFromSmiles("c1ccccc1")) # a # phenol = Chem.AddHs(Chem.MolFromSmiles("Oc1ccccc1")) # b # 01234567890 benzene = Chem.AddHs(Chem.MolFromSmiles("C1=CC=C2C=CC=CC2=C1")) # a phenol = Chem.AddHs(Chem.MolFromSmiles("C1=CC=C2C=CC=CC2=C1")) # b AllChem.EmbedMolecule(benzene) AllChem.EmbedMolecule(phenol) ff_handlers = Forcefield.load_from_file( "smirnoff_1_1_0_ccc.py").get_ordered_handles() r_benzene = Recipe.from_rdkit(benzene, ff_handlers) r_phenol = Recipe.from_rdkit(phenol, ff_handlers) r_combined = r_benzene.combine(r_phenol) core_pairs = np.array( [ [0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], # [10,10] ], dtype=np.int32, ) core_pairs[:, 1] += benzene.GetNumAtoms() a_idxs = np.arange(benzene.GetNumAtoms()) b_idxs = np.arange(phenol.GetNumAtoms()) + benzene.GetNumAtoms() core_k = 20.0 if stage == 0: centroid_k = 200.0 rbfe.stage_0(r_combined, b_idxs, core_pairs, centroid_k, core_k) # lambda_schedule = np.linspace(0.0, 1.0, 2) # lambda_schedule = np.array([0.0, 0.0, 0.0, 0.0, 0.0]) lambda_schedule = np.array([0.0, 0.0, 0.0, 0.0, 0.0]) elif stage == 1: rbfe.stage_1(r_combined, a_idxs, b_idxs, core_pairs, core_k) lambda_schedule = np.linspace(0.0, 1.2, 60) else: assert 0 system, host_coords, box, topology = builders.build_water_system(4.0) r_host = Recipe.from_openmm(system) r_final = r_host.combine(r_combined) # minimize coordinates of host + ligand A ha_coords = np.concatenate([host_coords, get_romol_conf(benzene)]) pool = Pool(args.num_gpus) # we need to run this in a subprocess since the cuda runtime # must not be initialized in the master thread due to lack of # fork safety r_minimize = minimize_setup(r_host, r_benzene) ha_coords = pool.map( minimize, [(r_minimize.bound_potentials, r_minimize.masses, ha_coords, box)], chunksize=1) # this is a list ha_coords = ha_coords[0] pool.close() pool = Pool(args.num_gpus) x0 = np.concatenate([ha_coords, get_romol_conf(phenol)]) masses = np.concatenate([r_host.masses, r_benzene.masses, r_phenol.masses]) seed = np.random.randint(np.iinfo(np.int32).max) intg = LangevinIntegrator(300.0, 1.5e-3, 1.0, masses, seed) # production run at various values of lambda for epoch in range(10): avg_du_dls = [] run_args = [] for lamb_idx, lamb in enumerate(lambda_schedule): run_args.append( (lamb, intg, r_final.bound_potentials, r_final.masses, x0, box, lamb_idx % args.num_gpus, stage)) avg_du_dls = pool.map(run, run_args, chunksize=1) print("stage", stage, "epoch", epoch, "dG", np.trapz(avg_du_dls, lambda_schedule))
def test_neighborlist_ligand_host(): ligand = hif2a_ligand_pair.mol_a ligand_coords = get_romol_conf(ligand) system, host_coords, box, top = build_water_system(4.0) num_host_atoms = host_coords.shape[0] host_coords = np.array(host_coords) coords = np.concatenate([host_coords, ligand_coords]) N = coords.shape[0] D = 3 cutoff = 1.0 block_size = 32 padding = 0.1 np.random.seed(1234) diag = np.amax(coords, axis=0) - np.amin(coords, axis=0) + padding box = np.diag(diag) # Can only sort the host coords, but not the row/ligand sort = True if sort: perm = hilbert_sort( coords[:num_host_atoms] + np.argmin(coords[:num_host_atoms]), D) coords[:num_host_atoms] = coords[:num_host_atoms][perm] col_coords = np.expand_dims(coords[:num_host_atoms], axis=0) # Compute the reference interactions of the ligand ref_ixn_list = [] num_ligand_atoms = coords[num_host_atoms:].shape[0] num_blocks_of_32 = (num_ligand_atoms + block_size - 1) // block_size box_diag = np.diag(box) for rbidx in range(num_blocks_of_32): row_start = num_host_atoms + (rbidx * block_size) row_end = min(num_host_atoms + ((rbidx + 1) * block_size), N) row_coords = coords[row_start:row_end] row_coords = np.expand_dims(row_coords, axis=1) deltas = row_coords - col_coords deltas -= box_diag * np.floor(deltas / box_diag + 0.5) dij = np.linalg.norm(deltas, axis=-1) # Since the row and columns are unique, don't need to handle duplicates idxs = np.argwhere(np.any(dij < cutoff, axis=0)) ref_ixn_list.append(idxs.reshape(-1).tolist()) for nblist in ( custom_ops.Neighborlist_f32(num_host_atoms, num_ligand_atoms), custom_ops.Neighborlist_f64(num_host_atoms, num_ligand_atoms), ): for _ in range(2): test_ixn_list = nblist.get_nblist_host_ligand( coords[:num_host_atoms], coords[num_host_atoms:], box, cutoff) # compute the sparsity of the tile assert len(ref_ixn_list) == len( test_ixn_list ), "Number of blocks with interactions don't agree" for bidx, (a, b) in enumerate(zip(ref_ixn_list, test_ixn_list)): if sorted(a) != sorted(b): print("TESTING bidx", bidx) print(sorted(a)) print(sorted(b)) np.testing.assert_equal(sorted(a), sorted(b))
