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:

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):

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.