Structure confidence heads

Linear pLDDT Head

EasyRaw PyTorchAbout 10 min1 public example plus private tests

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Your task

Produce one unnormalized score per pLDDT local-confidence bin for each atom slot of each token. Higher logits favor their corresponding bins; this head returns those scores. Project the supplied feature vector directly, with bias, to atom/bin logits.

Function signature

plddt_linear_head(
    activations: dict,
    parameters: dict,
    masks: dict,
) -> torch.Tensor

Worked example

One atom, two confidence bins

For one valid token and atom, let single=[1,3], W_plddt[:,0,:]=[[1,2],[3,-1]], and b_plddt[0,:]=[0.5,-0.5]; both token and atom masks are true. The two returned logits are 1·1 + 3·3 + 0.5 = 10.5 and 1·2 + 3·(-1) - 0.5 = -1.5. These are unnormalized bin scores with output shape (1,1,1,2); confidence reduction happens separately.

Parameters

Parameter	Shape and meaning
`activations`dict	Supplied intermediate tensors.
`parameters`dict	Supplied model parameters, keyed as shown below.
`masks`dict	Supplied masks. Each field below defines what True includes or permits.

Return

torch.Tensor · $[B, N, A, L]$

Return Predicted logits in atom-slot/bin order.

Required behavior

single is $[B,N,Cs]$ ; token_mask is $[B,N]$ , atom_mask is $[B,N,A]$ , and True means present. Sanitize invalid single-token payloads before the learned head.
Use the masked single features directly: no LayerNorm, hidden projection, or activation.
Project hidden features with $W_{plddt}:[C_s,A,L]$ and add $b_{plddt}:[A,L]$ . Return $[B,N,A,L]$ in bin-logit order, without softmax or pLDDT scaling.
Zero every output logit whose token is absent or atom slot is absent, including after adding biases. Supply all parameters; do not initialize new layers.

Why this matters

Different heads answer different questions: pLDDT describes local accuracy, a distogram describes distance, and PAE describes error after alignment in a particular residue’s frame. Their probabilities feed separate summaries; they are not interchangeable confidence scales.

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 plddt_linear_head(
    activations: dict,
    parameters: dict,
    masks: dict,
) -> torch.Tensor:
    """Return a tensor with shape (B, N, A, L)."""
    # Implement the operation. Inputs and test setup are supplied by BioTorch.
    raise NotImplementedError("Implement plddt_linear_head")

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3 progressive hints, a solution walkthrough, and grading that checks values, shapes and gradients are waiting in the workspace.

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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.