flax_model/alphafold3/model/scoring/scoring.py

"""Library of scoring methods of the model outputs."""

from flax_model.alphafold3.model import protein_data_processing
import jax.numpy as jnp
import numpy as np


Array = jnp.ndarray | np.ndarray


def pseudo_beta_fn(
    aatype: Array,
    dense_atom_positions: Array,
    dense_atom_masks: Array,
    is_ligand: Array | None = None,
    use_jax: bool | None = True,
) -> tuple[Array, Array] | Array:
  """Create pseudo beta atom positions and optionally mask.

  Args:
    aatype: [num_res] amino acid types.
    dense_atom_positions: [num_res, NUM_DENSE, 3] vector of all atom positions.
    dense_atom_masks: [num_res, NUM_DENSE] mask.
    is_ligand: [num_res] flag if something is a ligand.
    use_jax: whether to use jax for the computations.

  Returns:
    Pseudo beta dense atom positions and the corresponding mask.
  """
  if use_jax:
    xnp = jnp
  else:
    xnp = np

  if is_ligand is None:
    is_ligand = xnp.zeros_like(aatype)

  pseudobeta_index_polymer = xnp.take(
      protein_data_processing.RESTYPE_PSEUDOBETA_INDEX, aatype, axis=0
  ).astype(xnp.int32)

  pseudobeta_index = xnp.where(
      is_ligand,
      xnp.zeros_like(pseudobeta_index_polymer),
      pseudobeta_index_polymer,
  )

  pseudo_beta = xnp.take_along_axis(
      dense_atom_positions, pseudobeta_index[..., None, None], axis=-2
  )
  pseudo_beta = xnp.squeeze(pseudo_beta, axis=-2)

  pseudo_beta_mask = xnp.take_along_axis(
      dense_atom_masks, pseudobeta_index[..., None], axis=-1
  ).astype(xnp.float32)
  pseudo_beta_mask = xnp.squeeze(pseudo_beta_mask, axis=-1)

  return pseudo_beta, pseudo_beta_mask