The Role of Density Functional Theory in the Design and Analysis of Heterocyclic Anticancer Agents: A Comprehensive Review

Case study: Imidazo[1’,2’:1,5]pyrazolo[3,4-b]pyridine Derivatives

Abdel-Rahman et al.^[15] employed DFT calculations to elucidate the electronic properties of dicarboxylate functionalized imidazo[1’,2’:1,5]pyrazolo[3,4-b]pyridine derivative 16, which demonstrated potent antiproliferative activity against HCT116 and MCF7 cancer cell lines. The optimized structure revealed a near-planar conformation, facilitating effective π-conjugation across the rings. The calculated highest occupied molecular orbital (HOMO) energy of −5.71 eV and lowest unoccupied molecular orbital (LUMO) energy of −2.55 eV yielded a HOMO–LUMO energy gap of 3.16 eV, indicating moderate chemical reactivity. This moderate gap supports the compound’s ability to form stable interactions with biological targets such as EGFR.

The electrostatic potential (ESP) map illustrated regions of varying electrostatic potential, with nitrogen atoms in the central ring exhibiting significant negative potential, suggesting their role as electron donors. This electron-donating nature, driven by the nitrogen atoms in the rings, aligned perfectly with molecular docking findings, in which the compound formed key interactions with EGFR residues, including a hydrogen bond with Met769, facilitated by the electron-rich nitrogen atom.

Pyranoquinoline systems

Abdel-Megid et al.^[16] synthesized and characterized a novel pyrano[3,2-c] quinoline-2,5-dione-based 1,2,4-triazine (HMTIPQ) using combined spectral characterization and DFT computational analysis. DFT calculations accurately predicted bond lengths and angles, validating experimental X-ray crystallography data and enabling detailed analysis of temperature-dependent optoelectronic properties.

1,2,3-Triazole glycoside hybrids

El-Bayaa et al.^[17] investigated benzimidazole-pyridine-triazole-glycosyl hybrid 17, finding that the optimized structure revealed a near-planar conformation between the benzimidazole and pyridine rings. This structural arrangement facilitated π-electron delocalization across the conjugated system, thereby enhancing the molecule’s ability to interact electronically with biological targets. The HOMO-LUMO energy gap of 3.70 eV suggested moderate chemical reactivity, enabling stable yet dynamic interactions with EGFR. Progressive analysis of the most potent glycosides (15, 17, and 19) revealed HOMO-LUMO energy gaps decreasing from 3.73 eV to 3.70 eV to 3.65 eV, reflecting enhanced molecular reactivity and electron transfer capabilities.

HOMO-LUMO gap as a reactivity indicator

The energy gap between HOMO and LUMO (ΔE = E_LUMO - E_HOMO) serves as a fundamental descriptor of molecular reactivity. Smaller gaps generally indicate higher reactivity and polarizability, while larger gaps suggest greater kinetic stability and lower reactivity. This relationship has been extensively validated across heterocyclic anticancer agents. Importantly, compounds with intermediate HOMO-LUMO gaps (2-4 eV) often exhibit an optimal balance between reactivity toward cancer cells and selectivity over normal cells, as the gap modulates both electrophilic attack susceptibility and metabolic stability.

In the study by Oladipo et al.^[18] on (E)-N’-(2-bromophenyl)-N-(2,6-diisopropylphenyl) formamidine, Frontier orbital analysis suggests FMO characteristics crucial for understanding inhibitory potential against CDK1 and CDK2. The HOMO orbitals, being predominantly composed of N p-orbitals and aromatic carbon p-orbitals, indicated the molecule’s p-orbital nature and electron-donating capacity through both the CN nitrogen and the OAc oxygens.

Metalloporphyrin complexes

Aljohani and Nasri^[19] performed a comprehensive FMO analysis of bis(tert-butyl isocyanide) iron(II) meso-tetra(para-bromophenyl) porphyrin complex. TD-DFT/B3LYP-D3 calculations yielded EHOMO = -4.93 eV and ELUMO = -2.32 eV, yielding an energy gap of 2.61 eV, consistent with those of similar metalloporphyrins. The HOMO was primarily localized around the Fe(II) center and the coordinated nitrogen atoms. At the same time, the LUMO was concentrated on nitrogen atoms and pyrrole groups, suggesting possible intermolecular charge transfer. The HOMO primarily comprised d-orbitals from the Fe(II) center hybridized with p-orbitals of coordinating pyrrole nitrogens. At the same time, the LUMO was dominated by π*-antibonding orbitals on the porphyrin ring, facilitating intramolecular charge transfer upon excitation.

Ruthenium complexes with purine derivatives

Hajji et al.^[20] conducted detailed computational studies of ruthenium complexes containing Cp, methyl-N-1,3,5-triaza-7-phosphaadamantane (mPTA), and natural purine bases. The HOMO for κS-complex was mainly constituted by 8-MTT-HOMO-orbitals that slightly overlapped through one of the S lobes (4% contribution) with one of the Ru-d orbitals (3% contribution), indicating weak Ru-S bonding. In contrast, the κN7-complex-HOMO had a nodal plane between the Ru-d and N7-p-orbital, resulting in an antibonding character for the Ru-N7-bond. These detailed orbital analyses explained the coordination preferences and relative stabilities of different binding modes.