def benchmark_hif2a(verbose=False, num_batches=100, steps_per_batch=1000): from timemachine.testsystems.relative import hif2a_ligand_pair as testsystem mol_a, mol_b, core = testsystem.mol_a, testsystem.mol_b, testsystem.core ff = Forcefield.load_from_file("smirnoff_1_1_0_sc.py") single_topology = SingleTopology(mol_a, mol_b, core, ff) rfe = free_energy.RelativeFreeEnergy(single_topology) ff_params = ff.get_ordered_params() # build the protein system. complex_system, complex_coords, _, _, complex_box, _ = builders.build_protein_system( "tests/data/hif2a_nowater_min.pdb" ) solvent_system, solvent_coords, solvent_box, _ = builders.build_water_system(4.0) for stage, host_system, host_coords, host_box in [ ("hif2a", complex_system, complex_coords, complex_box), ("solvent", solvent_system, solvent_coords, solvent_box), ]: host_fns, host_masses = openmm_deserializer.deserialize_system(host_system, cutoff=1.0) # resolve host clashes min_host_coords = minimizer.minimize_host_4d([mol_a, mol_b], host_system, host_coords, ff, host_box) x0 = min_host_coords v0 = np.zeros_like(x0) # lamb = 0.0 benchmark( stage + "-apo", host_masses, 0.0, x0, v0, host_box, host_fns, verbose=verbose, num_batches=num_batches, steps_per_batch=steps_per_batch, ) benchmark( stage + "-apo-barostat-interval-25", host_masses, 0.0, x0, v0, host_box, host_fns, verbose=verbose, num_batches=num_batches, steps_per_batch=steps_per_batch, barostat_interval=25, ) # RBFE unbound_potentials, sys_params, masses, coords = rfe.prepare_host_edge(ff_params, host_system, x0) bound_potentials = [x.bind(y) for (x, y) in zip(unbound_potentials, sys_params)] x0 = coords v0 = np.zeros_like(x0) # lamb = 0.5 benchmark( stage + "-rbfe-with-du-dp", masses, 0.5, x0, v0, host_box, bound_potentials, verbose=verbose, num_batches=num_batches, steps_per_batch=steps_per_batch, ) for du_dl_interval in [0, 1, 5]: benchmark( stage + "-rbfe-du-dl-interval-" + str(du_dl_interval), masses, 0.5, x0, v0, host_box, bound_potentials, verbose=verbose, num_batches=num_batches, steps_per_batch=steps_per_batch, compute_du_dl_interval=du_dl_interval, )
def test_barostat_partial_group_idxs(): """Verify that the barostat can handle a subset of the molecules rather than all of them. This test only verify that it runs, not the behavior""" temperature = 300.0 * unit.kelvin initial_waterbox_width = 3.0 * unit.nanometer timestep = 1.5 * unit.femtosecond barostat_interval = 3 collision_rate = 1.0 / unit.picosecond seed = 2021 np.random.seed(seed) pressure = 1.0 * unit.atmosphere mol_a = hif2a_ligand_pair.mol_a ff = hif2a_ligand_pair.ff complex_system, complex_coords, complex_box, complex_top = build_water_system( initial_waterbox_width.value_in_unit(unit.nanometer)) min_complex_coords = minimize_host_4d([mol_a], complex_system, complex_coords, ff, complex_box) afe = AbsoluteFreeEnergy(mol_a, ff) unbound_potentials, sys_params, masses, coords = afe.prepare_host_edge( ff.get_ordered_params(), complex_system, min_complex_coords) # get list of molecules for barostat by looking at bond table harmonic_bond_potential = unbound_potentials[0] bond_list = get_bond_list(harmonic_bond_potential) group_indices = get_group_indices(bond_list) # Cut the number of groups in half group_indices = group_indices[len(group_indices) // 2:] lam = 1.0 bound_potentials = [] for params, unbound_pot in zip(sys_params, unbound_potentials): bp = unbound_pot.bind(np.asarray(params)) bound_potentials.append(bp) u_impls = [] for bp in bound_potentials: bp_impl = bp.bound_impl(precision=np.float32) u_impls.append(bp_impl) integrator = LangevinIntegrator( temperature.value_in_unit(unit.kelvin), timestep.value_in_unit(unit.picosecond), collision_rate.value_in_unit(unit.picosecond**-1), masses, seed, ) integrator_impl = integrator.impl() v_0 = sample_velocities(masses * unit.amu, temperature) baro = custom_ops.MonteCarloBarostat( coords.shape[0], pressure.value_in_unit(unit.bar), temperature.value_in_unit(unit.kelvin), group_indices, barostat_interval, u_impls, seed, ) ctxt = custom_ops.Context(coords, v_0, complex_box, integrator_impl, u_impls, barostat=baro) ctxt.multiple_steps(np.ones(1000) * lam)
