diff --git a/alphafold/common/confidence.py b/alphafold/common/confidence.py
index e5ea99af0..da1cb08c5 100644
--- a/alphafold/common/confidence.py
+++ b/alphafold/common/confidence.py
@@ -168,7 +168,80 @@ def predicted_tm_score(logits, breaks, residue_weights = None,
 
   return (per_alignment * residue_weights).max()
 
-def get_confidence_metrics(prediction_result, mask, rank_by = "plddt", use_jnp=False):
+
+def predicted_tm_score_chain(logits, breaks, residue_weights = None,
+    asym_id = None, use_jnp=False, chain_num=None):
+  """Computes predicted the chain matrix of pTM scores.
+
+  Args:
+    logits: [num_res, num_res, num_bins] the logits output from
+      PredictedAlignedErrorHead.
+    breaks: [num_bins] the error bins.
+    residue_weights: [num_res] the per residue weights to use for the
+      expectation.
+    asym_id: [num_res] the asymmetric unit ID - the chain ID. Only needed for
+      ipTM calculation.
+    use_jnp: Whether to use JAX.
+    chain_num: The number of chains in the multimer model.
+
+
+  Returns:
+    iptm_matrix: The predicted TM alignment or the predicted iTM score.
+    predicted_tm_term: The predicted TM term for each bin.
+  """
+  if use_jnp:
+    _np, _softmax = jnp, jax.nn.softmax
+  else:
+    _np, _softmax = np, scipy.special.softmax
+
+  if chain_num is None:
+    chain_num = 1
+
+  # residue_weights has to be in [0, 1], but can be floating-point, i.e. the
+  # exp. resolved head's probability.
+  if residue_weights is None:
+    residue_weights = _np.ones(logits.shape[0])
+
+  bin_centers = _calculate_bin_centers(breaks, use_jnp=use_jnp)
+  num_res = residue_weights.shape[0]
+
+  # Clip num_res to avoid negative/undefined d0.
+  clipped_num_res = _np.maximum(residue_weights.sum(), 19)
+
+  # Compute d_0(num_res) as defined by TM-score, eqn. (5) in Yang & Skolnick
+  # "Scoring function for automated assessment of protein structure template
+  # quality", 2004: http://zhanglab.ccmb.med.umich.edu/papers/2004_3.pdf
+  d0 = 1.24 * (clipped_num_res - 15) ** (1./3) - 1.8
+
+  # Convert logits to probs.
+  probs = _softmax(logits, axis=-1)
+
+  # TM-Score term for every bin.
+  tm_per_bin = 1. / (1 + _np.square(bin_centers) / _np.square(d0))
+  # E_distances tm(distance).
+  predicted_tm_term = (probs * tm_per_bin).sum(-1)
+
+  # jax.debug.print('residue weights = {x}',x=residue_weights)
+  
+  def get_cross_iptm(i, j):
+    pair_mask = jnp.logical_and(i * jnp.ones((num_res))[:, None] == asym_id[None, :] , j*jnp.ones((num_res))[None, :] == asym_id[:, None])
+    chain_chain_predicted_tm_term = predicted_tm_term * pair_mask
+    pair_residue_weights = pair_mask * (residue_weights[None, :] * residue_weights[:, None])
+    normed_residue_mask = pair_residue_weights / (1e-8 + pair_residue_weights.sum(-1, keepdims=True))
+    per_alignment = (chain_chain_predicted_tm_term * normed_residue_mask).sum(-1)
+    return (per_alignment * residue_weights).max()
+
+  iptm_matrix_list = []
+
+  for i in jnp.arange(chain_num):
+      local_list = []
+      for j in jnp.arange(chain_num):
+          local_list.append(get_cross_iptm(i, j))
+      iptm_matrix_list.append(local_list)
+  
+  return(iptm_matrix_list, predicted_tm_term)
+
+def get_confidence_metrics(prediction_result, mask, rank_by = "plddt", use_jnp=False, chain_num=None):
   """Post processes prediction_result to get confidence metrics."""  
   confidence_metrics = {}
   plddt = compute_plddt(prediction_result['predicted_lddt']['logits'], use_jnp=use_jnp)
@@ -195,7 +268,14 @@ def get_confidence_metrics(prediction_result, mask, rank_by = "plddt", use_jnp=F
           residue_weights=mask,
           asym_id=prediction_result['predicted_aligned_error']['asym_id'],
           use_jnp=use_jnp)
-
+      confidence_metrics['chain_iptm'], confidence_metrics['ptm_matrix'] = predicted_tm_score_chain(
+          logits=prediction_result['predicted_aligned_error']['logits'],
+          breaks=prediction_result['predicted_aligned_error']['breaks'],
+          residue_weights=mask,
+          asym_id=prediction_result['predicted_aligned_error']['asym_id'],
+          use_jnp=use_jnp,
+          chain_num=chain_num,
+          )
   # compute mean_score
   if rank_by == "multimer":
     mean_score = 80 * confidence_metrics["iptm"] + 20 * confidence_metrics["ptm"]
diff --git a/alphafold/model/modules_multimer.py b/alphafold/model/modules_multimer.py
index 7cd8a6fd5..1e909c944 100644
--- a/alphafold/model/modules_multimer.py
+++ b/alphafold/model/modules_multimer.py
@@ -461,12 +461,16 @@ def apply_network(prev, safe_key):
     if not return_representations:
       del ret['representations']
 
+    # Extract chain NUM
+    chain_num = c.embeddings_and_evoformer.max_relative_chain + 1
+
     # add confidence metrics
     ret.update(confidence.get_confidence_metrics(
       prediction_result=ret,
       mask=batch["seq_mask"],
       rank_by=self.config.rank_by,
-      use_jnp=True))
+      use_jnp=True,
+      chain_num=chain_num))
 
     ret["tol"] = confidence.compute_tol(
       prev["prev_pos"],