Importantly, compounds with intermediate HOMO-LUMO gaps (2-4 eV) often exhibit an optimal balance between reactivity toward cancer cells and selectivity over normal cells, as the gap modulates both electrophilic attack susceptibility and metabolic stability.

Case study: Iron porphyrin complex

For complex 1 in the Aljohani and Nasri^[19] study, calculated global reactivity indices included:

• Ionization Potential: 4.93 eV

• Electron Affinity: 2.32 eV

• Chemical Potential: -3.626 eV

• Electronegativity: 3.626 eV

• Global Hardness: 1.306 eV

• Global Softness: 0.766 eV

• Electrophilicity Index: 5.034 eV

These values indicate that the complex is reactive, with hardness comparable to that of related cadmium(II) complexes but higher than that of magnesium complexes. The negative chemical potential confirmed electron-accepting properties, while the electrophilicity index suggested strong electron-attracting ability.

These global reactivity descriptors reveal recurring patterns: compounds with moderate hardness (1-3 eV) and high electrophilicity indices (>4 eV) frequently exhibit enhanced anticancer activity, suggesting that these parameters are valuable screening criteria for rational drug design.

An example of a HOMO–LUMO diagram is presented by Mkacher et al.^[23]

NBO

NBO analysis provides insights into molecular bonding, charge distribution, and intramolecular interactions. This approach decomposes the molecular wave function into localized bonding and antibonding orbitals, enabling identification of Charge transfer interactions, hyperconjugation effects, lone pair delocalization, and reactive centers.

Mouli et al.^[24] utilized NBO analysis alongside DFT to identify reactive sites within indole-pyrrole hybrid molecules, supporting the identification of bioactive regions and potential protein-ligand interaction sites.

Similarly, Langeswaran et al.^[22] employed NBO analyses to identify reactive sites in 4-fluorochalcone, facilitating understanding of its interaction with cervical cancer targets.

MEP

MEP maps visualize the three-dimensional distribution of electrostatic potential around molecules, revealing:

• Nucleophilic sites (electron-rich regions, typically red)

• Electrophilic sites (electron-deficient regions, typically blue)

• Neutral regions (typically green)

• Potential hydrogen bonding sites

These maps are particularly valuable for predicting non-covalent interactions with biological macromolecules.

An example of a MEP map is presented by Mkacher et al.^[23]

Applications in anticancer agent design

In the study by Aljohani and Nasri,^[19] MEP analysis of the iron porphyrin complex revealed a blue region around axial ligands, suggesting electrophilic reactivity. In contrast, yellow regions near Br atoms indicated nucleophilic sites. These electrophilic and nucleophilic properties suggested potential applications in sensor technologies and in enhancing the understanding of biological interactions. The MEP extrema (-0.0308 a.u. to +0.0308 a.u.) aligned with observed red-to-blue gradients and underscored the amphiphilic nature of the complex, in which ligands introduce polar, reactive pockets. Such a distribution suggested enhanced solubility and targeted reactivity in polar environments, such as aqueous biological media.

For ruthenium-purine complexes studied by Hajji et al.^[20] ESP maps showed that the negative potential was mainly located on the carbonyl oxygen and nitrogen of purine. In contrast, a positive ESP was distributed on PTA ligands. This distribution explained the observed coordination preferences and solvent effects on complex stability.

Ruthenium-purine complexes

Hajji et al.^[20] conducted comprehensive theoretical IR spectroscopy studies, revealing that traditional assignments for purine C=O groups were incorrect. Calculations showed that the highest frequency absorption band corresponds to ν(C2=O) rather than ν(C6=O), contrary to historical assignments. For [RuCp(8MTT-κS)(PPh₃)(mPTA)]⁺, calculated frequencies (ν(C2=O)) 1678 cm^-1, (ν(C6=O)) 1617 cm^-1, (ν(C=C+C=N)) 1528 cm^-1 showed excellent agreement with experimental values (ν(C2=O)) 1673 cm^-1, (ν(C6=O)) 1628 cm^-1, ν(C=C+C=N) 1522 cm^-1. However, the strong similarity between the IR spectra of κS- and κN7-complexes indicated that IR spectroscopy alone cannot reliably differentiate coordination sites in these systems.

Phenothiazine derivatives

Bhardwaj et al.^[25] performed molecular docking analysis and spectroscopic investigation of 2-(Trifluoromethyl)phenothiazine using FT-IR and UV-Vis methods. DFT calculations successfully predicted experimental spectra, validating the computational approach and supporting structural assignments.

Hydrazinyl sugar conformations

El-Enazy et al.^[26] employed DFT at the B3LYP/6-31G(d) level to optimize Z- and E-conformations of hydrazinyl sugar 9 and calculate hydrogen chemical shifts at B3LYP/6-311+G(2d,p) level using Gaussian. Results showed a distinctive shift at 12 ppm appearing only in the more stable Z-structure, attributed to the NH group forming a hydrogen bond with an OH group—a feature absent in the E-conformation. This finding corroborated experimental peaks around 11.00 ppm due to NH of the Z-structure and agreed with reported structures of the same type, confirming Z-form formation.