def test_predict_absolute_conversion(self): """Just to verify that we can handle the most basic conversion RABFE prediction""" # Use the Simple Charges to verify determinism of model. Needed as one endpoint uses the ff definition forcefield = Forcefield.load_from_file("smirnoff_1_1_0_sc.py") # build the water system solvent_system, solvent_coords, solvent_box, solvent_topology = builders.build_water_system( 4.0) temperature = 300.0 pressure = 1.0 dt = 2.5e-3 client = CUDAPoolClient(NUM_GPUS) model = AbsoluteConversionModel( client, forcefield, solvent_system, construct_lambda_schedule(2), solvent_topology, temperature, pressure, dt, 10, 50, frame_filter=all_frames, ) mol_a = hif2a_ligand_pair.mol_a mol_b = hif2a_ligand_pair.mol_b core_idxs = setup_relative_restraints_by_distance(mol_a, mol_b) ref_coords = get_romol_conf(mol_a) mol_coords = get_romol_conf(mol_b) # original coords # Use core_idxs to generate R, t = rmsd.get_optimal_rotation_and_translation( x1=ref_coords[core_idxs[:, 1]], # reference core atoms x2=mol_coords[core_idxs[:, 0]], # mol core atoms ) aligned_mol_coords = rmsd.apply_rotation_and_translation( mol_coords, R, t) solvent_coords = minimizer.minimize_host_4d([mol_b], solvent_system, solvent_coords, forcefield, solvent_box, [aligned_mol_coords]) solvent_x0 = np.concatenate([solvent_coords, aligned_mol_coords]) ordered_params = forcefield.get_ordered_params() with temporary_working_dir() as temp_dir: dG, dG_err = model.predict(ordered_params, mol_b, solvent_x0, solvent_box, "prefix", core_idxs=core_idxs[:, 0], seed=2022) np.testing.assert_almost_equal(dG, 46.102816, decimal=5) np.testing.assert_equal(dG_err, 0.0) created_files = os.listdir(temp_dir) # 2 npz, 1 pdb and 1 npy per mol due to a->b and b->a self.assertEqual(len(created_files), 4) self.assertEqual( len([x for x in created_files if x.endswith(".pdb")]), 1) self.assertEqual( len([x for x in created_files if x.endswith(".npy")]), 1) self.assertEqual( len([x for x in created_files if x.endswith(".npz")]), 2)
n_replicates = 10 initial_waterbox_width = 3.0 * unit.nanometer timestep = 1.5 * unit.femtosecond collision_rate = 1.0 / unit.picosecond n_moves = 2000 barostat_interval = 5 seed = 2021 # thermodynamic parameters temperature = 300 * unit.kelvin pressure = 1.013 * unit.bar # generate an alchemical system of a waterbox + alchemical ligand: # effectively discard ligands by running in AbsoluteFreeEnergy mode at lambda = 1.0 mol_a, _, core, ff = hif2a_ligand_pair.mol_a, hif2a_ligand_pair.mol_b, hif2a_ligand_pair.core, hif2a_ligand_pair.ff complex_system, complex_coords, complex_box, complex_top = build_water_system( initial_waterbox_width.value_in_unit(unit.nanometer)) min_complex_coords = minimize_host_4d([mol_a], complex_system, complex_coords, ff, complex_box) afe = AbsoluteFreeEnergy(mol_a, ff) unbound_potentials, sys_params, masses, coords = afe.prepare_host_edge( ff.get_ordered_params(), complex_system, min_complex_coords) # define NPT ensemble potential_energy_model = PotentialEnergyModel(sys_params, unbound_potentials) ensemble = NPTEnsemble(potential_energy_model, temperature, pressure) # define a thermostat integrator = LangevinIntegrator(
with open(output_path.joinpath("training_edges.pk"), "wb") as ofs: dump(training.data, ofs) if len(validation): with open(output_path.joinpath("validation_edges.pk"), "wb") as ofs: dump(validation.data, ofs) # Build all of the different protein systems systems = {} for prot_path in protein_paths: # build the complex system # note: "complex" means "protein + solvent" complex_system, complex_coords, _, _, complex_box, _ = builders.build_protein_system( prot_path) # build the water system solvent_system, solvent_coords, solvent_box, _ = builders.build_water_system( 4.0) systems[prot_path] = RBFEModel( client=client, ff=forcefield, complex_system=complex_system, complex_coords=complex_coords, complex_box=complex_box, complex_schedule=construct_lambda_schedule( configuration.num_complex_windows), solvent_system=solvent_system, solvent_coords=solvent_coords, solvent_box=solvent_box, solvent_schedule=construct_lambda_schedule( configuration.num_solvent_windows), equil_steps=configuration.num_equil_steps,
def test_molecular_ideal_gas(): """ References ---------- OpenMM testIdealGas https://github.com/openmm/openmm/blob/d8ef57fed6554ec95684e53768188e1f666405c9/tests/TestMonteCarloBarostat.h#L86-L140 """ # simulation parameters initial_waterbox_width = 3.0 * unit.nanometer timestep = 1.5 * unit.femtosecond collision_rate = 1.0 / unit.picosecond n_moves = 10000 barostat_interval = 5 seed = 2021 # thermodynamic parameters temperatures = np.array([300, 600, 1000]) * unit.kelvin pressure = 100.0 * unit.bar # very high pressure, to keep the expected volume small # generate an alchemical system of a waterbox + alchemical ligand: # effectively discard ligands by running in AbsoluteFreeEnergy mode at lambda = 1.0 mol_a = hif2a_ligand_pair.mol_a ff = hif2a_ligand_pair.ff complex_system, complex_coords, complex_box, complex_top = build_water_system( initial_waterbox_width.value_in_unit(unit.nanometer)) min_complex_coords = minimize_host_4d([mol_a], complex_system, complex_coords, ff, complex_box) afe = AbsoluteFreeEnergy(mol_a, ff) _unbound_potentials, _sys_params, masses, coords = afe.prepare_host_edge( ff.get_ordered_params(), complex_system, min_complex_coords) # drop the nonbonded potential unbound_potentials = _unbound_potentials[:-1] sys_params = _sys_params[:-1] # get list of molecules for barostat by looking at bond table harmonic_bond_potential = unbound_potentials[0] bond_list = get_bond_list(harmonic_bond_potential) group_indices = get_group_indices(bond_list) volume_trajs = [] relative_tolerance = 1e-2 initial_relative_box_perturbation = 2 * relative_tolerance n_molecules = complex_top.getNumResidues() bound_potentials = [] for params, unbound_pot in zip(sys_params, unbound_potentials): bp = unbound_pot.bind(np.asarray(params)) bound_potentials.append(bp) u_impls = [] for bp in bound_potentials: bp_impl = bp.bound_impl(precision=np.float32) u_impls.append(bp_impl) # expected volume md_pressure_unit = ENERGY_UNIT / DISTANCE_UNIT**3 pressure_in_md = ( pressure * unit.AVOGADRO_CONSTANT_NA).value_in_unit(md_pressure_unit) expected_volume_in_md = (n_molecules + 1) * BOLTZ * temperatures.value_in_unit( unit.kelvin) / pressure_in_md for i, temperature in enumerate(temperatures): # define a thermostat integrator = LangevinIntegrator( temperature.value_in_unit(unit.kelvin), timestep.value_in_unit(unit.picosecond), collision_rate.value_in_unit(unit.picosecond**-1), masses, seed, ) integrator_impl = integrator.impl() v_0 = sample_velocities(masses * unit.amu, temperature) # rescale the box to be approximately the desired box volume already rescaler = CentroidRescaler(group_indices) initial_volume = compute_box_volume(complex_box) initial_center = compute_box_center(complex_box) length_scale = ((1 + initial_relative_box_perturbation) * expected_volume_in_md[i] / initial_volume)**(1.0 / 3) new_coords = rescaler.scale_centroids(coords, initial_center, length_scale) new_box = complex_box * length_scale baro = custom_ops.MonteCarloBarostat( new_coords.shape[0], pressure.value_in_unit(unit.bar), temperature.value_in_unit(unit.kelvin), group_indices, barostat_interval, u_impls, seed, ) ctxt = custom_ops.Context(new_coords, v_0, new_box, integrator_impl, u_impls, barostat=baro) vols = [] for move in range(n_moves // barostat_interval): ctxt.multiple_steps(np.ones(barostat_interval)) new_box = ctxt.get_box() volume = np.linalg.det(new_box) vols.append(volume) volume_trajs.append(vols) equil_time = len(volume_trajs[0]) // 2 # TODO: don't hard-code this? actual_volume_in_md = np.array( [np.mean(volume_traj[equil_time:]) for volume_traj in volume_trajs]) np.testing.assert_allclose(actual=actual_volume_in_md, desired=expected_volume_in_md, rtol=relative_tolerance)
def do_relative_docking(host_pdbfile, mol_a, mol_b, core, num_switches, transition_steps): """Runs non-equilibrium switching jobs: 1. Solvates a protein, minimizes w.r.t guest_A, equilibrates & spins off switching jobs (deleting guest_A while inserting guest_B) every 1000th step, calculates work. 2. Does the same thing in solvent instead of protein Does num_switches switching jobs per leg. Parameters ---------- host_pdbfile (str): path to host pdb file mol_a (rdkit mol): the starting ligand to swap from mol_b (rdkit mol): the ending ligand to swap to core (np.array[[int, int], [int, int], ...]): the common core atoms between mol_a and mol_b num_switches (int): number of switching trajectories to run per compound pair per leg transition_stpes (int): length of each switching trajectory Returns ------- {str: float}: map of leg label to work values of switching mol_a to mol_b in that leg, {'protein': [work values], 'solvent': [work_values]} Output ------ stdout noting the step number, lambda value, and energy at various steps stdout noting the work of transition, if applicable stdout noting how long it took to run Note ---- The work will not be calculated if any norm of force per atom exceeds 20000 kJ/(mol*nm) [MAX_NORM_FORCE defined in docking/report.py] The simulations won't run if the atom maps are not factorizable """ # Prepare host # TODO: handle extra (non-transitioning) guests? print("Solvating host...") ( solvated_host_system, solvated_host_coords, _, _, host_box, solvated_topology, ) = builders.build_protein_system(host_pdbfile) # Prepare water box print("Generating water box...") # TODO: water box probably doesn't need to be this big box_lengths = host_box[np.diag_indices(3)] water_box_width = min(box_lengths) ( water_system, water_coords, water_box, water_topology, ) = builders.build_water_system(water_box_width) # it's okay if the water box here and the solvated protein box don't align -- they have PBCs # Run the procedure start_time = time.time() guest_name_a = mol_a.GetProp("_Name") guest_name_b = mol_b.GetProp("_Name") combined_name = guest_name_a + "-->" + guest_name_b guest_conformer_a = mol_a.GetConformer(0) orig_guest_coords_a = np.array(guest_conformer_a.GetPositions(), dtype=np.float64) orig_guest_coords_a = orig_guest_coords_a / 10 # convert to md_units ff = Forcefield.load_from_file("smirnoff_1_1_0_ccc.py") all_works = {} for system, coords, box, label in zip( [solvated_host_system, water_system], [solvated_host_coords, water_coords], [host_box, water_box], ["protein", "solvent"], ): # minimize w.r.t. both mol_a and mol_b? min_coords = minimizer.minimize_host_4d([mol_a], system, coords, ff, box) try: single_topology = topology.SingleTopology(mol_a, mol_b, core, ff) rfe = free_energy.RelativeFreeEnergy(single_topology) ups, sys_params, combined_masses, combined_coords = rfe.prepare_host_edge( ff.get_ordered_params(), system, min_coords) except topology.AtomMappingError as e: print(f"NON-FACTORIZABLE PAIR: {combined_name}") print(e) return {} combined_bps = [] for up, sp in zip(ups, sys_params): combined_bps.append(up.bind(sp)) all_works[label] = run_leg( combined_coords, combined_bps, combined_masses, box, combined_name, label, num_switches, transition_steps, ) end_time = time.time() print( f"{combined_name} {label} leg time:", "%.2f" % (end_time - start_time), "seconds", ) return all_works
def estimate_dG( transformation: RelativeTransformation, num_lambdas: int, num_steps_per_lambda: int, num_equil_steps: int, ): # build the protein system. complex_system, complex_coords, _, _, complex_box = builders.build_protein_system( path_to_protein) # build the water system. solvent_system, solvent_coords, solvent_box, _ = builders.build_water_system( 4.0) stage_dGs = [] ff = transformation.ff mol_a, mol_b = transformation.mol_a, transformation.mol_b core = transformation.core # TODO: measure performance of complex and solvent separately lambda_schedule = construct_lambda_schedule(num_lambdas) for stage, host_system, host_coords, host_box in [ ("complex", complex_system, complex_coords, complex_box), ("solvent", solvent_system, solvent_coords, solvent_box), ]: print("Minimizing the host structure to remove clashes.") minimized_host_coords = minimizer.minimize_host_4d( mol_a, host_system, host_coords, ff, host_box) single_topology = topology.SingleTopology(mol_a, mol_b, core, ff) rfe = free_energy.RelativeFreeEnergy(single_topology) # solvent leg host_args = [] for lambda_idx, lamb in enumerate(lambda_schedule): gpu_idx = lambda_idx % num_gpus host_args.append( (gpu_idx, lamb, host_system, minimized_host_coords, host_box, num_equil_steps, num_steps_per_lambda)) # one GPU job per lambda window print("submitting tasks to client!") do_work = partial(wrap_method, fxn=rfe.host_edge) futures = [] for lambda_idx, lamb in enumerate(lambda_schedule): arg = (lamb, host_system, minimized_host_coords, host_box, num_equil_steps, num_steps_per_lambda) futures.append(client.submit(do_work, arg)) results = [] for fut in futures: results.append(fut.result()) def _mean_du_dlambda(result): """summarize result of rfe.host_edge into mean du/dl TODO: refactor where this analysis step occurs """ bonded_du_dl, nonbonded_du_dl, _ = result return np.mean(bonded_du_dl + nonbonded_du_dl) dG_host = np.trapz([_mean_du_dlambda(x) for x in results], lambda_schedule) stage_dGs.append(dG_host) pred = stage_dGs[0] - stage_dGs[1] return pred
# construct an RDKit molecule of aspirin # note: not using OpenFF Molecule because want to avoid the dependency (YTZ?) romol = Chem.AddHs(Chem.MolFromSmiles("CC(=O)OC1=CC=CC=C1C(=O)O")) ligand_masses = [a.GetMass() for a in romol.GetAtoms()] # generate conformers AllChem.EmbedMolecule(romol) # extract the 0th conformer ligand_coords = get_romol_conf(romol) # construct a 4-nanometer water box (from openmmtools approach: selecting out # of a large pre-equilibrated water box snapshot) system, host_coords, box, omm_topology = builders.build_water_system(4.0) host_bps, host_masses = openmm_deserializer.deserialize_system(system, cutoff=1.2) combined_masses = np.concatenate([host_masses, ligand_masses]) # write some conformations into this PDB file writer = pdb_writer.PDBWriter([omm_topology, romol], "debug.pdb") # note the order in which the coordinates are concatenated in this