Research Articles
Optimizing Molecular Property Prediction: A Bayesian Hyperparameter Tuning Guide
This article provides a comprehensive guide for researchers and drug development professionals on implementing Bayesian Optimization (BO) to tune machine learning models for molecular property prediction.
A Step-by-Step Protocol for Molecular Property Prediction Using ChemXploreML
This article provides a comprehensive, practical guide for researchers and drug development professionals to implement machine learning for molecular property prediction using ChemXploreML.
Molecular Graph Representations for AI: A Comprehensive Guide for Drug Discovery
This article provides a comprehensive overview of molecular graph representations, a cornerstone of modern AI-driven drug discovery.
Self-Supervised Learning for Molecular Representation: A Foundation for Next-Generation Drug Discovery
This article provides a comprehensive exploration of self-supervised learning (SSL) as a transformative paradigm for learning molecular representations in drug discovery and biomedical research.
A Practical Guide to Molecular Property Prediction with Machine Learning for Drug Discovery
This article provides a comprehensive roadmap for researchers and drug development professionals to implement machine learning for molecular property prediction.
A Beginner's Guide to Machine Learning in Drug Discovery: Foundations, Applications, and Future Trends
This guide provides researchers, scientists, and drug development professionals with a comprehensive introduction to the application of machine learning (ML) in modern drug discovery.
Molecular Descriptors in QSPR: A Comprehensive Guide for Drug Discovery and ADMET Prediction
This article provides a comprehensive overview of the critical role molecular descriptors play in Quantitative Structure-Property Relationship (QSPR) modeling for drug discovery and development.
Multi-Task Learning for Molecular Property Prediction: A Guide to Methods, Applications, and Best Practices
Multi-task learning (MTL) is transforming molecular property prediction by enabling models to learn multiple properties simultaneously, overcoming the critical challenge of scarce experimental data in drug discovery and materials science.
SMILES vs Graphs vs Fingerprints: A 2025 Guide to Molecular Representations in AI-Driven Drug Discovery
This article provides a comprehensive guide to the three pillars of molecular representation—SMILES, Graphs, and Fingerprints—tailored for researchers and professionals in drug development.
Graph Neural Networks for Molecular Property Prediction: A Complete Guide for Drug Discovery
This article provides a comprehensive introduction to Graph Neural Networks (GNNs) for molecular property prediction, a transformative technology accelerating drug discovery and materials design.