Research Articles
Hyperparameter Optimization for Drug Discovery ML Models: Methods, Applications, and Best Practices
This article provides a comprehensive guide to hyperparameter optimization (HPO) for machine learning (ML) models in drug discovery.
Parallel Hyperparameter Optimization for Chemical Models: Accelerating Drug Discovery and Materials Development
This article provides a comprehensive guide to parallel hyperparameter optimization (HPO) for chemical and molecular property prediction models.
Advanced Neural Network Architectures for Molecular Property Prediction: A 2025 Guide for Drug Discovery
This article provides a comprehensive guide for researchers and drug development professionals on tuning neural network architectures for molecular property prediction (MPP).
Automated Hyperparameter Tuning for Small Chemical Datasets: A Practical Guide for Research Scientists
This article provides a comprehensive guide for researchers and drug development professionals tackling the challenge of applying machine learning to small chemical datasets.
Optimizing Chemistry ML: A Comprehensive Guide to Hyperparameter Tuning with Optuna
This guide provides chemistry researchers and drug development professionals with a comprehensive framework for integrating Optuna into their machine learning workflows.
Optimizing Chemical Machine Learning with Keras Tuner: A Guide for Drug Discovery and Molecular Property Prediction
This article provides a comprehensive guide for researchers and scientists in drug development on leveraging Keras Tuner for hyperparameter optimization of deep learning models in chemical machine learning.
Hyperband for Chemistry: A Practical Guide to Faster, More Accurate Deep Learning Models in Drug Discovery and Materials Science
This article provides a comprehensive guide to the Hyperband algorithm for hyperparameter optimization of deep learning models in chemistry and drug discovery.
Hyperparameter Tuning for Materials Science Machine Learning: A Practical Guide for Researchers
This article provides a comprehensive guide to hyperparameter tuning, tailored for researchers and professionals in materials science and drug development.
Bayesian Optimization in Molecular Discovery: A Guide to Data-Efficient Property Prediction and Drug Design
This article provides a comprehensive overview of Bayesian optimization (BO) for molecular property prediction, a powerful machine learning framework that is transforming data-efficient drug and materials discovery.
Hyperparameter Optimization in QSAR Models: A Guide for Robust and Predictive Drug Discovery
Hyperparameter tuning is a critical, yet often overlooked, step in developing reliable Quantitative Structure-Activity Relationship (QSAR) models.