step -- # in a later step we will need to combine recipes in the same order combined_coords = np.concatenate([host_coords, ligand_coords]) num_host_atoms = host_coords.shape[0]
def test_barostat_varying_pressure(): temperature = 300.0 * unit.kelvin initial_waterbox_width = 3.0 * unit.nanometer timestep = 1.5 * unit.femtosecond barostat_interval = 3 collision_rate = 1.0 / unit.picosecond seed = 2021 np.random.seed(seed) # Start out with a very large pressure pressure = 1000.0 * unit.atmosphere mol_a = hif2a_ligand_pair.mol_a ff = hif2a_ligand_pair.ff complex_system, complex_coords, complex_box, complex_top = build_water_system( initial_waterbox_width.value_in_unit(unit.nanometer)) min_complex_coords = minimize_host_4d([mol_a], complex_system, complex_coords, ff, complex_box) afe = AbsoluteFreeEnergy(mol_a, ff) unbound_potentials, sys_params, masses, coords = afe.prepare_host_edge( ff.get_ordered_params(), complex_system, min_complex_coords) # get list of molecules for barostat by looking at bond table harmonic_bond_potential = unbound_potentials[0] bond_list = get_bond_list(harmonic_bond_potential) group_indices = get_group_indices(bond_list) lam = 1.0 u_impls = [] for params, unbound_pot in zip(sys_params, unbound_potentials): bp = unbound_pot.bind(np.asarray(params)) bp_impl = bp.bound_impl(precision=np.float32) u_impls.append(bp_impl) integrator = LangevinIntegrator( temperature.value_in_unit(unit.kelvin), timestep.value_in_unit(unit.picosecond), collision_rate.value_in_unit(unit.picosecond**-1), masses, seed, ) integrator_impl = integrator.impl() v_0 = sample_velocities(masses * unit.amu, temperature) baro = custom_ops.MonteCarloBarostat( coords.shape[0], pressure.value_in_unit(unit.bar), temperature.value_in_unit(unit.kelvin), group_indices, barostat_interval, u_impls, seed, ) ctxt = custom_ops.Context(coords, v_0, complex_box, integrator_impl, u_impls, barostat=baro) ctxt.multiple_steps(np.ones(1000) * lam) ten_atm_box = ctxt.get_box() ten_atm_box_vol = compute_box_volume(ten_atm_box) # Expect the box to shrink thanks to the barostat assert compute_box_volume(complex_box) - ten_atm_box_vol > 0.4 # Set the pressure to 1 bar baro.set_pressure((1 * unit.atmosphere).value_in_unit(unit.bar)) # Changing the barostat interval resets the barostat step. baro.set_interval(2) ctxt.multiple_steps(np.ones(2000) * lam) atm_box = ctxt.get_box() # Box will grow thanks to the lower pressure assert compute_box_volume(atm_box) > ten_atm_box_vol
def calculate_rigorous_work( host_pdbfile, guests_sdfile, outdir, num_deletions, deletion_steps, insertion_max_lambda=0.5, insertion_steps=501, eq1_steps=5001, fewer_outfiles=False, no_outfiles=False, ): """Runs non-equilibrium deletion jobs: 1. Solvates a protein, inserts guest, equilibrates, equilibrates more & spins off deletion jobs every 1000th step, calculates work. 2. Does the same thing in solvent instead of protein. Does num_deletions deletion jobs per leg per compound. Parameters ---------- host_pdbfile (str): path to host pdb file guests_sdfile (str): path to guests sdf file outdir (str): path to directory to which to write output num_deletions (int): number of deletion trajectories to run per leg per compound deletion_steps (int): length of each deletion trajectory insertion_max_lambda (float): how far away to insert from (0.0-1.0) insertion_steps (int): how long to insert over eq1_steps (int): how long to equilibrate after insertion and before starting the deletions fewer_outfiles (bool): only save the starting frame of each deletion trajectory no_outfiles (bool): don't keep any output files Returns ------- {str: {str: float}}: map of compound to leg label to work values {'guest_1': {'protein': [work values], 'solvent': [work_values]}, ...} Output ------ A pdb & sdf file for each guest's final insertion step (outdir/<guest_name>_pd_<step>_host.pdb & outdir/<guest_name>_pd_<step>_guest.sdf) (unless fewer_outfiles or no_outfiles is True) A pdb & sdf file for each guest's final eq1 step (outdir/<guest_name>_pd_<step>_host.pdb & outdir/<guest_name>_pd_<step>_guest.sdf) (unless fewer_outfiles or no_outfiles is True) A pdb & sdf file for each deletion job's first step (outdir/<guest_name>_pd_<step>_host.pdb & outdir/<guest_name>_pd_<step>_guest.sdf) (unless no_outfiles is True) stdout corresponding to the files written noting the lambda value and energy stdout noting the work of deletion, if applicable stdout noting how long each leg took to run Note ---- The work will not be calculated if the du_dl endpoints are not close to 0 or if any norm of force per atom exceeds 20000 