Deep Learning Model Assembly

---
name: deep-learning-model-assembly
description: Use when you have transformer encoder components and a prediction head
  specification, and need to wire them into a single trainable model that maps molecular
  structure inputs (SMILES, molecular graphs, or feature vectors) to scalar or vector
  molecular property predictions.
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_0337
  edam_topics:
  - http://edamontology.org/topic_3474
  - http://edamontology.org/topic_0154
  tools:
  - PyTorch
  - Transformers (Hugging Face)
  - RDKit
  - HyperCCS repository
  techniques:
  - ion-mobility-MS
  license_tier: open
  provenance_tier: literature
derived_from:
- doi: 10.1021/acs.analchem.5c03492
  title: HyperCCS
evidence_spans: []
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_hyperccs_cq
    doi: 10.1021/acs.analchem.5c03492
    title: HyperCCS
  dedup_kept_from: coll_hyperccs_cq
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.1021/acs.analchem.5c03492
  all_source_dois:
  - 10.1021/acs.analchem.5c03492
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# deep-learning-model-assembly

## Summary

Assemble a complete transformer-based deep learning architecture by composing a molecular encoder, transformer layers, and prediction head to predict molecular properties (e.g., Collision Cross Section) from SMILES or molecular graph inputs. This skill validates the forward pass and output shape to ensure the assembled model is correctly wired and produces predictions in the expected range.

## When to use

You have transformer encoder components and a prediction head specification, and need to wire them into a single trainable model that maps molecular structure inputs (SMILES, molecular graphs, or feature vectors) to scalar or vector molecular property predictions. Apply this skill when building or reconstructing a multimodal deep learning pipeline where architecture assembly and forward-pass validation are prerequisites to training or inference.

## When NOT to use

- Architecture components are not yet implemented or specified — design and implement them first.
- Input molecular data is not yet preprocessed into SMILES, graphs, or feature vectors.
- You only have a pre-trained checkpoint and do not need to modify or reconstruct the architecture.

## Inputs

- Transformer architecture specification (number of layers, embedding dimension, attention heads)
- Molecular encoder implementation (SMILES tokenizer or molecular graph encoder)
- Prediction head specification (output dimensions and activation functions)
- Example molecular structures (SMILES strings or molecular feature vectors)
- Expected output range or reference CCS values for validation

## Outputs

- Assembled PyTorch or TensorFlow model object with forward() method
- Forward-pass validation report (output shape, value ranges, absence of NaN/Inf)
- Model checkpoint or state_dict ready for training or inference

## How to apply

Load the model architecture specification and components from the source repository (e.g., HyperCCS). Define the transformer encoder to process molecular structure information as tokenized SMILES or feature vectors, specifying embedding dimension (e.g., 768), number of layers (e.g., 12), and attention heads (e.g., 12). Implement or retrieve the prediction head that outputs scalar CCS values from transformer embeddings. Assemble the complete model by connecting the molecular encoder → transformer layers → prediction head in sequence. Validate the model's forward pass by loading example molecular structures, checking that output shapes match the target dimensionality (e.g., scalar for CCS) and that predicted values fall within expected ranges (e.g., CCS values in Ångström² units). Use PyTorch's model.forward() or a test batch to confirm the wiring is correct before training.

## Related tools

- **PyTorch** (Framework for implementing and assembling transformer layers, encoders, and prediction heads; used to wire components and execute forward passes.)
- **Transformers (Hugging Face)** (Provides pre-built transformer encoder blocks and tokenization utilities for molecular SMILES input processing.)
- **RDKit** (Molecular structure parsing and graph representation for encoding molecular inputs as feature vectors or graphs.)
- **HyperCCS repository** (Reference implementation providing model architecture code, pre-trained MoLFormer checkpoints, and validation examples.) — https://github.com/NeoNexusX/HyperCCS

## Examples

```
# After cloning the HyperCCS repository and installing dependencies:
import torch
from model.layers.main_layer import HyperCCS

# Load model with architecture parameters
model = HyperCCS(n_head=12, n_layer=12, n_embd=768, adduct_num=3, ecfp_num=1024)

# Validate forward pass with example input
example_smiles_batch = torch.randint(0, 100, (4, 50))  # [batch_size, seq_len]
output = model(example_smiles_batch)
print(f"Output shape: {output.shape}, Expected: [4, 1]")  # Scalar CCS per molecule
assert output.shape == torch.Size([4, 1]), "Output shape mismatch"
assert not torch.isnan(output).any(), "NaN detected in output"
```

## Evaluation signals

- Forward pass executes without errors and produces tensor output with shape matching the prediction target (e.g., [batch_size, 1] for scalar CCS).
- Output values are numeric (no NaN, Inf, or undefined values) and fall within expected molecular property ranges (e.g., CCS values > 0).
- Model parameters are correctly initialized and gradients flow through all components (backprop test on a small batch).
- Example molecular structures (SMILES or feature vectors) produce consistent and realistic CCS predictions when compared to reference or literature values.
- Model state_dict contains expected keys for encoder, transformer layers, and prediction head with correct tensor dimensions.

## Limitations

- Assembly assumes individual components (encoder, transformer, head) are already correctly implemented; errors in any component will propagate through the forward pass.
- Validation with example molecules requires representative test data; sparse or non-representative validation may miss systematic errors in certain input domains.
- Output range expectations depend on dataset-specific normalization; CCS predictions must be checked against the training data scale and any applied transformations (e.g., log or z-score normalization).
- The skill does not include training, fine-tuning, or hyperparameter optimization; assembly only confirms structural correctness, not model quality or convergence.

## Evidence

- [other] Implement the CCS prediction head that outputs scalar CCS values from the transformer embeddings.: "Implement the CCS prediction head that outputs scalar CCS values from the transformer embeddings."
- [other] Assemble the complete HyperCCS model architecture by connecting the molecular encoder, transformer layers, and prediction head.: "Assemble the complete HyperCCS model architecture by connecting the molecular encoder, transformer layers, and prediction head."
- [other] Validate the model forward pass by loading example molecular structures and confirming output shape and value ranges match expected CCS predictions.: "Validate the model forward pass by loading example molecular structures and confirming output shape and value ranges match expected CCS predictions."
- [readme] The model uses a transformer-based architecture with: SMILES tokenization for molecular representation, Attention mechanisms for capturing molecular structure.: "The model uses a transformer-based architecture with: SMILES tokenization for molecular representation, Attention mechanisms for capturing molecular structure."
- [readme] Model Architecture Parameters: --n_head: Number of attention heads (default: 12), --n_layer: Number of transformer layers (default: 12), --n_embd: Embedding dimension (default: 768).: "Model Architecture Parameters: --n_head: Number of attention heads (default: 12), --n_layer: Number of transformer layers (default: 12), --n_embd: Embedding dimension (default: 768)."