Badran et al.^[27] utilized GIAO-NMR calculations alongside other DFT descriptors to characterize 4-hydroxy-1-methylquinolin-2(1H)-one-tethered heterocycles, relating electronic properties to anticancer activity against HepG-2 cells.

Porphyrin complexes

In the Aljohani and Nasri^[19] study, TD-DFT results indicated the first excited state at 2.1817 eV (568.28 nm) with an oscillator strength of 0.0683. While this differed from the experimental optical gap energy of 2.004 eV, the discrepancy was attributed to calculations using an isolated gas-phase molecule versus experimental measurements in dichloromethane solution. This comparison highlighted the importance of considering solvation effects in spectroscopic predictions.

Quinoxaline derivatives

Sushma et al.^[28] investigated quantum chemical and photophysical properties of 9-Chloro-2,6-Dimethyl-10-(Methylsulfanyl)Quinoxaline, demonstrating excellent correlation between calculated and experimental UV-Vis spectra, supporting the reliability of DFT for predicting optical properties of heterocyclic compounds.

Cycloaddition reactions

Adomako et al.^[31] employed M06/6-311G(d,p) DFT to comprehensively map the (3+2) cycloaddition of diarylnitrone derivatives with 1-(4-nitrophenyl)-5H-pyrrolin-2-one. The study determined complete chemo-, regio-, and stereoselectivity profiles for the formation of isoxazolidine heterocycles, which are known to display anticancer activity. The calculations revealed energy barriers, transition-state geometries, and electronic reorganization during bond formation, thereby explaining the observed product distributions and selectivity patterns.

This mechanistic understanding enabled the rational design of synthetic strategies to access desired stereoisomers with potential anticancer properties.

Histone Deacetylase (HDAC) inhibitor mechanisms

Khatun et al.^[32] comprehensively reviewed DFT applications in HDAC-based chemotherapeutics, describing how calculations elucidate:

• Zinc binding mechanisms in the HDAC active site

• Transition states for deacetylation reactions

• Electronic structure changes during catalysis

• Selectivity determinants for different HDAC isoforms

• Inhibitor binding modes and energetics

These mechanistic insights guide the rational design of more selective and potent HDAC inhibitors for cancer therapy.

Platinum-based anticancer drugs

Sahadevan et al.^[33] employed quantum mechanical approaches and molecular docking analysis of platinum metal-based anticancer drugs Lobaplatin and Heptaplatin targeting cancer DNA. DFT studies provided insights into structural properties, reactivity, and mechanisms of DNA interaction, comparing these agents and explaining their distinct biological profiles.

Ruthenium-purine complex coordination

Hajji et al.^[20] conducted an elegant study of solvent effects on 8-mercapto-the-ophylline (8MTT) coordination to ruthenium centers. In the gas phase, κS-complexes containing PTA were somewhat more stable than corresponding κN-complexes, while reverse stability was found for complexes with cationic mPTA. However, solvent calculations using the conductor-like polarizable continuum model (CPCM) revealed dramatic effects:

For [RuCp(8MTT)(PTA)₂] in water, the κS-complex showed much more negative solvation free energy than the κN7-complex, making κS coordination strongly preferred, which arose from different charge distributions and resulting dipole moments (κS: 15.73 D; κN7: 7.1 D). The κS-complex’s larger dipole moment led to stronger water-dipole interactions, increasing the Gibbs free energy difference and favoring κS-complex stability in water.

For mPTA-containing complexes, different distance relationships between negative purine oxygens/nitrogens and positive mPTA charges resulted in opposite solvation effects, making κS-complexes thermodynamically more stable in both water and ethanol despite gas-phase preferences for κN7 coordination.

These calculations explained experimental observations and demonstrated DFT’s ability to predict solvent-dependent coordination chemistry relevant to drug behavior in biological environments.

DFT treatment of organometallic systems requires careful consideration of factors less critical for purely organic heterocycles. Metal-containing complexes necessitate specialized basis sets (e.g., LanL2DZ, stuttgart/ dresden (SDD), def2-TZVP) to account for relativistic effects, particularly for heavy metals. Hybrid functionals like B3LYP perform adequately for organic systems, but the M06 family of functionals often provides superior accuracy for metal coordination geometries and weak interactions in organometallic anticancer agents.

Imidazo[1,2-b][1,2,4]triazine Derivatives

Alhamzani et al.^[34] synthesized imidazo[1,2-b][1,2,4]triazine-based pyrimidine derivatives as selective PI3Kα inhibitors. DFT calculations of electronic properties, combined with cytotoxicity, docking, and ADMET data, guided substituent selection to improve selectivity and potency.

Thieno[3,2-b]pyrrole scaffolds

Goel et al.^[21] investigated thieno[3,2-b]pyrrole as a privileged scaffold for anticancer agents. DFT analysis of electronic parameters helped identify key structural features linked to anticancer activity and informed synthetic strategies.

Quinazolinone-oxadiazole hybrids

Miriyala et al.^[35] developed quinazoline-4(3H)-one derivatives with 1,2,4- and 1,3,4-oxadiazole moieties. DFT, together with docking and ADME/Tox predictions, confirmed strong anticancer activity against breast and lung cancer cell lines and supported lead optimization.

