Role of Molecular Docking in Drug Discovery

Drug discovery requires two main methods: virtual computational screening techniques and traditional experimental high-throughput methods. Molecular docking is an established computational structure-based method used in drug discovery. It allows the identification of potential drug targets and predicting molecular ligand-target interactions at the atomic level. Hence, the behavior of the molecules at the binding site as well as the biochemical processes can be characterized.

Drug Discovery

Drug Discovery. Image Credit: metamorworks/

How it works

With degrees of translational, rotational, and conformational freedom of both the ligand and the target protein, there are numerous possible binding modes between the two molecules. Unfortunately, it would be too computational heavy and expensive to test every combination. Hence molecular docking provides a way of discovering potential drugs.

The molecule docking process involves two main steps: predicting ligand conformation, its position and orientation in these sites, and assessing the binding affinity. Although usually little is known about the structure of the ligand, knowing the location of the binding site significantly increases docking efficiency.

The binding site can be known by referencing related proteins sharing a similar function or can be predicted using software such as GRID and POCKET. Docking without any assumption about the binding site is called blind docking.

The algorithm for molecular docking was first introduced in the 1980s. It has now become of great importance for pharmaceutical research. The algorithm for molecular docking first samples conformations of the ligand in the active site of the protein then ranks the conformations via a scoring system. After that, the algorithm predicts quantitative binding energetics and eventually provides rankings of docked ligand-target complexes with the best stability.

Conformational search

In the conformational search stage, structural parameters of the ligand are considered. This includes torsional, translational, and rotational degrees of freedom. The algorithm performs this by a combination of systematic and stochastic search methods. The systematic search changes the structural parameters of the ligand and probes the energy landscape.

Eventually, the minimum energy solution for binding can be elucidated. Whereas the stochastic search changes the conformation parameters randomly and generates a wide range of energy landscapes.

Evaluation of binding energetics

Molecular docking programs use scoring systems to predict the binding energetics between the ligand and the target protein. The binding constant (Kd) and the Gibbs free energy (ΔGL) of the most important physical-chemical event during binding are evaluated. The lowest energy cost interaction is highly ranked.

Structural-based methods

With the predictions from molecular docking,  the ligand can then be synthesized and tested experimentally. The binding complex can be crystallized to further understand the binding mechanism, as well as potential inhibitors. This saves time and money compared to traditional high-throughput methods where a vast number of ligands were screened experimentally. Molecular docking provides a more refined, target-based method for drug discovery.


  • Ferreira, L., Dos Santos, R., Oliva, G., & Andricopulo, A. (2015). Molecular docking and structure-based drug design strategies. Molecules (Basel, Switzerland), 20(7), 13384–13421.
  • Pinzi, L., & Rastelli, G. (2019). Molecular Docking: Shifting Paradigms in Drug Discovery. International Journal of Molecular Sciences, 20(18), 4331–.
  • Meng, X. Y., Zhang, H. X., Mezei, M., & Cui, M. (2011). Molecular docking: a powerful approach for structure-based drug discovery. Current computer-aided drug design, 7(2), 146–157.

Further Reading

Last Updated: Mar 24, 2021

Christy Cheung

Written by

Christy Cheung

Christy is passionate about communicating science to a wide range of audiences- from the general public to researchers in various fields. She has a BSc in Biological Sciences and is now an MRes student in Biomedical Research Bacterial Pathogenesis and Infection stream at Imperial College London. She has a great interest in tackling the problem of antimicrobial resistance and in translating pre-clinical research into therapeutic solutions.


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