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4900749 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | # Copyright 2025 Dhruv Nair. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
ProteinMPNN / LigandMPNN model wrapper.
A thin diffusers-compatible wrapper around the foundry MPNN model,
following the same pattern as the transformer and scheduler wrappers.
Reuses the foundry model implementation directly, adding only the
ModelMixin/ConfigMixin interface for diffusers integration.
"""
from dataclasses import dataclass
from typing import Optional
import torch
import torch.nn as nn
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin
from mpnn.model.mpnn import LigandMPNN, ProteinMPNN
MODEL_CLASSES = {
"protein_mpnn": ProteinMPNN,
"ligand_mpnn": LigandMPNN,
}
@dataclass
class MPNNModelOutput:
"""Output from the MPNN model wrapper."""
sequence_logits: torch.Tensor # [B, L, n_vocab]
sequence_indices: torch.Tensor # [B, L]
decoder_features: dict # full decoder output dict
class MPNNModel(ModelMixin, ConfigMixin):
"""
Diffusers-compatible wrapper around the foundry ProteinMPNN / LigandMPNN.
Wraps `mpnn.model.mpnn.ProteinMPNN` (or `LigandMPNN`) to provide a
diffusers ModelMixin/ConfigMixin interface. All model logic is delegated
to the foundry implementation.
State dict keys match the foundry checkpoint format via the `model.*`
prefix (stripped on load).
"""
config_name = "config.json"
@register_to_config
def __init__(
self,
model_type: str = "protein_mpnn",
hidden_dim: int = 128,
num_encoder_layers: int = 3,
num_decoder_layers: int = 3,
num_neighbors: int = 48,
dropout_rate: float = 0.1,
num_positional_embeddings: int = 16,
min_rbf_mean: float = 2.0,
max_rbf_mean: float = 22.0,
num_rbf: int = 16,
# LigandMPNN-specific
num_context_atoms: int = 25,
num_context_encoding_layers: int = 2,
):
super().__init__()
model_cls = MODEL_CLASSES.get(model_type)
if model_cls is None:
raise ValueError(
f"Unknown model_type '{model_type}'. "
f"Choose from: {list(MODEL_CLASSES.keys())}"
)
common_kwargs = dict(
num_node_features=hidden_dim,
num_edge_features=hidden_dim,
hidden_dim=hidden_dim,
num_encoder_layers=num_encoder_layers,
num_decoder_layers=num_decoder_layers,
num_neighbors=num_neighbors,
dropout_rate=dropout_rate,
num_positional_embeddings=num_positional_embeddings,
min_rbf_mean=min_rbf_mean,
max_rbf_mean=max_rbf_mean,
num_rbf=num_rbf,
)
if model_type == "ligand_mpnn":
common_kwargs["num_context_atoms"] = num_context_atoms
common_kwargs["num_context_encoding_layers"] = num_context_encoding_layers
self.model = model_cls(**common_kwargs)
def forward(
self,
X: torch.Tensor,
S: Optional[torch.Tensor] = None,
residue_mask: Optional[torch.Tensor] = None,
designed_residue_mask: Optional[torch.Tensor] = None,
chain_labels: Optional[torch.Tensor] = None,
R_idx: Optional[torch.Tensor] = None,
temperature: float = 0.1,
**kwargs,
) -> MPNNModelOutput:
"""
Run ProteinMPNN / LigandMPNN sequence design.
Args:
X: Backbone atom coordinates [B, L, num_atoms, 3].
For ProteinMPNN: num_atoms=4 (N, CA, C, O).
S: Ground-truth sequence tokens [B, L] (optional, for teacher forcing).
residue_mask: Valid residue mask [B, L] (default: all valid).
designed_residue_mask: Which residues to design [B, L] (default: all).
chain_labels: Chain identifiers [B, L] (default: single chain).
R_idx: Residue indices [B, L] (default: 0..L-1).
temperature: Sampling temperature (default: 0.1).
Returns:
MPNNModelOutput with sequence logits and sampled indices.
"""
B, L = X.shape[0], X.shape[1]
device = X.device
if S is None:
S = torch.zeros(B, L, dtype=torch.long, device=device)
if residue_mask is None:
residue_mask = torch.ones(B, L, dtype=torch.bool, device=device)
if designed_residue_mask is None:
designed_residue_mask = torch.ones(B, L, dtype=torch.bool, device=device)
if chain_labels is None:
chain_labels = torch.zeros(B, L, dtype=torch.long, device=device)
if R_idx is None:
R_idx = torch.arange(L, device=device).unsqueeze(0).expand(B, -1)
# Atom mask: mark all atoms as valid based on coordinate presence
X_m = (X.abs().sum(dim=-1) > 0).float() # [B, L, num_atoms]
network_input = {
"X": X,
"X_m": X_m,
"S": S,
"R_idx": R_idx,
"chain_labels": chain_labels,
"residue_mask": residue_mask,
"designed_residue_mask": designed_residue_mask,
"temperature": temperature,
**kwargs,
}
output = self.model(network_input)
logits = output["decoder_features"]["logits"] # [B, L, n_vocab]
S_sampled = output["decoder_features"].get(
"S_sampled", logits.argmax(dim=-1)
)
return MPNNModelOutput(
sequence_logits=logits,
sequence_indices=S_sampled,
decoder_features=output["decoder_features"],
)
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