DFT-Driven Investigation of Molecular Reactivity and Docking-Based Binding Behavior of a Peptide-Like Organic Compound (C??H??N?O?)

Peptide-like organic molecules have attracted considerable attention in contemporary drug discovery due to their structural versatility, biocompatibility, and ability to engage in highly specific intermolecular interactions with biological targets. The presence of multiple heteroatoms, such as nitrogen and oxygen, in these compounds enhances their hydrogen-bonding capacity and electronic tunability, making them promising scaffolds for therapeutic applications. In this context, theoretical and computational approaches have emerged as powerful tools for probing the electronic structure and molecular reactivity of such systems prior to experimental validation. Density Functional Theory (DFT) has become one of the most widely employed quantum chemical methods for investigating the structural, electronic, and reactivity properties of organic and bioactive molecules. Through the analysis of frontier molecular orbitals, global reactivity descriptors, and molecular electrostatic potential (MEP) surfaces, DFT provides valuable insight into charge transfer behavior, chemical stability, and reactive sites governing intermolecular interactions. These parameters are particularly relevant for peptide-like compounds, whose biological activity is often dictated by subtle electronic effects and conformational preferences. In parallel, molecular docking techniques play a crucial role in predicting the binding affinity and interaction patterns of small molecules with target proteins. Docking simulations enable the identification of favorable binding conformations, key amino acid residues involved in ligand recognition, and the nature of stabilizing interactions such as hydrogen bonding, electrostatic forces, and hydrophobic contacts. When combined with DFT-derived electronic descriptors, docking studies offer a comprehensive framework for establishing structure– reactivity– binding relationships at the molecular level. The peptide-like organic compound with molecular formula C??H??N?O? represents a nitrogen-rich system with multiple functional groups capable of participating in strong noncovalent interactions. Despite its potential relevance in medicinal chemistry, a detailed theoretical investigation correlating its electronic structure with biological binding behavior remains unexplored. Therefore, the present study aims to perform an integrated DFT and molecular docking analysis to elucidate the molecular reactivity, electronic characteristics, and protein-binding mechanism of C??H??N?O?. The outcomes of this work are expected to provide fundamental insights into its bioactive potential and to support its further development as a viable scaffold in rational drug design.

REVIEW OF LITERATURE

Computational chemistry has become an indispensable component of modern molecular design, particularly in the investigation of bioactive organic and peptide-like molecules. Among the available theoretical methods, Density Functional Theory (DFT) has proven to be highly effective in describing the electronic structure, molecular stability, and reactivity of complex organic systems. Numerous studies have demonstrated that DFT-derived parameters such as frontier molecular orbital energies, global reactivity descriptors, and molecular electrostatic potential (MEP) maps provide reliable predictions of chemical behavior and interaction propensity in biologically relevant environments. Peptide-like molecules, characterized by the presence of amide linkages and multiple heteroatoms, have been widely explored due to their strong affinity toward biological macromolecules. Earlier investigations have shown that nitrogen- and oxygen-rich compounds exhibit enhanced hydrogen-bonding ability and conformational flexibility, which are critical factors governing molecular recognition and binding specificity. DFT studies on such systems have revealed that HOMO–LUMO energy gaps play a crucial role in determining molecular stability, while electronegativity, softness, and electrophilicity indices are closely associated with reactivity and biological activity. In parallel, molecular docking techniques have been extensively employed to explore protein–ligand interactions at the atomic level. Docking simulations enable the prediction of preferred binding orientations, interaction energies, and key amino acid residues involved in ligand stabilization within the active site of target proteins. Previous reports have highlighted that peptide-like and multifunctional organic compounds often display favorable docking scores due to the formation of multiple hydrogen bonds and electrostatic interactions, supported by hydrophobic contacts that enhance binding stability. Recent literature emphasizes the importance of integrating quantum chemical calculations with molecular docking to establish structure–activity and structure–binding relationships. Combined DFT and docking approaches have been successfully applied to a wide range of bioactive molecules, including enzyme inhibitors, anticancer agents, antimicrobial compounds, and DNA-binding ligands. Such integrated studies reveal that electronic properties predicted by DFT strongly influence docking behavior, thereby validating the use of theoretical descriptors as predictive tools in rational drug design. Despite significant progress in this area, many peptide-like organic molecules remain insufficiently explored at the theoretical level, particularly regarding the correlation between molecular reactivity and biological binding behavior. Compounds with complex functional architectures and high heteroatom content, such as those with the molecular formula C??H??N?O?, require detailed computational assessment to understand their interaction mechanisms and bioactive potential. Consequently, an integrated DFT and molecular docking investigation is essential to bridge this gap and to provide a comprehensive understanding of their electronic, reactive, and binding characteristics.

