Research Articles
Validating Target Prediction Methods in Computational Chemistry: A 2025 Guide to Methods, Benchmarks, and Best Practices
Accurate computational prediction of drug-target interactions is crucial for reducing the high cost and time of drug discovery.
Sequential Simplex Optimization: A Strategic Guide for Efficient Chemical and Pharmaceutical Development
This article provides a comprehensive guide to Sequential Simplex Optimization, a highly efficient experimental design strategy for optimizing chemical and pharmaceutical systems.
Managing Computational Expense in Coupled-Cluster Calculations: Strategies for Accurate and Feasible Simulations
This article provides a comprehensive guide for researchers and drug development professionals on managing the high computational costs of coupled-cluster methods, the gold standard in quantum chemistry.
Hyperparameter Optimization for Chemistry ML Models: A Guide to Enhanced Accuracy and Efficiency
This article provides a comprehensive guide to hyperparameter optimization (HPO) for machine learning models in chemical sciences.
Active Learning for Chemical Space Exploration: Accelerating Drug Discovery with AI
This article explores the transformative role of Active Learning (AL) in navigating the vast chemical space for drug discovery.
Molecular Dynamics Simulations in Biomolecular Research: From Atomic Insights to Drug Discovery
This article provides a comprehensive overview of Molecular Dynamics (MD) simulations and their transformative role in biomolecular research and drug development.
Computational Approaches for Reaction Mechanism Exploration: From Quantum Chemistry to AI-Guided Discovery
This article provides a comprehensive overview of modern computational strategies for elucidating chemical reaction mechanisms, a critical capability for researchers in drug development and chemical sciences.
Machine Learning in Protein-Ligand Binding Affinity Prediction: From Foundational Concepts to Advanced Applications in Drug Discovery
Accurate prediction of protein-ligand binding affinity is a cornerstone of computational drug discovery, enabling rapid identification and optimization of therapeutic candidates.
AI-Driven Computational Chemistry: Revolutionizing Materials Design and Drug Discovery in 2025
This article explores the transformative integration of artificial intelligence and computational chemistry in designing advanced materials and therapeutics.
Free Energy Perturbation for Protein-Ligand Binding: From Theory to AI-Driven Drug Discovery
This article provides a comprehensive overview of Free Energy Perturbation (FEP), a rigorous physics-based method for predicting protein-ligand binding affinities in drug discovery.