Research Articles
MM-GBSA vs FEP: A Comprehensive Guide to Binding Affinity Prediction in Drug Discovery
This article provides a detailed comparative analysis of Molecular Mechanics Generalized Born Surface Area (MM-GBSA) and Free Energy Perturbation (FEP) methods for predicting protein-ligand binding affinities.
From Hypothesis to Validation: Using MM-GBSA to Confirm and Refine Your Pharmacophore Models
This article provides a comprehensive guide for computational chemists and drug discovery scientists on integrating Molecular Mechanics Generalized Born Surface Area (MM-GBSA) calculations with pharmacophore modeling.
MLIP vs Classical Force Fields: A Definitive Accuracy Benchmark for Computational Drug Discovery
This article provides a comprehensive analysis comparing the accuracy of Machine Learning Interatomic Potentials (MLIPs) with traditional classical force fields (FFs) in the context of biomedical research.
Machine Learning Interatomic Potentials vs DFT: A Comprehensive Guide to Energy & Force Validation for Drug Discovery
This article provides researchers, scientists, and drug development professionals with a comprehensive framework for validating Machine Learning Interatomic Potentials (MLIPs) against Density Functional Theory (DFT).
Mastering MLIP Training Set Generation: Strategies for Accurate Drug Discovery & Materials Science
This comprehensive guide explores the critical process of configuration space generation for Machine Learning Interatomic Potentials (MLIPs).
Reducing the Computational Cost of MLIP Training: Practical Strategies for Drug Discovery Researchers
Machine Learning Interatomic Potentials (MLIPs) are revolutionizing molecular dynamics simulations in drug discovery, but their high computational cost remains a significant barrier.
Beyond the Hype: Building Robust MLIPs for Reliable Molecular Dynamics in Drug Discovery
This article provides a comprehensive guide for researchers and drug development professionals on ensuring the robustness of Machine Learning Interatomic Potentials (MLIPs) in molecular dynamics (MD) simulations.
Bridging the Prediction Gap: A Comprehensive Guide to Discrepancy Analysis in MLIP Rare Event Modeling for Drug Discovery
This article provides a comprehensive analysis of discrepancy analysis in Machine Learning Interatomic Potential (MLIP) models for rare event prediction, a critical challenge in computational drug discovery and materials science.
MLIP Phase Change Memory: From Materials Science to Transformative Biomedical Applications
This article explores the application of Machine Learning Interatomic Potentials (MLIPs) in accelerating the discovery and optimization of Phase Change Memory (PCM) materials, with a focus on implications for biomedical...
Mastering Rare Event Sampling: A Comprehensive Guide to MLIP-Driven Molecular Dynamics for Drug Discovery
This article provides a detailed exploration of Machine Learning Interatomic Potentials (MLIPs) for simulating rare events in molecular dynamics, crucial for drug discovery and biomolecular research.