Research Articles
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.
Mastering the MLIP Database: A Complete Training Guide for Biomedical Researchers & Drug Developers
This comprehensive guide provides biomedical researchers and drug development professionals with structured training on the Materials Project (MLIP) database.
MLIP Machine Learning Potentials for Lithium Battery Electrolyte Simulations: From Atomistic Accuracy to Next-Generation Design
This article provides a comprehensive guide for researchers and scientists on applying Machine Learning Interatomic Potentials (MLIPs) to simulate lithium battery electrolytes.
Converging on Accuracy: A Practical Guide to Fixing FEP Sampling and Convergence Issues
Free Energy Perturbation (FEP) calculations have become an indispensable tool in computational drug discovery for predicting binding affinities.
Optimizing Lennard-Jones Parameters with Condensed-Phase Data: A Guide for Robust Force Field Development
This article provides a comprehensive guide for researchers and scientists in drug development on the advanced optimization of Lennard-Jones (LJ) parameters using condensed-phase target data.
Node-Embedding Approaches for Consistent Force Field Parameter Assignment: From Molecular Graphs to Accurate Simulations
This article explores the transformative shift in molecular mechanics force field development from traditional, discrete atom-typing to modern, data-driven node-embedding approaches.
Iterative Optimization for Force Field Development: A Modern Guide to Enhanced Accuracy and Efficiency
This article provides a comprehensive guide to iterative optimization procedures for developing molecular mechanics force fields, a critical tool for computational drug discovery and materials science.
Bayesian Inference of Conformational Populations (BICePs): A Guide to Advanced Parameter Refinement in Structural Biology and Drug Development
This article explores Bayesian Inference of Conformational Populations (BICePs), a powerful algorithm that refines computational models against sparse and noisy experimental data.
Predicting Molecular Mechanics Parameters with Graph Neural Networks: A New Paradigm for Force Field Development
This article explores the transformative role of Graph Neural Networks (GNNs) in predicting Molecular Mechanics (MM) force field parameters, a critical task for accurate and efficient molecular dynamics simulations in...
Advanced Flexible Scanning for Torsion Parameter Optimization in Biomolecular Force Fields
This article provides a comprehensive guide for researchers and drug development professionals on performing flexible scans to optimize torsion parameters in molecular simulations.