Indole-pyrrole hybrids

Mouli et al.^[24] conducted a comprehensive in silico characterization of indole-substituted densely functionalized pyrrole against breast cancer, integrating DFT, molecular docking, MD simulations, and ADME analysis.

Electronic structure calculations demonstrate electronic properties that correlate with observed cytotoxicity, while combined computational approaches validated and explained selectivity across various cancer cell lines.

Phenolic hydrazones

Xing et al.^[36] synthesized novel heterocyclic phenolic hydrazone-based derivatives, employing B3LYP/def2-SVP DFT for geometry optimization, FMO analysis, MEP mapping, and reactive site identification. These calculations rationalized the higher anticancer activity of compounds L3/L4 and supported docking-based binding affinity predictions. Drug-likeness studies complemented DFT findings, creating a comprehensive SAR understanding.

Triazole hybrids

Al Sheikh Ali et al.^[37] designed 4-(1,2,4-triazol-3-ylsulfanylmethyl)-1,2,3-triazole derivatives with DFT calculations of HOMO/LUMO energies, energy gaps, and global reactivity descriptors explaining the superior anticancer potency of the most active compounds. Softness, hardness, electronegativity, and electrophilicity indices correlated with experimental IC₅₀ values, enabling predictive SAR models.

Hybrid molecule design

DFT guides the combination of different heterocyclic scaffolds to enhance pharmacological profiles while reducing toxicity.

Benzimidazole-triazole-pyridine glycosides

El-Bayaa et al.^[17] synthesized benzimidazole-triazole-pyridine glycoside conjugates as anticancer agents. DFT analysis of electronic descriptors for the most potent glycosides (15, 17, 19) revealed correlations between HOMO-LUMO gaps and cytotoxic profiles. Progressive decreases in the gap (3.73 eV → 3.70 eV → 3.65 eV) reflected enhanced molecular reactivity, facilitating favorable EGFR interactions. Dipole moments (0.8515 D → 2.63 D → 2.93 D) suggested that increased polarity improved aqueous solubility and electrostatic complementarity with EGFR polar residues. These descriptors rationalized SAR and highlighted the role of glycosyl deprotection in modulating electronic properties to amplify target-specific cytotoxicity.

Quinolinyl-thiazole hybrids

Dhongade et al.^[38] developed novel quinolinyl-thiazole hybrid candidates bearing N-methyl piperazine as potential anti-breast cancer agents. Synthesis, biological evaluation, and computational studies, including DFT calculations, identified optimal structural combinations for enhanced activity and selectivity.

Across the diverse scaffolds examined, DFT-guided optimization strategies consistently identify that electron-withdrawing substituents modulate HOMO-LUMO gaps toward optimal ranges (2-4 eV). At the same time, the strategic placement of hydrogen bond donors/acceptors enhances target selectivity. However, some discrepancies between DFT predictions and experimental outcomes highlight the need for validation through docking and biological testing.

EGFR targeting

Multiple studies have employed DFT-optimized structures for EGFR docking:

• Oladipo et al.^[18] used DFT-optimized (E)-N’-(2-bromophenyl)-N-(2,6-diisopropylphenyl)formamidine structures to predict binding affinities and interaction profiles with CDK1 and CDK2.

• Abdel-Wahab et al.^[39] synthesized bis(thienylpyrazolyl)carbohydrazide derivatives as multifunctional agents, employing DFT-optimized structures for docking studies that validated observed bioactivity.

• Perinbaraj et al.^[40] designed novel potent oxindole derivatives as VEGFR2 inhibitors using DFT optimization, drug-likeness assessment, and structural dynamics studies, demonstrating how computational workflow identifies promising candidates before synthesis.

Multi-target approaches

Alhamzani et al.^[34] employed DFT-optimized structures to study PI3Kα selective inhibitors, while Miriyala et al.^[35] validated quinazolinone-oxadiazole hybrids against multiple cancer-related targets. These studies demonstrate DFT’s versatility in supporting polypharmacology approaches to cancer therapy.

Binding stability assessment

El-Bayaa et al.^[17] demonstrated integration of DFT with MD simulations for benzimidazole-triazole-pyridine glycosides. DFT calculations identified electron-donating sites (particularly the nitrile group), which formed persistent hydrogen bonds with Met769 of EGFR in docking studies. MD simulations confirmed these interactions persisted for 97% of the 100 ns simulation, validating DFT predictions and explaining observed antiproliferative potency.

Abdel-Rahman et al.^[15] showed that nitrogen-mediated hydrogen bonds identified by DFT HOMO analysis persisted in 73% of 100 ns MD simulations, demonstrating how DFT predictions translate into dynamic binding stability.

Dynamic interaction profiles

Mouli et al.^[24] integrated DFT, molecular docking, and MD simulations for indole-pyrrole hybrids, revealing how DFT-identified reactive sites maintain interactions throughout dynamic simulations. This comprehensive approach validated observed cytotoxicity and selectivity patterns.

1,2,4-Triazines

Abdel-Megid et al.^[16] extensively studied 1,2,4-triazine-containing pyranoquinoline derivatives using DFT to predict electronic structures supporting anticancer agent development. Temperature-dependent calculations of optoelectronic properties revealed how changes in electronic structure affect biological activity across physiological conditions.