kJ/(mol*nm) [MAX_NORM_FORCE defined in docking/report.py] """ if not os.path.exists(outdir): os.makedirs(outdir) print(f""" HOST_PDBFILE = {host_pdbfile} GUESTS_SDFILE = {guests_sdfile} OUTDIR = {outdir} DELETION_MAX_LAMBDA = {DELETION_MAX_LAMBDA} MIN_LAMBDA = {MIN_LAMBDA} insertion_max_lambda = {insertion_max_lambda} insertion_steps = {insertion_steps} eq1_steps = {eq1_steps} num_deletions = {num_deletions} deletion_steps = {deletion_steps} """) # Prepare host # TODO: handle extra (non-transitioning) guests? print("Solvating host...") ( solvated_host_system, solvated_host_coords, _, _, host_box, solvated_topology, ) = builders.build_protein_system(host_pdbfile) _, solvated_host_pdb = tempfile.mkstemp(suffix=".pdb", text=True) writer = pdb_writer.PDBWriter([solvated_topology], solvated_host_pdb) writer.write_frame(solvated_host_coords) writer.close() solvated_host_mol = Chem.MolFromPDBFile(solvated_host_pdb, removeHs=False) os.remove(solvated_host_pdb) # Prepare water box print("Generating water box...") # TODO: water box probably doesn't need to be this big box_lengths = host_box[np.diag_indices(3)] water_box_width = min(box_lengths) ( water_system, water_coords, water_box, water_topology, ) = builders.build_water_system(water_box_width) # it's okay if the water box here and the solvated protein box don't align -- they have PBCs _, water_pdb = tempfile.mkstemp(suffix=".pdb", text=True) writer = pdb_writer.PDBWriter([water_topology], water_pdb) writer.write_frame(water_coords) writer.close() water_mol = Chem.MolFromPDBFile(water_pdb, removeHs=False) os.remove(water_pdb) ff = Forcefield.load_from_file("smirnoff_1_1_0_ccc.py") # Run the procedure all_works = defaultdict(dict) print("Getting guests...") suppl = Chem.SDMolSupplier(guests_sdfile, removeHs=False) for guest_mol in suppl: start_time = time.time() guest_name = guest_mol.GetProp("_Name") guest_conformer = guest_mol.GetConformer(0) orig_guest_coords = np.array(guest_conformer.GetPositions(), dtype=np.float64) orig_guest_coords = orig_guest_coords / 10 # convert to md_units for system, coords, host_mol, box, label in zip( [solvated_host_system, water_system], [solvated_host_coords, water_coords], [solvated_host_mol, water_mol], [host_box, water_box], ["protein", "solvent"], ): minimized_coords = minimizer.minimize_host_4d([guest_mol], system, coords, ff, box) afe = free_energy.AbsoluteFreeEnergy(guest_mol, ff) ups, sys_params, combined_masses, combined_coords = afe.prepare_host_edge( ff.get_ordered_params(), system, minimized_coords) combined_bps = [] for up, sp in zip(ups, sys_params): combined_bps.append(up.bind(sp)) works = run_leg( minimized_coords, orig_guest_coords, combined_bps, combined_masses, box, guest_name, label, host_mol, guest_mol, outdir, num_deletions, deletion_steps, insertion_max_lambda, insertion_steps, eq1_steps, fewer_outfiles, no_outfiles, ) all_works[guest_name][label] = works end_time = time.time() print( f"{guest_name} {label} leg time:", "%.2f" % (end_time - start_time), "seconds", ) return all_works
def test_absolute_free_energy(): suppl = Chem.SDMolSupplier("tests/data/ligands_40.sdf", removeHs=False) all_mols = [x for x in suppl] mol = all_mols[1] complex_system, complex_coords, _, _, complex_box, _ = builders.build_protein_system( "tests/data/hif2a_nowater_min.pdb") # build the water system. solvent_system, solvent_coords, solvent_box, _ = builders.build_water_system( 4.0) ff = Forcefield.load_from_file("smirnoff_1_1_0_ccc.py") ff_params = ff.get_ordered_params() seed = 2021 lambda_schedule = np.linspace(0, 1.0, 4) equil_steps = 1000 prod_steps = 1000 afe = free_energy.AbsoluteFreeEnergy(mol, ff) def absolute_model(ff_params): dGs = [] for host_system, host_coords, host_box in [ (complex_system, complex_coords, complex_box), (solvent_system, solvent_coords, solvent_box), ]: # minimize the host to avoid clashes host_coords = minimizer.minimize_host_4d([mol], host_system, host_coords, ff, host_box) unbound_potentials, sys_params, masses, coords = afe.prepare_host_edge( ff_params, host_system, host_coords) harmonic_bond_potential = unbound_potentials[0] group_idxs = get_group_indices( get_bond_list(harmonic_bond_potential)) x0 = coords v0 = np.zeros_like(coords) client = CUDAPoolClient(1) temperature = 300.0 pressure = 1.0 integrator = LangevinIntegrator(temperature, 1.5e-3, 1.0, masses, seed) barostat = MonteCarloBarostat(x0.shape[0], pressure, temperature, group_idxs, 25, seed) model = estimator.FreeEnergyModel( unbound_potentials, client, host_box, x0, v0, integrator, lambda_schedule, equil_steps, prod_steps, barostat, ) dG, _ = estimator.deltaG(model, sys_params) dGs.append(dG) return dGs[0] - dGs[1] dG = absolute_model(ff_params) assert np.abs(dG) < 1000.0
