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Molecular docking is a computational approach used to predict the binding orientation and affinity of ligands with target receptors, supporting drug discovery and biomedical research. This chapter outlines the principles, methodology, and recent advancements in docking. The process involves protein and ligand preparation, grid generation, active site prediction, and docking simulations, which collectively help identify stable complexes and optimize lead molecules. Emerging developments, including deep learning frameworks like GNINA, hybrid artificial intelligence approaches, and improved docking algorithms, have enhanced prediction accuracy and efficiency in virtual screening. Applications of molecular docking extend to drug repurposing, anticancer and antidepressant drug design, biosensor development, biomarker identification, and environmental remediation. Case studies include thymol and carvacrol derivatives as anticancer agents, chalcone derivatives with antidepressant potential, and graphene-based composites for dental restoratives. Docking also assists in evaluating interactions with metabolic enzymes, supporting drug safety and specificity. Despite limitations such as scoring accuracy and receptor flexibility, integration with molecular dynamics, machine learning, and quantum computing offers promising future directions. Molecular docking continues to be a cost-effective and versatile tool in drug development, personalized medicine, and translational research.
Chitkara College of Pharmacy, Chitkara University, Rajpura, India
Gagandeep Singh
Chitkara College of Pharmacy, Chitkara University, Rajpura, India
Rohit Bhatia
*
Chitkara College of Pharmacy, Chitkara University, Rajpura, India
*Address all correspondence to: bhatiarohit5678@gmail.com
1. Introduction
The purpose of research on docking is to anticipate the desired three-dimensional arrangements [1]. The state of the art in several computational elements of virtual screening of a library of small compounds utilizing molecular docking is described in the current work [1, 2]. Docking is a popular technique for predicting how small-molecule medicinal drugs will align with their protein targets, thereby predicting the small molecule’s affinity [3, 4]. Molecular modeling is a technique that creates, characterizes, and alters the structures and interactions of substances, as well as their properties, which are dependent on their three-dimensional geometries [4]. This prediction is used to virtually screen huge libraries of compounds and produces the conformation and, typically, the binding affinity of the small molecule in its projected least energy state. Sampling and scoring are the two primary steps that make up docking. A thorough search of the conformational space of the molecules being docked is referred to as sampling [5]. The ability to use the pose for lead optimization and the determination of a small molecule’s proper binding pose are both necessary.
For figuring out its binding affinity, activities like virtual screening or deciding whether a molecule is significant for further experimental research, accurate assessment of binding affinity is essential [6]. Empirical scoring functions like X-Score, AutoDock Vina, and Chem-Score are used by a significant percentage of docking software [7, 8]. Using a particular method, the molecular docking program can assist us in determining the best conformation and orientation based on complementarity and pre-organization. A scoring function is then applied to forecast the binding affinity and examine the interactive mode [9]. A general docking procedure is depicted in Figure 1.
The methodology of molecular docking involves a systematic approach to predicting the preferred orientation of a ligand when bound to a receptor (macromolecule) in order to form a stable complex. The process involves the following steps:
2.1 Step 1. Preparation of protein and ligand
The target protein’s three-dimensional (3D) structure is acquired from the Protein Data Bank (PDB), a database kept up to date by the Research Collaboratory for Structural Bioinformatics (RCSB), in order to initiate the molecular docking procedure. To make sure the protein structure is appropriate for docking simulations, it must be pre-processed after downloading. The following actions are involved in this: elimination of water molecules, which, unless they have a recognized function in the active site, may obstruct ligand binding; stabilization of charges to restore amino acid residues’ proper ionization states; and completing any atoms or residues that are missing from the PDB dataset.
Hydrogen atoms, particularly polar hydrogens, are added to mimic physiological circumstances.
When required, side chains are created to finish off incomplete amino acid residues.
2.2 Step 2: Preparation of ligand
For this ligand molecule, Lipinski’s Rule of Five was then applied. It is applied to both drug-like and drug-unlike substances. It lowers the failure rate and raises the high likelihood of success. Pub Chem compounds can be obtained from a variety of databases, including ZINC. It might utilize the chem sketch tool from the mol file.
2.3 Step 3: Grid generation
Each aspect is maintained in this, including the site, rotatable group, excluded volumes, and limitations. The primary determinant is the quantity of genetic operations (crossover, migration, and mutation) carried out in determining the binding cavity prediction.
2.4 Step 4: Prediction of active site
It is necessary to predict the protein molecule’s active site. Following protein preparation, any water molecules and heteroatoms that may be present are removed from the cavity.
2.5 Step 5: Docking run
The interaction between ligands and proteins is examined. The optimal docking score is then determined [10–12].
The following sections highlight some of the recent advances employed in molecular docking, which are making this a more promising approach in drug design and discovery.
3.1 Drug repurposing using AI, molecular docking and hybrid approaches
A research group investigated two important drug repurposing approaches – the artificial intelligence (AI) approach and the molecular docking technique – in both general disorders and Alzheimer’s Disease (AD) in particular. Furthermore, the hybrid strategy that combines molecular docking methods and artificial intelligence was also investigated. In AD research, drug repurposing shows promise as a persuasive and successful tactic. In this field, molecular docking techniques and artificial intelligence both show great promise. More accurate predictions are produced by AI algorithms, which make it easier to investigate novel therapeutic uses for currently available medications. Similarly, drug–target interaction modeling is revolutionized by molecular docking approaches, which use sophisticated algorithms to evaluate large drug databases against particular target proteins. The goal is to expedite the drug discovery process, which may enhance the therapy strategy for AD [13].
