Research Articles
Comparative Effectiveness Research in Pharmaceuticals: A Guide to Methods, Applications, and Future Directions
This article provides a comprehensive overview of Comparative Effectiveness Research (CER) in the pharmaceutical sector, tailored for researchers, scientists, and drug development professionals.
Ensuring Model Robustness in Drug Discovery: A Comprehensive Guide to Y-Randomization and Applicability Domain Analysis
This article provides a comprehensive guide for researchers, scientists, and drug development professionals on the critical practices of assessing model robustness using Y-randomization and Applicability Domain (AD) analysis.
Validating CYP450 Inhibition Models: From Deep Learning Advances to Clinical Application
This comprehensive review examines current methodologies and challenges in validating computational models for predicting human cytochrome P450 inhibition, a critical factor in drug safety assessment.
A Practical Guide to ADMET Prediction: Choosing the Right Evaluation Metrics for Classification and Regression Models
This article provides a comprehensive framework for evaluating machine learning models that predict Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties.
Natural vs. Synthetic Compounds: A Data-Driven Analysis of Predictive Accuracy in Drug Discovery
This article provides a comprehensive analysis for researchers and drug development professionals on the predictive accuracy of computational models for natural versus synthetic compounds.
Molecular Representations for ADMET Prediction: A Comparative Analysis of Traditional and AI-Driven Approaches
Accurate prediction of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties is crucial for reducing late-stage failures in drug discovery.
Bridging the Prediction Gap: A Practical Guide to Validating In Silico ADMET Models with In Vitro Data
This article provides a comprehensive framework for researchers and drug development professionals to effectively validate in silico ADMET predictions with robust in vitro data.
Benchmarking Open Access vs. Commercial ADMET Tools: A 2025 Guide for Drug Development
This article provides a comprehensive, evidence-based benchmark of open-access and commercial ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction tools for researchers and drug development professionals.
Industrial Validation of Machine Learning Models for ADMET Prediction: Strategies, Challenges, and Best Practices
This article provides a comprehensive guide for researchers and drug development professionals on validating machine learning (ML) models for industrial ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction.
Deep Learning vs. Traditional QSAR: A Performance Evaluation for Modern Drug Discovery
The integration of artificial intelligence, particularly deep learning (DL), is revolutionizing Quantitative Structure-Activity Relationship (QSAR) modeling in drug discovery.