diff --git a/src/alphafold3/jax/geometry/utils.py b/src/alphafold3/jax/geometry/utils.py
index 7f2fc66..1a29c62 100644
--- a/src/alphafold3/jax/geometry/utils.py
+++ b/src/alphafold3/jax/geometry/utils.py
@@ -13,72 +13,9 @@
 from collections.abc import Iterable
 import numbers
 
-import jax
-from jax import lax
 import jax.numpy as jnp
 
 
-def safe_select(condition, true_fn, false_fn):
-  """Safe version of selection (i.e. `where`).
-
-  This applies the double-where trick.
-  Like jnp.where, this function will still execute both branches and is
-  expected to be more lightweight than lax.cond.  Other than NaN-semantics,
-  safe_select(condition, true_fn, false_fn) is equivalent to
-
-    jax.tree.map(lambda x, y: jnp.where(condition, x, y),
-                 true_fn(),
-                 false_fn()),
-
-  Compared to the naive implementation above, safe_select provides the
-  following guarantee: in either the forward or backward pass, a NaN produced
-  *during the execution of true_fn()* will not propagate to the rest of the
-  computation and similarly for false_fn.  It is very important to note that
-  while true_fn and false_fn will typically close over other tensors (i.e. they
-  use values computed prior to the safe_select function), there is no NaN-safety
-  for the backward pass of closed over values.  It is important than any NaN's
-  are produced within the branch functions and not before them.  For example,
-
-    safe_select(x < eps, lambda: 0., lambda: jnp.sqrt(x))
-
-  will not produce NaN on the backward pass even if x == 0. since sqrt happens
-  within the false_fn, but the very similar
-
-    y = jnp.sqrt(x)
-    safe_select(x < eps, lambda: 0., lambda: y)
-
-  will produce a NaN on the backward pass if x == 0 because the sqrt happens
-  prior to the false_fn.
-
-  Args:
-    condition: Boolean array to use in where
-    true_fn: Zero-argument function to construct the values used in the True
-      condition.  Tensors that this function closes over will be extracted
-      automatically to implement the double-where trick to suppress spurious NaN
-      propagation.
-    false_fn: False branch equivalent of true_fn
-
-  Returns:
-    Resulting PyTree equivalent to tree_map line above.
-  """
-  true_fn, true_args = jax.closure_convert(true_fn)
-  false_fn, false_args = jax.closure_convert(false_fn)
-
-  true_args = jax.tree.map(
-      lambda x: jnp.where(condition, x, lax.stop_gradient(x)), true_args
-  )
-
-  false_args = jax.tree.map(
-      lambda x: jnp.where(condition, lax.stop_gradient(x), x), false_args
-  )
-
-  return jax.tree.map(
-      lambda x, y: jnp.where(condition, x, y),
-      true_fn(*true_args),
-      false_fn(*false_args),
-  )
-
-
 def unstack(value: jnp.ndarray, axis: int = -1) -> list[jnp.ndarray]:
   return [
       jnp.squeeze(v, axis=axis)
@@ -93,18 +30,6 @@ def angdiff(alpha: jnp.ndarray, beta: jnp.ndarray) -> jnp.ndarray:
   return d
 
 
-def safe_arctan2(
-    x1: jnp.ndarray, x2: jnp.ndarray, eps: float = 1e-8
-) -> jnp.ndarray:
-  """Safe version of arctan2 that avoids NaN gradients when x1=x2=0."""
-
-  return safe_select(
-      jnp.abs(x1) + jnp.abs(x2) < eps,
-      lambda: jnp.zeros_like(jnp.arctan2(x1, x2)),
-      lambda: jnp.arctan2(x1, x2),
-  )
-
-
 def weighted_mean(
     *,
     weights: jnp.ndarray,