3.2 GNINA1.3 the next increment in molecular docking with deep learning
This approach offers a straightforward covalent docking interface. As with previous programs, GNINA expects the bound covalent form of the ligand to be supplied as input rather than assuming a specific chemical process. The underlying deep learning framework is updated to PyTorch in this release, which makes docking more computationally efficient and opens the door for the smooth integration of additional deep learning techniques into the docking pipeline. To enable high-throughput virtual screening with GNINA, scientists introduced knowledge-distilled CNN scoring functions and retrained CNN scoring functions on the revised CrossDocked2020 v1.3 dataset [14].
3.3 Molecular docking simulation DFT computation formation of creatinine chemosensor of Cu2+
Using SiO2-Pr-SO3H as a silica nano-catalyst, a multi-component reaction of isatin, malononitrile, and creatinine is carried out to create a Cu2+ chemosensor in a quick reaction time and with high yield, without the need for solvents. Since the Cu2 + ion is clearly one of the hazardous heavy metals for both human health and the environment, it is critical to quickly identify and detect it using an effective chemosensor. The electronic characteristics of the ligand and the ligand-Cu2+ complex were examined, as well as the ligand’s active site for Cu2+ interaction, using the computational approach (DFT). The activity of the synthesized compound against the COVID-19 major protease (PDB ID: 6LU7) is examined using molecular docking modeling [15].
3.4 Modified AutoDock Vina algorithms
AutoDock Vina (Vina) has solidified its position as a top software package due to its user-friendly interface and effective energy-based scoring system. However, the growing demands of computational biology have prompted efforts to improve Vina, leading to several algorithmic breakthroughs. This in-depth assessment examines Vina’s most recent developments in molecular docking. These changes include novel scoring functions combined with machine learning and hybrid parallelization techniques that utilize high-performance computing [16].
Design of thymol and carvacrol derivatives against cancer
By the help of computational analysis of thymol and carvacrol derivatives as anticancer agents, several thymol and carvacrol compounds have been created. To discover and evaluate the possible biological targets associated with cancer, computational techniques such as network pharmacology and molecular docking approaches were employed. The ethoxy-cyclohexyl analogues were consistently the most effective among the produced compounds against a panel of 10 distinct cancer cell lines from various origins (see Figure 3). The AKT1 protein was identified as a core and central target of the most active drugs based on biological target predictions. This protein has a binding pocket that the most potent substances bind to, according to molecular docking [17, 18].
Design of chalcone derivatives connected to N-heterocyclic compounds
New chalcone derivatives with a benzo[d][1–3]triazol-1-yl substituent were designed using a molecular docking approach, suggesting increased antidepressant efficacy (see Figure 4). The activity of chalcone compounds containing morpholino and piperidin-1-yl groups was comparatively reduced. The chalcone derivatives demonstrated excellent binding affinity with a good docking score [19].
Design of graphene-based analogs
A molecular docking and dynamics investigation of graphene and its modifications for improved adherence in dental restoratives was reported recently. Because of its structural and adhesive qualities, which have improved the mechanical performance of dental composites, graphene has garnered a lot of interest in the dental field. For this, molecular docking and molecular dynamics (MD) simulations were adopted to study the behavior and interactions of monomers and graphene-based adhesives. Molecular docking was used to evaluate the binding energies and interactions between monomers and graphene derivatives (see Figure 5), as well as MD simulations [20–23].
Discovery of significant biomarker genes for the occurrence of kidney renal clear cell carcinoma and liver hepatocellular carcinoma
A combined high-throughput screening and molecular docking strategy, with potential for specialized treatment interventions, was developed for the discovery of cancer biomarkers. The cancer-fighting capabilities of andrographolide, particularly against liver hepatocellular carcinoma (LIHC; see Figure 6), have been emphasized by molecular docking and network pharmacology [24, 25].
Remediation of materials
Protein-ligand docking can also be used to predict which pollutants are enzyme-degradable. The most effective drug may be identified using it, as well as its desired location. Enzymes and their mechanisms of action can also be determined using molecular docking. It can also be applied to ascertain the connections among proteins. The remediation method is used to virtually filter molecules [26].
Molecular modeling in modern drug development
Molecular docking is employed to assess any damage caused by interaction with other proteins, including cytochrome P450 and proteases. Docking is used to ascertain proposed drug specificity against homologous proteins, and understanding cellular relationships facilitates the discovery of possible pharmacological targets [27].
Figure 3.
Structure of designed thymol (TH) and carvacrol (CH) analogs.
Figure 4.
Structure of designed potent chalcone derivatives.
Numerous tools used for drug design and discovery are available through molecular docking. Medical chemists make it easy to view structured molecular databases. It predicts ligand-receptor attachments with accuracy. It is employed in the creation of new drugs and to learn about the novel drug development and design process. The complications of the molecular docking method include lead molecule optimization, biological pathway assessment, and de novo drug design [28–31]. With advances in MD, artificial intelligence, machine learning, and quantum computing, computational docking in drug development should have a bright future with prompt and relevant results. These contemporary techniques, such as drug repurposing and hybrid approaches, have improved drug discovery and adhered to repurposing workflows [32]. By adjusting medication selection based on each patient’s unique genetic profile, molecular docking is expected to play a significant role in personalized medicine and enhance therapeutic outcomes. High-throughput virtual screening pipelines and cloud-based docking platforms will further democratize drug discovery resources.
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Written By
Aastha Rana, Gagandeep Singh, Rohit Bhatia
Submitted: 31 August 2025Reviewed: 07 October 2025Published: 14 January 2026
Open access peer-reviewed chapter - ONLINE FIRST
