graph LR
Data_Pipeline["Data Pipeline"]
Model_Architecture["Model Architecture"]
Training_Evaluation_Engine["Training & Evaluation Engine"]
Prediction_Feature_Extraction["Prediction & Feature Extraction"]
Structural_Analysis_Scoring["Structural Analysis & Scoring"]
Data_Pipeline -- "provides processed data loaders to" --> Training_Evaluation_Engine
Data_Pipeline -- "provides processed data loaders to" --> Prediction_Feature_Extraction
Model_Architecture -- "provides instantiated model objects to" --> Training_Evaluation_Engine
Model_Architecture -- "provides instantiated model objects to" --> Prediction_Feature_Extraction
Training_Evaluation_Engine -- "utilizes for inference during evaluation" --> Prediction_Feature_Extraction
Training_Evaluation_Engine -- "utilizes for performance assessment" --> Structural_Analysis_Scoring
Prediction_Feature_Extraction -- "provides extracted features to" --> Structural_Analysis_Scoring
Structural_Analysis_Scoring -- "provides similarity scores and results back to" --> Training_Evaluation_Engine
click Data_Pipeline href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/DeeplyTough/Data_Pipeline.md" "Details"
click Model_Architecture href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/DeeplyTough/Model_Architecture.md" "Details"
click Training_Evaluation_Engine href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/DeeplyTough/Training_Evaluation_Engine.md" "Details"
The DeeplyTough project, a research-oriented deep learning application in computational structural biology and cheminformatics, exhibits a modular and pipeline-driven architecture. The core components are designed to handle the entire workflow from raw data ingestion to model training, prediction, and structural analysis.
Data Pipeline [Expand]
This component is responsible for the entire data lifecycle, from ingesting raw biological datasets (e.g., PDB files, pocket definitions) and performing initial preprocessing (e.g., HTMD featurization) to transforming data into a voxelized format, applying augmentations, and generating efficient data loaders. It ensures that data is consistently prepared and accessible for both model training and inference.
Related Classes/Methods:
deeplytough.datasets(1:1)deeplytough.engine.datasets(1:1)
Model Architecture [Expand]
This component defines and constructs the deep learning neural network architectures, primarily focusing on SE(3)-equivariant networks. It encapsulates the logic for building complex 3D convolutional layers and handling model configuration, ensuring that the neural network structure is flexible and adaptable to various tasks.
Related Classes/Methods:
Training & Evaluation Engine [Expand]
This is the central orchestrator for the model's learning process. It manages the training loop, including optimizer configuration, learning rate scheduling, loss computation, logging metrics (e.g., to TensorBoard), and saving/resuming model checkpoints. It also initiates and coordinates benchmark evaluations to assess model performance against established datasets.
Related Classes/Methods:
deeplytough.scripts.train(1:1)deeplytough.scripts.prospeccts_benchmark(1:1)deeplytough.scripts.toughm1_benchmark(1:1)deeplytough.scripts.vertex(1:1)
This component handles the inference phase of the deep learning models. It is responsible for loading trained models from checkpoints, performing forward passes on new input data, and extracting high-dimensional feature vectors (descriptors) from specific points of interest within the structural data. It acts as the primary interface for applying trained models to new inputs.
Related Classes/Methods:
This component implements various algorithms for comparing and scoring the similarity between structural features (descriptors) that have been extracted by the Prediction & Feature Extraction component. It provides different matching strategies (e.g., pairwise, complete, bipartite) and calculates similarity scores, which are then used for quantifying model performance and facilitating structural comparisons.
Related Classes/Methods:
deeplytough.matchers(1:1)