Pyridine derivatives

Multiple studies have employed DFT to characterize pyridine-containing anticancer agents:

• El-Bayaa et al.^[17,41] synthesized pyridyl-glycosyl hybrids and benzimidazole-pyridine-triazole glycosides, using DFT to understand electron distribution and reactivity patterns governing EGFR inhibition.

• El-Bakri et al.^[42] designed novel isoquinoline derivatives using DFT for geometry refinement, FMO energies, MEP, NBO, and NLO properties, linking electronic structure to predicted anticancer potential through docking to Tdp1 and EGFR.

Thiazoles

Sharma et al.^[43] developed novel Schiff base-thiazole derivatives, exploring antimicrobial and anticancer activity through spectroscopic analysis, DFT calculations, and molecular docking studies. DFT provided electronic structure insights supporting biological activity predictions.

Hussein et al.^[44] synthesized thiazole derivatives (pyrazole-thiazole hybrids) using DFT to obtain optimized structures and minimum-energy conformations for docking to tyrosine kinase, supporting anticancer activity interpretation.

Benzoxazoles

Avuthu et al.^[45] investigated triazole-thiadiazole/benzo[d]oxazole scaffolds using DFT calculations to deduce molecular structures, topologies, and electronic distributions of the most active molecules, supporting SAR with breast cancer cell results.

Majhi^[30] analyzed spectroscopic and electronic properties of benzoxazole derivatives exhibiting notable anticancer activity through comprehensive DFT studies.

4-Hydroxyquinolones

Bouone et al.^[46] investigated anticancer activity of modified 4-hydroxyquinolone analogues through in vitro and in silico studies. B3LYP/6-31G(d,p) DFT provided molecular descriptors (HOMO, LUMO, ΔE, hardness, softness, electrophilicity) interpreting electron density/reactivity relationships with anticancer activity.

Badran et al.^[27] synthesized 4-hydroxy-1-methylquinolin-2(1H)-one-tethered heterocycles, employing B3LYP/6-311++G(d,p) DFT for optimization, FMO analysis, global reactivity indices, MEP, Fukui functions, GIAO-NMR, and NLO properties. Small gap/softness values correlated with higher anticancer activity and favorable topoisomerase IIβ docking scores.

Oxindole derivatives

Perinbaraj et al.^[40] designed novel potent oxindole derivatives as VEGFR2 inhibitors for cancer therapy through computational insights from molecular docking, drug-likeness assessment, DFT, and structural dynamics studies. DFT calculations elucidated the electronic and dynamic properties, supporting the potential of this selective anticancer agent.

Thienopyrimidines

Mohamed et al.^[47] synthesized new aggregation-induced emission luminogens based on thieno[2,3-d]pyrimidine moiety using DFT to analyze molecular packing, electronic transitions, and how aggregation and intermolecular interactions control luminescence. Optimized structures were docked as CDK2 inhibitors, demonstrating dual optical and therapeutic potential.

Thiophene derivatives

Abhijith et al.^[48] conducted quantum computational, solvation, and in silico biological studies of a potential anti-cancer thiophene derivative using DFT (Gaussian09) to examine solvated/unsolvated electronic structure, optical properties, and topological electron density. These structures were docked to brain cancer targets (6ETJ, 6YPE).

Indole-pyrrole hybrids

Mouli et al.^[24] performed comprehensive in silico characterization of indole-substituted densely functionalized pyrrole against breast cancer, integrating DFT, molecular docking, MD simulations, and ADME analysis.

DFT calculations elucidated electronic and dynamic properties supporting selective anticancer agent potential, while NBO analysis identified bioactive regions and protein-ligand interaction sites.

Peptide-Indole Conjugates

Küçükbay et al.^[49] employed B3LYP/6-311+G(d,p) DFT to optimize molecular geometry and analyze electronic properties of new indole-based heterocycles. These structures fed EGFR docking studies, helping rationalize anticancer IC₅₀ values and supporting SAR development.

Platinum complexes

Sahadevan et al.^[33] used quantum mechanical approaches for Lobaplatin and Heptaplatin, providing insights into structural properties, reactivity, and DNA interactions through comparative DFT analysis.

Kaszuba et al.^[50] determined preferred coordination geometries and ligand arrangements around Pt(II) in lipophilic complexes containing triazole-containing bicyclic ligands. DFT comparisons of possible isomers explained the spectroscopic data and distinct anticancer behavior relative to cisplatin.

Ruthenium complexes

Hajji et al.^[20] extensively studied Ru complexes containing Cp, mPTA, and natural purine bases, evaluating antiproliferative activity, solubility, and redox properties. Computational analysis of ligands and complexes revealed coordination preferences, electronic structures, and solvent effects governing biological activity.

Antimony complexes

Mittal et al.^[51] investigated Sb(III) derivatives containing heterocyclic and N-alkyl-N-phenyl dithiocarbamate moieties. DFT optimized geometries (two candidate octahedral arrangements), computed HOMO-LUMO energies, and supported assignment of anisobidentate coordination, linking structure to observed anticancer and antimicrobial activities.

