Structure confidence heads

TM Ranking Score

EasyRaw PyTorchAbout 10 min3 public examples plus private tests

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Your task

Combine supplied confidence summaries into one ranking score per structure.

Function signature

tm_ranking_score(
    ptm: torch.Tensor,
    iptm: torch.Tensor,
    ranking_mix: float = 0.8,
) -> torch.Tensor

Worked example

Mix two confidence scores

With ptm=[0.5], iptm=[0.75], and ranking_mix=0.8, return ranking_score=[0.2·0.5+0.8·0.75]=[0.7]. This focused lesson combines supplied scores; it has no interface fallback or clash penalty.

Parameters

Parameter	Shape and meaning
`ptm`torch.Tensor	$[B]$ Supplied predicted TM score.
`iptm`torch.Tensor	$[B]$ Supplied interface TM score.
`ranking_mix`floatDefault `0.8`	Weight of the interface-TM term in mixed ranking.

Return

torch.Tensor · $[B]$

Return Combined ranking score per structure.

Required behavior

Use $(1-mix)\,pTM+mix\,ipTM$ , where ranking_mix defaults to 0.8 and lies in $[0,1]$ . Scores are supplied batch vectors; this lesson does not recompute TM probabilities.
Return this mixture directly, including for single-chain inputs.
Use CPU float32/float64 tensors with one shared floating dtype/device. Preserve strided layouts and do not mutate supplied tensors.
Use raw tensor operations, not a ready-made implementation of the requested primitive. Supplied parameters and random variates must not be reinitialized.

Why this matters

The usual multimer TM ranking mixture emphasizes interface confidence while retaining a smaller contribution from global fold confidence. This lesson combines already-computed scores; it is not the full AF3 clash/disorder ranking rule.

Starter code

You write the body of this function in the BioTorch workspace. Your code runs in an isolated PyTorch sandbox; Run checks the public examples and Submit grades the full test suite, including gradients.

import torch


def tm_ranking_score(
    ptm: torch.Tensor,  # (B,)
    iptm: torch.Tensor,  # (B,)
    ranking_mix: float = 0.8,
) -> torch.Tensor:
    """Return a tensor with shape (B,)."""
    # Implement the operation. Inputs and test setup are supplied by BioTorch.
    raise NotImplementedError("Implement tm_ranking_score")

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Related exercises

Sources and further reading

A primitive exercise, not an end-to-end reproduction of the named models. No affiliation or endorsement is implied.