Research Articles
XGBoost vs. Random Forest for Caco-2 Permeability Prediction: A Comprehensive Comparison for Drug Discovery
Accurately predicting Caco-2 permeability is crucial for assessing intestinal absorption and oral bioavailability of drug candidates.
A Practical Guide to Selecting Machine Learning Algorithms for Predictive ADMET Modeling
This article provides a comprehensive framework for researchers, scientists, and drug development professionals to select and apply machine learning algorithms for predicting specific Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET)...
Bridging the Gap: A Practical Framework for Validating Computational ADMET Models with Experimental Data
This article provides a comprehensive guide for researchers and drug development professionals on the critical process of validating computational ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) models.
Beyond the Black Box: A Practical Guide to Diagnosing and Fixing Poor Performance in ADMET Models
Accurate prediction of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties is crucial for reducing late-stage drug attrition, yet models often suffer from poor generalization and reliability.
Optimizing Caco-2 Permeability with Molecular Pair Analysis: A Guide for Predictive ADMET Profiling
This article provides a comprehensive guide for researchers and drug development professionals on leveraging Matched Molecular Pair Analysis (MMPA) to optimize intestinal permeability predictions using the Caco-2 cell model.
Overcoming Data Scarcity in ADMET Prediction: Advanced ML Strategies for Novel Compounds
Accurately predicting the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) of novel compounds is crucial for drug development but remains challenging due to limited experimental data.
Navigating Instability: AI-Driven Strategies for Accurate Natural Product ADMET Prediction
This comprehensive review addresses the critical challenge of chemical instability in natural product ADMET prediction, a major bottleneck in drug discovery.
Beyond the Rule of Five: Optimizing Molecular Descriptors for Advanced Permeability Prediction in Drug Discovery
Accurately predicting molecular permeability is a critical challenge in drug discovery, especially for complex therapeutic modalities like cyclic peptides and heterobifunctional degraders that operate beyond traditional chemical space.
From Lab to Real World: 7 Data-Centric Strategies to Boost AI Model Transferability in Drug Development
This article provides a strategic roadmap for researchers and drug development professionals to enhance the performance and reliability of AI/ML models when applied to real-world industry data.
Beyond the Domain: Strategies to Overcome Applicability Domain Limitations in Modern QSAR Modeling
This article addresses the critical challenge of Applicability Domain (AD) limitations in Quantitative Structure-Activity Relationship (QSAR) models, a well-known constraint that confines model reliability to specific regions of chemical space.