Iron porphyrin complexes

Aljohani and Nasri (2025)^[19] characterized bis(tert-butyl isocyanide) iron(II) meso-tetra(para-bromophenyl) porphyrin complex through comprehensive DFT/TD-DFT/MEP and non-covalent interaction (NCI)-reduced density gradient (RDG) investigations plus molecular docking. DFT optimization at the B3LYP-D3/LanL2DZ level showed excellent agreement with X-ray structures (bond length differences within 0.02 Å). Global reactivity indices and frontier orbital analysis explained the complex’s anticancer potential and apoptosis-inducing properties.

Silver N-heterocyclic carbene (NHC) complexes

Şahin-Bölükbaşı et al.^[52] reviewed silver(I)-(NHC) complexes challenging cancer, summarizing DFT applications across studies: geometry optimization, electronic structure (HOMO-LUMO), metal-ligand bonding, and SAR supporting cytotoxicity profile explanations.

Hassan et al.^[53] synthesized N-heterocyclic carbene selenium compounds (benzimidazolium/Se-NHC) with DFT supporting structural assignments and frontier orbital analysis, which, together with docking, rationalized the strong anticancer activity of lead Se-NHC C1.

Yaqoob et al.^[54] employed B3LYP/6-31G(d) DFT before synthesis to compute HOMO-LUMO gaps, spectra, dipole moments, and “biological potential” scores for selenium N-heterocyclic carbene compounds, guiding which NHC-selenium heterocycles to synthesize and test for anticancer activity.

Rhénium complexes

Matlou et al.^[55] studied Re(I) tricarbonyl complexes with imidazole-type ligands using B3LYP-GD(BJ)/6-311++G(d,p)/LanL2DZ DFT for full geometry optimization, HOMO-LUMO analysis, binding energies, and quantum theory of atoms in molecules (QTAIM) topological analysis, elucidating stability and bioactivity underlying docking-predicted anticancer effects.

The case studies collectively demonstrate that DFT provides actionable insights across all major heterocyclic classes. Nitrogen-rich heterocycles benefit from FMO and reactivity analysis; oxygen-containing systems from conformational studies; sulfur heterocycles from charge distribution mapping; and metal complexes from comprehensive electronic structure characterization. Notably, convergent findings emerge: planar geometries, moderate LUMO energies, and strategic heteroatom placement consistently correlate with enhanced anticancer activity.

Theoretical foundation

The RDG is calculated by:

$\text{RDG}\left(r\right)=\left(\text{1}/\left(\text{2}{\left(\text{3}{\pi }^{\text{2}}\right)}^{(\text{1}/\text{3})}\right)\right)\times \left[\right|\nabla \rho \left(r\right)|/\rho {\left(r\right)}^{(\text{4}/\text{3})}]$

where ρ(r) is the electron density and ∇ρ(r) is its gradient. Low-density gradient regions indicate weak interactions, while high-density gradient regions indicate strong interactions. The sign of the second eigenvalue (λ₂) of the electron density Hessian matrix, combined with ρ, classifies interactions:

• Negative (λ₂)ρ: attractive interactions (e.g., hydrogen bonds)

• Positive (λ₂)ρ: repulsive interactions (steric clashes)

• (λ₂)ρ ≈ 0: weak van der Waals interactions

Applications

Aljohani and Nasri (2025)^[19] performed NCI-RDG analysis for iron porphyrin complex 1. RDG plots showed red regions indicating steric repulsion within phenyl groups of TBrPP porphyrinate and green regions indicating van der Waals interactions between axial ligands and the rest of the molecule. This analysis provided a detailed understanding of the intramolecular forces that affect complex stability and geometry.

Mehdi Zaidi et al.^[56] employed NCI analysis alongside DFT for C5N2 2D material as a drug carrier for cisplatin, carmustine, and mechlorethamine. PBE0-D3BJ/def2-SVP calculations of interaction energies, NCI, QTAIM, and ELF properties revealed binding and release mechanisms of classical anticancer drugs on the substrate.

Bond critical points

QTAIM identifies bond critical points (BCPs) where the gradient of electron density vanishes, characterizing bonds through:

• Electron density at BCP (ρ_BCP)

• Laplacian of electron density (∇ ²ρ_BCP)

• Bond ellipticity

• Energy density

Applications in drug carrier studies

Zaidi et al.^[56] used QTAIM alongside interaction energy calculations and NCI analysis to understand drug binding to C5N2 substrates. QTAIM analysis revealed the nature and strength of drug-substrate interactions, informing the design of targeted drug delivery systems.

Matlou et al.^[55] employed QTAIM topological analysis for Re(I) tricarbonyl complexes, elucidating metal-ligand bonding characteristics and explaining observed stability patterns.

QTAIM analysis thus directly informs selectivity predictions: compounds forming stronger, more covalent bonds with cancer cell targets than with normal tissue components exhibit enhanced therapeutic indices.

Comprehensive screening pipelines

Typical workflow:

• 1.

  DFT: geometry optimization, electronic property calculation

• 2.

  Docking: binding affinity prediction, interaction mapping

• 3.

  MD: binding stability assessment, conformational dynamics

• 4.

