PARAM-DOCK: An Integrated Framework for Parallel Multi-Protein–Multi-Ligand Docking and Scoring for Scalable Structure-Based Drug Discovery
Abstract
Molecular docking is a central component of structure-based drug discovery, but practical docking studies often require several independent programs for receptor and ligand preparation, binding-site definition, docking, visualisation, interaction analysis, and downstream compound assessment. This fragmentation becomes particularly limiting when multiple protein targets and multiple ligands must be evaluated in the same study. Here, we present PARAM-DOCK, an open-source molecular docking engine and integrated web workflow implemented in Python with Numba-compiled numerical kernels. PARAM-DOCK supports parallel multi-protein-multi-ligand docking and automates major pre-processing and post-processing steps within a single platform. The docking engine combines a seven-term empirical scoring function with an iterated local search strategy in which Monte Carlo perturbations are followed by bounded quasi-Newton minimisation using analytical gradients. Final pose selection can additionally incorporate binding-basin population and pocket-complementarity descriptors when they are informative. The web platform integrates molecular preparation, docking setup, pose ranking, 2D and 3D protein-ligand interaction analysis, comparative score and interaction heatmaps, molecular visualisation, and ADMET prediction. PARAM-DOCK was benchmarked against Vina using matched receptor and ligand PDBQT files, identical search boxes, symmetry-corrected RMSD evaluation, and identical hardware. On the 285-complex CASF-2016 core set, PARAM-DOCK achieved a Pearson correlation of 0.631 for scoring power and a mean within-target Spearman correlation of 0.579, compared with 0.604 and 0.528 for Vina. In de novo redocking of 255 complexes, PARAM-DOCK placed the top-ranked pose within 2.0 Å in 68.6% of cases and recovered a near-native pose among nine outputs in 84.3%, compared with 74.1% and 93.7% for Vina. Conditional on generating a near-native pose, PARAM-DOCK ranked it first in 81.4% of complexes versus 79.1% for Vina. These results indicate competitive scoring and ranking performance, while conformational sampling remains the main area for further improvement.
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BibTeX
@misc{kittad2026paramdock,
title = {{PARAM-DOCK: An Integrated Framework for Parallel Multi-Protein–Multi-Ligand Docking and Scoring for Scalable Structure-Based Drug Discovery}},
author = {Samarth Kittad and Mallikarjunachari Uppuladinne and Aneesh Kotipalli and Shruti Koulgi and Vinod Jani and Uddhavesh Sonavane},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15008491/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008491/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - PARAM-DOCK: An Integrated Framework for Parallel Multi-Protein–Multi-Ligand Docking and Scoring for Scalable Structure-Based Drug Discovery
AU - Samarth Kittad
AU - Mallikarjunachari Uppuladinne
AU - Aneesh Kotipalli
AU - Shruti Koulgi
AU - Vinod Jani
AU - Uddhavesh Sonavane
PY - 2026
DA - 2026/09/08
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008491/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008491/v1
AB - Molecular docking is a central component of structure-based drug discovery, but practical docking studies often require several independent programs for receptor and ligand preparation, binding-site definition, docking, visualisation, interaction analysis, and downstream compound assessment. This fragmentation becomes particularly limiting when multiple protein targets and multiple ligands must be evaluated in the same study. Here, we present PARAM-DOCK, an open-source molecular docking engine and integrated web workflow implemented in Python with Numba-compiled numerical kernels. PARAM-DOCK supports parallel multi-protein-multi-ligand docking and automates major pre-processing and post-processing steps within a single platform. The docking engine combines a seven-term empirical scoring function with an iterated local search strategy in which Monte Carlo perturbations are followed by bounded quasi-Newton minimisation using analytical gradients. Final pose selection can additionally incorporate binding-basin population and pocket-complementarity descriptors when they are informative. The web platform integrates molecular preparation, docking setup, pose ranking, 2D and 3D protein-ligand interaction analysis, comparative score and interaction heatmaps, molecular visualisation, and ADMET prediction. PARAM-DOCK was benchmarked against Vina using matched receptor and ligand PDBQT files, identical search boxes, symmetry-corrected RMSD evaluation, and identical hardware. On the 285-complex CASF-2016 core set, PARAM-DOCK achieved a Pearson correlation of 0.631 for scoring power and a mean within-target Spearman correlation of 0.579, compared with 0.604 and 0.528 for Vina. In de novo redocking of 255 complexes, PARAM-DOCK placed the top-ranked pose within 2.0 Å in 68.6% of cases and recovered a near-native pose among nine outputs in 84.3%, compared with 74.1% and 93.7% for Vina. Conditional on generating a near-native pose, PARAM-DOCK ranked it first in 81.4% of complexes versus 79.1% for Vina. These results indicate competitive scoring and ranking performance, while conformational sampling remains the main area for further improvement.
ER -