Novel benzofuranyl chalcones with antileishmanial activity: synthesis, structure-activity relationships, and target-oriented computational studies

This dataset contains all data files (~4 GB) for the computational studies of benzofuranyl chalcones as antileishmanial agents. 📌 Important: This dataset is designed to work with the Jupyter notebooks available in the GitHub repository:GitHub: https://github.com/Adrian-D-Vargas/Novel-benzofuranyl-chalcones 📦 Dataset Contents This Zenodo repository contains 6 compressed archives with all molecular docking, MD simulation, and trajectory data: File Description Compressed Size Uncompressed Size docking.tar.xz Complete docking workflow: structures, poses, grids, results ~2.5 MB ~28 MB md_trajs.tar.xz Aligned MD trajectories (15 × 100ns) + dry topologies ~3.9 GB ~6 GB chalcone-13e.tar.xz System 13e: solvated topology + MD input files ~3.1 MB ~50 MB chalcone-14c.tar.xz System 14c: solvated topology + MD input files ~3.1 MB ~50 MB chalcone-14l.tar.xz System 14l: solvated topology + MD input files ~3.1 MB ~50 MB md_initial_structures.tar.xz Initial structures for MD preparation ~1 MB ~3 MB Total dataset size: ~4 GB compressed, ~7 GB uncompressed 🚀 Quick Start Guide STEP 1: Clone GitHub Repository First, clone the Jupyter notebooks from GitHub: git clone https://github.com/Adrian-D-Vargas/Novel-benzofuranyl-chalcones.git cd Novel-benzofuranyl-chalcones STEP 2: Download Zenodo Files Download all files from this Zenodo repository to your local clone directory. STEP 3: Extract Archives Extract each compressed archive in the repository root: # Extract all data files tar -xf docking.tar.xz tar -xf md_trajs.tar.xz tar -xf chalcone-13e.tar.xz tar -xf chalcone-14c.tar.xz tar -xf chalcone-14l.tar.xz tar -xf md_initial_structures.tar.xz # Verify directory structure ls -d */ # Expected: chalcone-13e/ chalcone-14c/ chalcone-14l/ docking/ md_initial_structures/ md_trajs/ STEP 4: Run Jupyter Notebooks jupyter notebook # Open and run in order: # 1. 1_docking-analysis.ipynb # 2. 2_md-preparation.ipynb (reference only) # 3. 3_md-analysis.ipynb (Sections 2-5) # 4. 4_md-visualizations.ipynb 📁 Detailed Archive Contents 🔬 docking.tar.xz Molecular docking data for 34 benzofuranyl chalcones against FRD enzyme. docking/ ├── results.csv # Summary of all docking scores ├── FAD.sdf # FAD cofactor structure ├── FRD-apo_H.mol2 # FRD protein structure (protonated) ├── good.prm, good.as, good_cav1.grd # rDock configuration files ├── chalcone_preparation/ │ ├── chalcone-DB.csv # 34 chalcones with SMILES │ └── chalcone-DB.sdf # 3D structures library ├── output_poses/ # Final docked poses (best pose per ligand) └── output_rDock/ ├── docking_out.sdf # Raw rDock output (100 poses per ligand) ├── docking_out_sorted.sdf # Sorted by score ├── docking_out_sorted_unique.sdf # Unique best poses └── docking_results_processed.csv # Processed scores Key file: results.csv - Contains IC₅₀, docking scores, and MM-PBSA energies for all compounds. 🎬 md_trajs.tar.xz Aligned MD trajectories for analysis (reduced to 1 frame every 5 frames from original 100 ns simulations). md_trajs/ ├── 13e_rep1.nc, 13e_rep2.nc, ..., 13e_rep5.nc # Chalcone 13e trajectories ├── 14c_rep1.nc, 14c_rep2.nc, ..., 14c_rep5.nc # Chalcone 14c trajectories ├── 14l_rep1.nc, 14l_rep2.nc, ..., 14l_rep5.nc # Chalcone 14l trajectories ├── FRD-13e_dry.parm7 / .rst7 # Dry topology and coordinates 13e ├── FRD-14c_dry.parm7 / .rst7 # Dry topology and coordinates 14c └── FRD-14l_dry.parm7 / .rst7 # Dry topology and coordinates 14l Note: Each trajectory contains 1 frame every 5 frames from original 100 ns simulations.Use with: Notebook 3_md-analysis.ipynb (Sections 2-5) and 4_md-visualizations.ipynb. ⚛️ chalcone-13e.tar.xz, chalcone-14c.tar.xz, chalcone-14l.tar.xz (~50 MB each) Complete MD simulation systems (solvated topologies + input files). chalcone-XX/ ├── FRD-XX.parm7 # Solvated topology (AMBER format) ├── FRD-XX.rst7 # Initial coordinates ├── check_com.sh # Center of mass verification script ├── min/ │ ├── min.in # Minimization input │ └── min1.in # Additional minimization ├── eq/ │ ├── eq1.in ... eq5.in # Equilibration protocol (5 stages) └── md1/ ... md5/ └── md.in # Production MD input (100 ns each) Use case: Reference for MD simulation parameters or to reproduce simulations with AMBER. 🧱 md_initial_structures.tar.xz (~40 MB) Initial structures used for MD system preparation. md_initial_structures/ ├── FAD.sdf # FAD cofactor ├── FRD_amber.pdb # AMBER stile pdb └── FRD_apo_H.pdb # FRD protein from *.mol2 Use with: Notebook 2_md-preparation.ipynb for understanding system preparation workflow. 🔬 Experimental Data Summary Top Performing Compounds The dataset includes computational studies for 35 benzofuranyl chalcones tested against Leishmania mexicana. Systems selected for MD simulations (highest metabolic inhibition): Compound IC₅₀ (μM) Metabolic Inhibition (%) Selectivity Index MM-PBSA ΔG (kcal/mol) Chalcone 13e 90.87 90.9 ± 0.5 462.96 -22.27 Chalcone 14c 91.15 91.2 ± 0.4 370.37 -16.13 Chalcone 14l 90.82 90.8 ± 0.5 666.67 -15.86 Complete data: See ./results.csv after extraction. 💻 Software Requirements To work with this dataset, you need: Molecular Docking Analysis Python 3.8+ with pandas, matplotlib, seaborn, rdkit Open Babel (for structure processing) MD Trajectory Analysis AmberTools 22+ (cpptraj for trajectory processing) MD Simulation (Optional - to reproduce simulations) AMBER 22 (commercial license required) Full requirements: See GitHub repository README for detailed installation instructions.