METHODOLOGY

Quantum Chemical Calculations (DFT)

The molecular structure of the peptide-like organic compound with molecular formula C??H??N?O? was initially constructed using standard molecular modeling tools and subsequently optimized using Density Functional Theory (DFT). All quantum chemical calculations were performed employing the Gaussian suite of programs. Geometry optimization was carried out using the Becke’s three-parameter hybrid functional combined with the Lee–Yang–Parr correlation functional (B3LYP) and the 6-31G(d,p) basis set. This level of theory was selected due to its proven reliability in describing the electronic structure and geometrical features of organic and bioactive molecules. Vibrational frequency analysis was conducted at the same level of theory to confirm that the optimized geometry corresponds to a true minimum on the potential energy surface, as evidenced by the absence of imaginary frequencies. Frontier molecular orbital (FMO) analysis was performed to obtain the energies and spatial distributions of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO). The HOMO–LUMO energy gap was used as an indicator of molecular stability and charge transfer capability. Global reactivity descriptors, including chemical hardness (η), softness (S), electronegativity (χ), chemical potential (μ), and electrophilicity index (ω), were calculated using Koopmans’ theorem based on HOMO and LUMO energies. In addition, the molecular electrostatic potential (MEP) surface was generated to identify electrophilic and nucleophilic regions, providing insight into potential interaction sites relevant for biological binding.

Molecular Docking Studies

Molecular docking simulations were performed to investigate the binding behavior of C??H??N?O? with a selected target protein. The three-dimensional crystal structure of the target protein was retrieved from the Protein Data Bank (PDB). Protein preparation involved the removal of co-crystallized ligands, water molecules, and ions, followed by the addition of polar hydrogen atoms and assignment of Kollman partial charges using Auto Dock Tools. The optimized ligand geometry obtained from DFT calculations was converted into the appropriate format, and Gasteiger charges were assigned prior to docking. Docking calculations were carried out using the Auto Dock 4.2 software package, employing the Lamarckian Genetic Algorithm (LGA) to explore possible ligand conformations within the protein active site. A grid box encompassing the active site residues was defined with appropriate dimensions and spacing to ensure sufficient conformational sampling. Multiple docking runs were performed to identify the most stable binding pose based on binding energy and cluster population analysis. The best-ranked docked complex was selected for detailed interaction analysis. Protein–ligand interactions, including hydrogen bonds, electrostatic interactions, and hydrophobic contacts, were analyzed using molecular visualization tools such as Discovery Studio Visualizer and PyMOL.

Correlation of DFT and Docking Results

To establish a structure–reactivity–binding relationship, the DFT-derived electronic descriptors were correlated with molecular docking results. Particular emphasis was placed on the role of HOMO–LUMO distribution and MEP-identified reactive sites in governing protein–ligand interactions. This integrated approach enabled a comprehensive understanding of how electronic properties influence binding affinity and interaction stability, thereby supporting the predictive capability of computational methods in rational drug design.

Table 1. Molecular Docking Parameters Used for Protein–Ligand Interaction Study

Optional Additional Table (if journal asks for clarity)

Table 2. Docking Output Analysis Criteria

Table 3. AutoDock Binding Energy Components for C??H??N?O?

Table 4. AutoDock Docking Results and Cluster Analysis

Table 5. Protein–Ligand Interaction Summary (Auto Dock Analysis)

Table 6. AutoDock Scoring Function Parameters

Table 7. Correlation between DFT Reactivity Descriptors and AutoDock Binding Parameters of C??H??N?O?