  ADMET: pharmacokinetic property prediction, toxicity screening

Case studies

Alhamzani et al.^[34] demonstrated this integrated approach for imidazo[1,2-b][1,2,4]triazine-based pyrimidine derivatives as PI3Kα inhibitors. DFT calculations informed electronic properties, docking predicted binding modes, and ADMET studies assessed drug-likeness, creating comprehensive candidate profiles before synthesis.

Oladipo et al.^[18] combined structural studies, DFT computational analysis, and inhibitory potential assessment of formamidine derivatives against CDK1 and CDK2, demonstrating synergy between experimental and computational approaches.

Miriyala et al.^[35] integrated DFT, docking, and ADME/Tox predictions for quinazolinone-oxadiazole hybrids, successfully predicting and validating anticancer potential against breast and lung cancer cell lines.

Spectroscopic validation

Multiple studies demonstrated a strong correlation between DFT-predicted and experimental spectra:

• Abdel-Megid et al.^[16] achieved excellent agreement between calculated and experimental IR, UV-Vis, and NMR spectra for pyran quinoline derivatives.

• Bhardwaj et al.^[25] validated DFT predictions through experimental FT-IR and UV-Vis measurements of phenothiazine derivatives.

• Sushma et al.^[28] demonstrated quantum chemical predictions accurately reproduced experimental photophysical properties of quinoxaline derivatives.

Crystallographic validation

X-ray structures provide ultimate validation of DFT-optimized geometries:

• Aljohani and Nasri^[19] showed that calculated bond distances agreed with experimental values within 0.02 Å for iron porphyrin complexes.

• Hajji et al.^[20] obtained crystal structures for ruthenium-purine complexes, finding calculated structures in good agreement despite systematic ∼5% overestimation of Ru-P and Ru-S bond lengths—a known artifact at B3LYP/DZVP and B3LYP/LANL2DZ levels.

Activity correlation

The strongest validation comes from correlating computational predictions with biological activity:

• Xing et al.^[36] demonstrated that DFT-derived reactivity descriptors correlated with experimental antioxidant and anticancer activities.

• Al Sheikh Ali et al.^[37] showed global reactivity descriptors predicted relative potencies of triazole derivatives, validated by IC₅₀ measurements.

• El-Bayaa et al.^[17] found HOMO-LUMO gaps and dipole moments correlated with cytotoxic potencies across glycoside series.

Stacking energy calculations

Kaneti et al.^[57] performed large-scale DFT calculations (ωB97XD/6-31G(d,p)) to compute stacking energies of quinazoline and per imidine heterocycles with model G-quadruplex structures. These quantum-calculated binding affinities served as QSAR descriptors and correlated with experimental melanoma cell line IC₅₀ values. This approach demonstrated DFT’s ability to model specific DNA interactions beyond traditional base pairing, enabling the rational design of G-quadruplex-selective ligands.

C5N2 substrate systems

Mehdi Zaidi et al.^[56] employed PBE0-D3BJ/def2-SVP DFT for full optimization of cisplatin, carmustine, mechlorethamine, C5N2 sheets, and their complexes. Comprehensive analyses of interaction energies, NCI, QTAIM, and ELF properties illuminated the binding and release mechanisms of classical anticancer drugs, informing the design of targeted drug delivery systems. Electronic property calculations revealed how drug loading affects carrier properties, enabling optimization of release kinetics.

Electronic transitions in aggregates

Mohamed et al.^[47] used DFT to analyze how molecular packing and intermolecular interactions control luminescence in thieno[2,3-d]pyrimidine-based systems. Electronic transition calculations explained aggregation-induced emission behavior, while optimized structures enabled evaluation of anticancer potential through CDK2 docking. This dual-function approach exemplifies how DFT guides the design of theragnostic agents.

Ferrocene and iron complexes

Iron-based compounds offer advantages over platinum agents, including reduced toxicity and different mechanisms of action. DFT studies characterize metal-ligand bonding, redox properties, and DNA-binding modes distinct from those of classical platinum drugs.

Ruthénium polypyridyl complexes

Hajji et al.^[20] demonstrated how DFT elucidates coordination preferences, solvent effects, and electronic structures governing antiproliferative activity of ruthenium complexes. Calculations predicted coordination-site preferences that were experimentally validated, illustrating DFT’s predictive power for organometallic drug design.

Machine learning integration

Emerging trends combine DFT-calculated descriptors with machine learning for accelerated drug discovery.

Descriptor-based QSAR

DFT-derived properties serve as features for machine learning models predicting biological activity:

• HOMO-LUMO gaps

• Global reactivity indices

• Atomic charges

• Molecular dipole moments

• Stacking energies

Kaneti et al.^[57] pioneered this approach with G-quadruplex ligands, using DFT-calculated stacking energies in QSAR models correlating with IC₅₀ values. This methodology enables rapid virtual screening of large chemical libraries before DFT optimization and experimental validation.

However, ML-DFT integration faces significant challenges, including limited training data for specialized heterocyclic scaffolds, difficulty in validating predictions against experimental outcomes, and scalability constraints when screening large chemical spaces. Robust validation protocols coupling computational predictions with systematic experimental testing remain essential.

Trade-offs

Computational cost increases dramatically with basis set size and functional complexity. Optimal selection balances accuracy with feasibility:

• For routine geometry optimization: B3LYP/6-31G(d,p) provides good accuracy at moderate cost.

