About This Architecture

Drug-protein interaction prediction pipeline combining MolBERT and ESM encoders to transform molecular SMILES and amino acid sequences into embedding vectors. Drug and protein embeddings flow through a feature fusion layer using concatenation and attention mechanisms, then feed into an XGBoost classifier for binary interaction prediction. The architecture integrates a feature store for embedding persistence, training pipeline for model retraining, and model registry for version control, with monitoring and logging throughout the inference path. Fork this diagram to customize encoder architectures, fusion strategies, or classification models for your computational biology workflow. This pattern demonstrates best practices for production ML in drug discovery, balancing model accuracy with inference latency and reproducibility.

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How do you build a production machine learning pipeline for predicting drug-protein interactions?

This diagram shows a complete drug-protein interaction prediction system using MolBERT to encode drug SMILES strings and ESM to encode protein amino acid sequences into embedding vectors. These embeddings are fused via concatenation and attention, then classified by XGBoost, with feature store, model registry, monitoring, and logging ensuring production reliability.