Research Articles
Benchmarking Graph Neural Networks for PPI Prediction: A Comprehensive Guide for Biomedical Researchers
Protein-protein interactions (PPIs) form the cornerstone of cellular function and are critical targets for therapeutic intervention.
Benchmarking OOD Detection for Protein Sequences: Methods, Applications, and Clinical Implications
Out-of-distribution (OOD) detection is critical for ensuring the reliability and safety of machine learning models in protein science, particularly in high-stakes applications like drug discovery and functional annotation.
Beyond the Training Set: A Comprehensive Guide to Benchmarking OOD Generalization for Chemical-Protein Interaction Prediction
Accurate prediction of chemical-protein interactions (CPI) is fundamental to drug discovery, yet models often fail when applied to novel chemical or protein spaces (out-of-distribution, OOD).
Bayesian Optimization in Protein Engineering: Maximizing Discovery with Limited Experimental Budgets
This article provides a comprehensive guide for researchers and drug development professionals on implementing Bayesian optimization (BO) for protein engineering under stringent experimental constraints.
Bayesian Optimization for Protein Model Hyperparameter Tuning: A Guide for Biomedical AI Researchers
This article provides a comprehensive guide to applying Bayesian optimization (BO) for hyperparameter tuning in protein structure and function prediction models.
Bayesian Learning in Protein Engineering: A Complete Guide to Sequence-Function Mapping for Researchers
This comprehensive guide explores Bayesian learning as a transformative framework for mapping protein sequence to function.
Bayesian Flow Networks: Revolutionizing Protein Sequence Design for AI-Driven Drug Discovery
This comprehensive guide explores Bayesian Flow Networks (BFNs) as a groundbreaking framework for generative modeling of protein sequences.
The Prediction Paradox: Strategies for Balancing Speed and Accuracy in Protein & Molecular Structure Prediction
This article provides a comprehensive analysis of the critical trade-off between speed and accuracy in computational structure prediction for biomedical research.
Navigating the Exploration-Reliability Trade-Off: Modern Strategies for Protein Sequence Design in Drug Discovery
This article provides a comprehensive guide for researchers and drug development professionals on navigating the critical balance between exploring novel protein sequences and ensuring their functional reliability.
BLOSUM62 vs One-Hot Encoding: Performance Showdown for Protein Sequence Modeling in Drug Discovery
For computational biologists and drug developers building predictive models from protein sequences, a fundamental choice is representation: biologically-informed substitution matrices like BLOSUM62 or simple, position-agnostic one-hot encoding.