• For high-accuracy energetics: B3LYP/6-311++G(d,p) or M06-2X/6-311++G(d,p).

• For metal complexes: Effective core potentials (LanL2DZ, Stuttgart RSC) combined with all-electron basis sets for ligands.

• For weak interactions: Include dispersion corrections (D3, D3BJ) or use functionals like ωB97XD.

Validation through method comparison

Sophisticated studies employ multiple methods:

• Adomako et al.^[31] selected M06/6-311G(d,p) for cycloaddition mechanism studies after validation against higher-level methods.

• Aljohani and Nasri^[19] used B3LYP-D3/LanL2DZ, incorporating dispersion corrections essential for modeling stacking and weak interactions in porphyrin systems.

Continuum models

CPCM and PCM (Polarizable Continuum Model) represent solvents as dielectric continua:

Hajji et al.^[20] demonstrated dramatic solvent effects on ruthenium-purine complex coordination preferences using CPCM, explaining experimental observations and revealing that gas-phase calculations alone were insufficient.

Explicit solvation

For strong solute-solvent interactions (hydrogen bonding), explicit water molecules may be necessary, though this dramatically increases computational cost.

Multi-level validation

Robust studies employ multiple validation approaches:

• Spectroscopic validation: Compare calculated vs. experimental IR, NMR, UV-Vis spectra

• Crystallographic validation: Compare optimized geometries vs. X-ray structures

• Activity correlation: Relate calculated descriptors to experimental IC₅₀ values

• Method benchmarking: Compare multiple functionals/basis sets

Error analysis

Systematic errors should be recognized and discussed:

• Metal-ligand bond length overestimation (typically ∼5%)

• Gas-phase vs. solution differences

• Basis set superposition error (BSSE)

• Spin contamination in open-shell systems

Resource management

Large-scale studies require strategic resource allocation:

• Pre-screening: Use cheaper methods (PM7, HF/6-31G) for initial screening

• Progressive refinement: Optimize geometries at modest levels, then single-point calculations at higher levels

• Parallel computing: Leverage multiple processors for frequency calculations and TD-DFT

• Fragment approaches: For very large systems, model active sites while fixing the remainder

Simplified models

DFT studies model isolated molecules or simple complexes, while biological activity depends on:

• Cellular uptake and distribution

• Metabolic transformations

• Off-target effects

• Pharmacokinetic factors

• Tumor microenvironment

Computational predictions must be validated experimentally, and DFT cannot replace biological testing.

Emergent properties

Anticancer activity emerges from complex interactions across multiple scales that DFT alone cannot capture. Integration with systems biology and pharmacological modeling remains necessary for a complete understanding.

Deep learning for property prediction

Training neural networks on DFT-calculated descriptors enables rapid property prediction for millions of compounds:

• Activity prediction without explicit DFT calculations

• Virtual screening acceleration

• Lead optimization guidance

Active learning workflows

Iterative cycles of:

• ML model predictions

• DFT calculations for selected candidates

• Model retraining

• Experimental validation

This approach maximizes information gain while minimizing computational cost.

Key challenges include developing transfer-learning approaches to leverage data across related heterocyclic families, establishing uncertainty quantification methods for ML-DFT predictions, and creating interpretable models that retain chemical insight rather than functioning as black boxes.

Meta-dynamics and umbrella sampling

Combining DFT with enhanced sampling techniques enables exploration of:

• Rare conformational events

• Binding/unbinding pathways

• Drug-target association mechanisms

• Metabolic transformation pathways

QM/MM methodologies enable accurate treatment of drug-protein interactions by combining quantum-mechanical precision at the active site with molecular-mechanics efficiency in the protein environment, which is essential for modeling covalent drug-target interactions and metallodrug mechanisms.

QM/MM for protein-ligand systems

Treating binding sites with DFT while modelling protein environment with force fields provides unprecedented accuracy for:

• Binding energy decomposition

• Polarization effects

• Charge transfer during binding

• Covalent inhibitor mechanisms

DFT in ONIOM schemes

Layered calculations treat critical regions at high DFT levels while using cheaper methods for the surroundings, enabling the study of larger, more realistic systems.

Photodynamic therapy agents

TD-DFT advances enable the design of:

• Singlet oxygen generators

• Two-photon absorbing agents

• Fluorescent trackers with therapeutic activity

• Light-activated prodrugs

Computational photochemistry

Mapping excited-state potential energy surfaces reveals photochemical reaction pathways relevant to light-activated anticancer agents.

Multi-target drug design

DFT-guided design of compounds simultaneously modulating multiple targets:

• Kinase inhibitors with tuned selectivity profiles

• Epigenetic modulators affecting multiple enzymes

• Compounds targeting both cancer cells and the microenvironment

Network pharmacology integration

Combining DFT predictions with systems biology approaches to understand:

• Drug effects on cellular networks

• Synergistic combinations

• Resistance mechanisms

• Personalized medicine strategies

Emerging quantum algorithms

Quantum computers promise exact solutions to electronic structure problems that are currently intractable:

• Large molecule ground states

• Strongly correlated systems

• Excited state manifolds

• Real-time dynamics

While practical applications await advances in hardware, DFT will likely bridge classical and quantum computational chemistry for drug discovery.