Research Articles
Beyond Accuracy: A Practical Framework for Assessing Model Performance on Molecular Activity Cliffs
This article provides a comprehensive guide for researchers and drug development professionals on evaluating machine learning model performance in the presence of molecular activity cliffs—critical yet challenging phenomena in drug...
Molecular Representation Learning: A Systematic Comparison of Models, Applications, and Future Frontiers in Drug Discovery
This article provides a systematic comparison of molecular representation learning models, a cornerstone of AI-driven drug discovery.
Random Forest vs XGBoost vs LightGBM: A Comprehensive Benchmark for Molecular Property Prediction
This article provides a systematic comparison of three dominant machine learning algorithms—Random Forest, XGBoost, and LightGBM—for predicting molecular properties in pharmaceutical and chemical sciences.
Bridging the Digital-Experimental Divide: A Practical Guide to Validating Molecular Property Predictions
This article addresses the critical challenge of validating computational molecular property predictions against experimental data, a central task in modern drug discovery.
Beyond the Training Set: Evaluating and Enhancing Out-of-Distribution Robustness in Molecular Property Predictors
The accurate prediction of molecular properties for compounds outside a model's training distribution is a critical frontier in AI-driven drug discovery.
Mol2Vec vs. VICGAE: A Performance and Practicality Comparison for Molecular Property Prediction
This article provides a comprehensive comparison of two prominent molecular embedding techniques, Mol2Vec and VICGAE, for predicting key chemical properties.
The Ultimate Guide to Benchmarking Machine Learning Models on MoleculeNet Datasets (2025)
This article provides a comprehensive resource for researchers and drug development professionals on benchmarking machine learning models using the MoleculeNet ecosystem.
Graph Neural Networks vs. Molecular Fingerprints: A 2025 Benchmark for Molecular Property Prediction
Molecular property prediction is a cornerstone of modern drug discovery and materials science.
Beyond the Data Desert: Strategies for Comprehensive Chemical Space Coverage in AI-Driven Drug Discovery
This article addresses the critical challenge of limited chemical space coverage in training datasets for AI-driven drug discovery.
Navigating Activity Cliffs: Strategies to Enhance Molecular Property Prediction for Robust Drug Discovery
Activity cliffs (ACs), where minute structural changes cause significant potency shifts, present a major challenge for AI-driven molecular property prediction, often leading to model inaccuracies and unreliable guidance for drug...