2026-10-13T15:09:04Z https://www.scidb.cn/oai

10.57760/sciencedb.26031 2025-06-10T11:06:28Z

2025-06-10 Molecular Dynamics Simulation Data of Five BRAF V600E Inhibitor Complexes: AI-Designed Small Molecules in Oncology doi:10.57760/sciencedb.26031 en Background & ContextThis dataset comprises molecular dynamics (MD) simulations of four novel BRAF V600E inhibitor complexes, designed using REINVENT 4, an advanced AI/ML framework integrating reinforcement learning (RL) and transfer learning (TL). The BRAF V600E mutation is a critical therapeutic target in melanoma, thyroid carcinoma, and colorectal cancer, but existing inhibitors face challenges like drug resistance and off-target toxicity. These simulations validate the binding stability and dynamics of AI-generated inhibitors, offering insights for structure-based drug optimization.Data ContentThe dataset includes:Topology Files (*.top, *.gro) – Essential for simulation reproducibility.Trajectory Files (e.g., *.xtc, *.trr) – Full MD trajectories for all replicates.Simulation Parameters – Force field (e.g., AMBER/CHARMM), temperature, pressure, and integration steps.Potential ApplicationsDrug Discovery: Benchmarking AI-designed inhibitors against known BRAF inhibitors.Structural Biology: Studying protein-ligand stability and conformational dynamics.Machine Learning: Training predictive models for binding affinity or resistance mutations. BRAF V600E inhibitors; AI-driven drug design; Molecular dynamics simulations; Oncology therapeutics; REINVENT 4; GROMACS/AMBER Zuokun Lu PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank