Research Articles
Advancing Scoring Functions in Molecular Docking: From Foundational Principles to Machine Learning and Robust Validation
Molecular docking is a cornerstone of structure-based drug design, yet the accuracy of its predictions hinges critically on the performance of scoring functions.
Benchmarking Quantum Mechanical Methods for Reaction Pathway Prediction: A Guide for Computational Chemists and Drug Developers
This article provides a comprehensive comparison of quantum mechanical (QM) methods for modeling chemical reaction pathways, a critical task in drug discovery and synthetic methodology development.
Statistical Validation Techniques for Predictive Models: A Comprehensive Guide for Biomedical Research
This article provides a comprehensive framework for the statistical validation of predictive models in biomedical and clinical research.
Benchmark Datasets for Computational Chemistry: A Guide for Drug Development and AI Model Validation
This article provides a comprehensive guide to benchmark datasets for computational chemistry, tailored for researchers and drug development professionals.
Beyond Accuracy: A Modern Framework for Assessing Model Performance in Molecular Property Prediction
Accurately assessing model performance is paramount for the successful application of machine learning in drug discovery and materials science.
Bridging the Digital and the Physical: A Framework for Validating Computational Predictions with Experimental Data in Biomedicine
This article provides a comprehensive guide for researchers and drug development professionals on the critical process of comparing computational predictions with experimental data.
Evaluating Chemical Knowledge in Large Language Models: Benchmarks, Applications, and Future Directions
This article provides a comprehensive overview for researchers and drug development professionals on the evaluation of Large Language Models (LLMs) in chemistry.
Robust Validation Strategies for Computational Models in Drug Discovery: A Guide for Researchers
This article provides a comprehensive guide to validation strategies for computational models, with a specific focus on applications in drug discovery and development.
Coupled Cluster vs. DFT: A Practical Guide to Accuracy, Cost, and Application in Computational Chemistry and Drug Design
This article provides a comprehensive comparison between Coupled Cluster (CC) theory, particularly CCSD(T), the 'gold standard' of quantum chemistry, and the more computationally efficient Density Functional Theory (DFT).
Benchmarking DFT for Transition Metal Complexes: A Guide to Accurate Calculations in Catalysis and Drug Design
Density Functional Theory (DFT) is indispensable for studying transition metal complexes in catalysis and pharmaceutical development, but achieving accurate results is notoriously challenging.