client = GRPCClient(hosts=cmd_args.hosts) client.verify() path_to_ligand = "tests/data/ligands_40.sdf" suppl = Chem.SDMolSupplier(path_to_ligand, removeHs=False) forcefield = Forcefield.load_from_file("smirnoff_1_1_0_ccc.py") mols = [x for x in suppl] dataset = Dataset(mols) absolute_solvent_schedule = construct_absolute_lambda_schedule_solvent( cmd_args.num_windows) relative_solvent_schedule = construct_relative_lambda_schedule( cmd_args.num_windows - 1) solvent_system, solvent_coords, solvent_box, solvent_topology = builders.build_water_system( 4.0) # pick the largest mol as the blocker largest_size = 0 ref_mol = None for mol in mols: if mol.GetNumAtoms() > largest_size: largest_size = mol.GetNumAtoms() ref_mol = mol print("Reference Molecule:", ref_mol.GetProp("_Name"), Chem.MolToSmiles(ref_mol)) temperature = 300.0 pressure = 1.0 dt = 2.5e-3
address, options=[ ("grpc.max_send_message_length", 500 * 1024 * 1024), ("grpc.max_receive_message_length", 500 * 1024 * 1024), ], ) stub = service_pb2_grpc.WorkerStub(channel) stubs.append(stub) ff_raw = open(forcefield, "r").read() ff_handlers = deserialize_handlers(ff_raw) box_width = 3.0 host_system, host_coords, box, _ = builders.build_water_system(box_width) lambda_schedule = np.array( [float(x) for x in general_cfg["lambda_schedule"].split(",")]) num_steps = int(general_cfg["n_steps"]) for epoch in range(100): print("Starting Epoch", epoch, datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S")) epoch_dir = os.path.join(general_cfg["out_dir"], "epoch_" + str(epoch)) if not os.path.exists(epoch_dir): os.makedirs(epoch_dir)
def test_endpoint_parameters_match_decoupling_and_conversion_complex(self): """Verifies that the parameters at the endpoint of conversion match with the starting parameters of the decoupling. Done on a complex model, as the hydration models differ Conv: P_start -> P_independent Decouple: P_independent -> P_arbitrary """ host_system, host_coords, host_box, host_topology = builders.build_water_system( 4.0) num_host_atoms = host_coords.shape[0] ff_params = hif2a_ligand_pair.ff.get_ordered_params() temperature = 300.0 pressure = 1.0 dt = 2.5e-3 client = CUDAPoolClient(NUM_GPUS) decouple_model = RelativeBindingModel( client, hif2a_ligand_pair.ff, host_system, construct_lambda_schedule(2), host_topology, temperature, pressure, dt, 10, 50, frame_filter=all_frames, ) blocker = hif2a_ligand_pair.mol_a ligand = hif2a_ligand_pair.mol_b decouple_topo = decouple_model.setup_topology(blocker, ligand) decouple_ref = RelativeFreeEnergy(decouple_topo) decouple_unbound_potentials, decouple_sys_params, _ = decouple_ref.prepare_host_edge( ff_params, decouple_model.host_system) conv_model = AbsoluteConversionModel( client, hif2a_ligand_pair.ff, host_system, construct_lambda_schedule(2), host_topology, temperature, pressure, dt, 10, 50, frame_filter=all_frames, ) conv_topo = conv_model.setup_topology(ligand) conv_ref = AbsoluteFreeEnergy(ligand, conv_topo) conv_unbound_potentials, conv_sys_params, _ = conv_ref.prepare_host_edge( ff_params, conv_model.host_system) assert len(conv_sys_params) == len(decouple_sys_params) seen_nonbonded = False for i, decouple_pot in enumerate(decouple_unbound_potentials): if not isinstance(decouple_pot, NonbondedInterpolated): continue seen_nonbonded = True conv_pot = conv_unbound_potentials[i] assert isinstance(conv_pot, NonbondedInterpolated) conv_nonbonded_params = conv_sys_params[i] decouple_nonbonded_params = decouple_sys_params[i] # Shapes of parameters # Conversion Leg [src_ligand, dest_ligand] # Decouple Leg [dest_blocker, dest_ligand, blocker_halved, ligand_halved] # Should have the same number of parameters besides the blocker. Since params are interpolated, multiply by 2 assert conv_nonbonded_params.shape[ 0] == decouple_nonbonded_params.shape[0] - blocker.GetNumAtoms( ) * 2 # Should both share the same number of parameters types assert conv_nonbonded_params.shape[ 1] == decouple_nonbonded_params.shape[1] conv_params = conv_nonbonded_params[num_host_atoms * 2:] decouple_params = decouple_nonbonded_params[num_host_atoms * 2:] assert conv_params.shape[ 0] == decouple_params.shape[0] - blocker.GetNumAtoms() * 2 # Verify the dest params of conv match the src params of decouple np.testing.assert_array_equal( conv_params[len(conv_params) // 2:], decouple_params[len(decouple_params) // 2 + blocker.GetNumAtoms():], ) assert seen_nonbonded, "Found no NonbondedInterpolated potential"