Research Articles
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.
High-Throughput Computational Screening of Crystal Structures: Accelerating Drug Discovery and Materials Design
This article provides a comprehensive overview of high-throughput computational screening (HTCS) for crystal structures, a transformative approach accelerating discovery in structural biology, drug development, and materials science.
Machine Learning-Accelerated Genetic Algorithms (MLaGA): Revolutionizing Nanoparticle Discovery for Biomedical Applications
This article explores the transformative integration of Machine Learning (ML) with Genetic Algorithms (GA) to accelerate the discovery and optimization of nanoparticles for drug delivery and biomedical applications.
Beyond the Filter: Understanding the Critical Limitations of Forward Screening in Modern Materials Discovery
Forward screening, the long-standing paradigm of filtering pre-defined material candidates against target properties, faces fundamental challenges in the era of vast chemical spaces and AI-driven design.