Research Articles
Handling Data Skew for Robust Feature Distribution and Stability Prediction in Drug Development
This article provides a comprehensive guide for researchers and drug development professionals on managing data skew to ensure reliable feature distribution and stability predictions.
Reducing Computational Cost in DFT Stability Calculations: 2025 Guide with Machine Learning & Best Practices
Density Functional Theory (DFT) is a cornerstone of computational chemistry and materials science, but its high computational cost remains a major bottleneck for high-throughput screening and large-scale dynamic simulations, particularly...
Feature Selection Engineering for Thermodynamic Stability Models: A Guide for Drug Development
This article provides a comprehensive guide for researchers and drug development professionals on applying feature selection engineering to build robust and predictive models for thermodynamic stability.
Correcting Unphysical Predictions in Defect Formation Energies: From Computational Fixes to AI Solutions
Accurate prediction of defect formation energies is paramount for advancing materials science, influencing properties from chemical reactivity to conductivity.
Improving Stability in Small-Sample Prediction Models: Methods for Biomedical and Clinical Research
This article addresses the critical challenge of model instability in stability prediction when working with limited sample sizes, a common scenario in biomedical research and drug development.
Optimizing Chemical Potential Ranges for Advanced Material Formation: Strategies for Researchers and Drug Developers
This article provides a comprehensive guide for researchers and drug development professionals on optimizing chemical potential ranges to control material formation.
Mitigating Inductive Bias for Robust Machine Learning in Material Stability Prediction
This article explores the critical challenge of inductive bias in machine learning (ML) models for predicting material stability, a key task in accelerating drug development and materials discovery.
Machine Learning for Material Stability Prediction: From Composition to Clinical Application
This article explores the transformative role of composition-based machine learning (ML) models in predicting material stability, a critical property for pharmaceutical development and advanced materials science.
Improving Accuracy in Machine Learning Predictions of Thermodynamic Stability: A Guide for Biomedical Researchers
This article provides a comprehensive guide for researchers and drug development professionals on enhancing the accuracy of machine learning models for predicting thermodynamic stability—a critical property in drug design and...
Graph Neural Networks for Interatomic Interactions: Advancing Stability Prediction in Drug Discovery and Materials Science
This article explores the transformative role of Graph Neural Networks (GNNs) in predicting interatomic interactions and system stability, a critical challenge in computational chemistry and drug development.