Research Articles
Validating Nucleation Models: Bridging Simulation and Experiment in Pharmaceutical Development
This article provides a comprehensive framework for the experimental validation of nucleation models, a critical step for their reliable application in pharmaceutical development.
Secondary Nucleation Rate Measurement: Techniques, Models, and Applications in Pharmaceutical Development
This comprehensive review addresses the critical challenge of measuring and modeling secondary nucleation rates in crystallization processes, a key determinant of crystal size distribution, purity, and polymorphic form in pharmaceutical...
Beyond the Classical: Critical Challenges and Modern Advances in Nucleation Theory for Biomedical Research
Classical Nucleation Theory (CNT) has long provided a foundational framework for understanding phase transitions, yet it frequently fails to deliver quantitative predictions for complex systems.
Polymer-Induced Liquid Precursors (PILP): A Non-Classical Pathway for Advanced Biomaterials and Bone Regeneration
This article comprehensively examines the Polymer-Induced Liquid-Precursor (PILP) process, a transformative biomineralization pathway with significant implications for biomedical research and therapeutic development.
Beyond Classical Theory: How Liquid-Liquid Phase Separation Governs Nucleation in Biology and Disease
This article synthesizes the latest research on the pivotal role of Liquid-Liquid Phase Separation (LLPS) in non-classical nucleation pathways.
From Linear Models to Foundation AI: A Practical Comparison of Traditional QSPR and Modern Methods in Drug Discovery
This article provides a comprehensive analysis for researchers and drug development professionals on the evolution from traditional Quantitative Structure-Property Relationship (QSPR) methods to modern foundation models.
Optimizing Acquisition Functions for Bayesian Optimization: A Guide for Drug Discovery and Scientific Research
This article provides a comprehensive guide for researchers and scientists on optimizing acquisition functions (AFs) for Bayesian optimization (BO), with a focus on drug discovery applications.
Machine Learning Accelerates Density Functional Theory: Cutting Computational Costs for Drug and Materials Discovery
Density Functional Theory (DFT) is a cornerstone of modern computational chemistry and materials science but is plagued by high computational costs that limit its application to large, complex systems.
Advancing Solid-State Structure Prediction: Machine Learning, AI, and Validation Strategies for Biomedical Research
Accurate prediction of solid-state structures is a critical challenge with profound implications for drug development and material science.
Automated Feature Engineering for Nanomaterial Discovery: Accelerating AI-Driven Design and Development
This article explores the transformative role of automated feature engineering (AutoFE) in accelerating the discovery and development of nanomaterials.