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42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
import torch
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from rf2aa.data.data_loader import RawInputData
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from rf2aa.data.data_loader_utils import blank_template
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from rf2aa.data.parsers import parse_mol
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from rf2aa.kinematics import get_chirals
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from rf2aa.util import get_bond_feats, get_nxgraph, get_atom_frames
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def load_small_molecule(input_file, input_type, model_runner):
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if input_type == "smiles":
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is_string = True
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else:
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is_string = False
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obmol, msa, ins, xyz, mask = parse_mol(
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input_file, filetype=input_type, string=is_string, generate_conformer=True
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)
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return compute_features_from_obmol(obmol, msa, xyz, model_runner)
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def compute_features_from_obmol(obmol, msa, xyz, model_runner):
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L = msa.shape[0]
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ins = torch.zeros_like(msa)
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bond_feats = get_bond_feats(obmol)
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xyz_t, t1d, mask_t, _ = blank_template(
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model_runner.config.loader_params.n_templ,
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L,
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deterministic=model_runner.deterministic,
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)
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chirals = get_chirals(obmol, xyz[0])
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G = get_nxgraph(obmol)
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atom_frames = get_atom_frames(msa, G)
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msa, ins = msa[None], ins[None]
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return RawInputData(
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msa, ins, bond_feats, xyz_t, mask_t, t1d, chirals, atom_frames, taxids=None
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)
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def remove_leaving_atoms(input, is_leaving):
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keep = ~is_leaving
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return input.keep_features(keep)
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