Research Articles
Building Confidence in Computational Models: A Fit-for-Purpose Framework for Biomedical Research
This article provides a comprehensive guide for researchers and drug development professionals on establishing confidence in computational models, from early discovery to clinical application.
Verification and Validation in Computational Modeling: A Guide for Credible Drug Development
This article provides a comprehensive guide to Verification, Validation, and Uncertainty Quantification (VVUQ) in computational modeling for biomedical research and drug development.
Generative AI vs. Active Learning: A Strategic Comparison for Accelerating Drug Discovery
This article provides a comprehensive comparison of Generative AI and Active Learning, two pivotal machine learning paradigms transforming pharmaceutical R&D.
Assessing Active Learning Model Generalization: Strategies and Benchmarks for Biomedical and Drug Development
This article provides a comprehensive framework for assessing the generalization capabilities of active learning (AL) models, a critical challenge for their reliable application in data-scarce domains like drug development.
Active Learning Query Strategies: A Performance Comparison for Accelerating Drug Discovery
This article provides a comprehensive performance comparison of active learning (AL) query strategies, tailored for researchers and professionals in drug development.
Active Learning for Molecular Optimization: Strategies, Applications, and Benchmarking in Drug Discovery
This article provides a comprehensive evaluation of active learning (AL) as a transformative machine learning strategy for accelerating molecular optimization in drug discovery.
Beyond Exhaustive Screening: How Active Learning Delivers Unprecedented Efficiency Gains in Research and Drug Discovery
This article explores the paradigm shift from exhaustive, manual screening to AI-driven active learning (AL) across scientific research and drug discovery.
Beyond Tanimoto: The Evolution of Active Learning and Molecular Similarity in Modern Drug Discovery
This article explores the transformative integration of active learning with advanced molecular similarity metrics, moving beyond traditional Tanimoto coefficients to accelerate drug discovery.
Validating Active Learning FEP+ Predictions: A Framework for Accelerating Drug Discovery
This article provides a comprehensive framework for the validation of Active Learning (AL) driven Free Energy Perturbation (FEP+) predictions in drug discovery.
Benchmarking Active Learning for Drug Discovery: A Comprehensive Review of Strategies, Applications, and Performance
Active learning (AL) is transforming drug discovery by enabling more efficient and cost-effective experimentation.