import caffe
caffe.set_mode_gpu()
import caffe_util
import generate
model_file = 'models/rvl-le13_24_0.5_3_3_32_2_1024_e.model'
model_name = 'abgan_rvl-le13_24_0.5_3_3_32_2_1024_e_disc2'
weights_file = '{}/{}.lowrmsd.0.0_gen_iter_20000.caffemodel'.format(
model_name, model_name)
data_root = '/home/mtr22/PDBbind/refined-set/'
targets = ['2avo', '2pwc']
lig_files = [data_root + '{}/{}_min.sdf'.format(t, t) for t in targets]
rec_files = [data_root + '{}/{}_rec.pdb'.format(t, t) for t in targets]
centers = [generate.get_center_from_sdf_file(l) for l in lig_files]
data_file = generate.get_temp_data_file(zip(rec_files, lig_files))
net_param = caffe_util.NetParameter.from_prototxt(model_file)
net_param.set_molgrid_data_source(data_file, '')
channels = generate.channel_info.get_default_channels(False, True, True)
net = caffe_util.Net.from_param(net_param, weights_file, phase=caffe.TEST)
latent_blobs = dict(rec=net.blobs['rec_latent_fc'],
lig=net.blobs['lig_latent_sample'])
net.forward(end='latent_concat')
net.blobs['lig_latent_std'] = 0.0
net.forward(start='lig_latent_noise')
latent_vecs = defaultdict(dict)
loss = ''
iter_ = 50
name = '{}{}{}'.format(encode, n_latent, loss)
model_file = 'models/{}-le13_24_0.5_2_1l_8_1_{}_{}.model'.format(
encode, n_latent, loss)
model_name = 'adam2_2_2__0.01_{}-le13_24_0.5_2_1l_8_1_{}_{}_d11_24_2_1l_16_1_x'.format(
encode, n_latent, loss)
data_root = '/net/pulsar/home/koes/dkoes/PDBbind/refined-set/'
rec_file = data_root + '1ai5/1ai5_rec.pdb'
lig_file = data_root + '1ai5/1ai5_min.sdf'
lig_mol = g.get_mols_from_sdf_file(lig_file)[0]
lig_mol.removeh()
center = g.get_mol_center(lig_mol)
data_file = g.get_temp_data_file([(rec_file, lig_file)])
channels = g.atom_types.get_default_channels(rec=False,
lig=True,
use_covalent_radius=True)
weights_file = '{}/{}.1ai5.0.all_gen_iter_{}000.caffemodel'.format(
model_name, model_name, iter_)
net_param = caffe_util.NetParameter.from_prototxt(model_file)
net_param.set_molgrid_data_source(data_file, '')
data_param = net_param.get_molgrid_data_param(caffe.TEST)
data_param.random_rotation = True
net = caffe_util.Net.from_param(net_param, weights_file, phase=caffe.TEST)
latent_name = '{}_latent_fc'.format(dict(r='rec', l='lig')[encode])
after_latent_name = 'lig_latent_defc'