DockNmine, a Web Portal to Assemble and Analyse Virtual and Experimental Interaction Data

Area of Science:

• Computational chemistry
• Drug discovery
• Bioinformatics

Background:

• Drug discovery requires extensive experiments to identify compounds binding to specific targets.
• The vast chemical space makes experimental screening of all potential ligands infeasible.
• Virtual screening and scoring functions are crucial for predicting ligand-protein interactions but face challenges in data standardization and transferability.

Purpose of the Study:

• To develop a platform for expert and authenticated annotation of experimental and docking data.
• To address the limitations of existing data repositories in handling standardized molecular descriptions and experimental results.
• To facilitate the comparison and validation of computational predictions against experimental data in drug discovery.

Main Methods:

• The dockNmine platform was designed to incorporate controlled information using standard identifiers (Uniprot ID for proteins, SMILES for ligands).
• It allows the input of experimental data and associated publications.
• The platform facilitates the incorporation of docking experiments through forms that parse parameters and results, and provides pre-computed outputs for correlation analysis.

Main Results:

• dockNmine enables standardized data entry for ligands and targets, enhancing data retrieval and cross-database compatibility.
• The platform integrates docking experiment details, including parameters and outcomes.
• Pre-computed analyses are available to assess the correlation between docking predictions and experimental findings.

Conclusions:

• dockNmine provides a valuable service for expert annotation and standardization of drug discovery data.
• The platform improves the reliability and comparability of virtual screening results with experimental outcomes.
• It offers a solution for managing and analyzing integrated experimental and computational data in drug discovery pipelines.