Structural insights and ADMET analysis of CAFI: hydrogen bonding, molecular docking, and drug-likeness in renal function enhancers

Abstract

Using quantum chemical calculations, spectroscopic methods, and molecular docking analysis, this work explores the electronic, structural, vibrational, and biological characteristics of CAFI. Intramolecular hydrogen bonding between the methyl and C = O groups (with bond lengths less than 3 Å) was detected, affirming molecular stability. Corresponded with the theoretical expectations, FT-IR and UV spectra corroborating CAFI’s chemical stability. Frontier molecular orbital study indicated HOMO-LUMO energy gaps between 4.227 eV (gas) and 4.792 eV (ethanol), underscoring charge transfer activity. Molecular docking revealed CAFI as the most potent binder to proteins that stimulate kidney function, with a binding energy of -4.08 kcal/mol and sustained hydrogen bonding connections. ADMET analysis confirmed CAFI’s drug-likeness, indicating advantageous absorption, distribution, metabolism, and toxicity characteristics. These findings indicate CAFI as a potential treatment candidate for the regulation of renal function.

Introduction

Chronic kidney disease (CKD) is a acute public health concern since it impairs excellence of life and increases the hazard of morbidity and early demise, especially in the elderly. Because CKD negatively affects people’s standard of life, it presents a significant threat to public health. CKD and its associated consequences have significantly increased morbidity and death in recent years. Caffeine is an alkaloid molecule that is easily originate in various beverages, such as coffee, tea, carbonated drinks, and soft drinks like cola and Pepsi [1]. To alleviate tension and anxiety in daily life, there is an increase in the consumption of caffeine [2]. Additionally, the ingestion of caffeine can enhance cognitive function and alleviate anxiety [3]. It exerts a more significant influence on one’s total well-being. Caffeine has a chemical structure that closely resembles that of adenosine [4]. Consuming a greater amount of caffeinated beverages is linked with a 3% reduction in the likelihood of developing CKD [5]. Habitual moderate caffeine use is advantageous for cognitive performance in people undergoing hemodialysis, as it improves attention and attentiveness [6]. From this, we can say that further investigation is needed to confirm the study. So, the present investigation, it evaluates the kidney stimulant function of caffeine using DFT by studying their characteristics. The current study assesses the antiallergic of CAFI, a molecule consisting of a Methyltheophylline ring. The study demonstrates that the compounds have strong kidney disease and are biocompatible with the right protein receptors. To the best of our understanding, the theoretical investigation and research in biology on CAFI have not yet been made available. Owing to a gap in the literature, a serious endeavour has been made to conceptually investigate CAFI. According to cutting-edge discoveries in the field of medicine, the relationship connecting biological function and geometry is essential for hastening the creation of new drugs. A compound’s stable structure, vibrational preference, interaction behavior, and reactivity sites can all be learned a great deal from DFT simulations [7, 8]. We can uncover new therapeutics and develop practical applications for drug design by using the local reactivity descriptors to better understand the relationships involving physically favourable molecular structures along with material research as well as biological processes. Spectroscopic approaches can provide techniques for investigating the proper structure and contact of several dynamic physical systems. The CAFI molecule’s connections to protein-ligand’s binding energy also RMSD value were investigated using docking and MD simulation; the findings corroborate pharmacological activity. The assessment of potential medications from various databases also uses a drug similarity prediction. Using theoretical techniques, the study examines the electrical structure and stability of caffeine with a focus on its biological consequences. According to the study’s hypothesis, caffeine’s biological activity specifically, its drug-likeness and pharmacokinetic behavior strongly correlates with its electrical characteristics as determined by the NBO and FMO methodologies. The goal of combining solvent effects with ADMET predictions is to offer a thorough framework for comprehending the molecular interactions of caffeine and its possible significance in medication development. The stability and reactivity metrics derived from HOMO-LUMO and NBO analyses correspond to caffeine’s biological role as a psychoactive and metabolic stimulant. Experimental validation through docking and enzymatic assays can strengthen these theoretical findings. The electron transfer mechanisms elucidated by NBO are directly related to potential receptor binding, while solvent adaptability enhances its applicability in diverse biological environments. By showcasing caffeine’s favorable ADMET profile, the study highlights its potential for drug repurposing, particularly in formulations targeting specific receptors.

Experimental details

Employing PerkinElmer FT-IR spectrometer, FT-IR emission spectra were examined utilising conventional KBr pellet technique of CAFI in the 4000–500 cm^− 1 wavelength range. UV spectra were recorded using an UV-vis spectrophotometer, which has a wavelength ranging between 200 and 800 nm. The crystal structure data has been obtained by already reported CIF (no. 610381) [18].

Computational facts

Gaussian’09 algorithm was utilized to calculate CAFI using wb97xd/6-311 + + g(d, p) [9]. The VEDA4 [10] tool uses PED interpretation to carry out the vibrational investigations. The same level of NBO research was done, providing unambiguous evidence of intra- and intermolecular interactions [11]. The MEP Structure, which is constructed utilizing the chemical orbital mappings of the largest and FMO employing Gauss View 5.0 application, serves to analyze the very potentially active location [12]. All isosurface visualisations were yielded by the VMD programme, and Multiwfn application, was used to conduct investigations on the ELF, DORI, RDG, and LOL [13,14,15]. The Auto dock software simulates three distinct ways of interaction between ligands and proteins. Studies have been conducted on the effect of effectual MD modelling on macromolecule flexibility. The ligand-protein stability has been assessed using the GROMACS 5.1 programme. The Swiss Param web server was exploited to analyse the ligand [16, 17].

Results and discussion

Structural conformation

Optimized geometry

The optimized structural parameters of caffeine were reckoned via the wb97xd/6-311 + + G(d, p) basis set of different solvents gas, ethanol, methanol &heptane, as detailed in Table 1 and S1-S2) and by the atom numbering scheme illustrated in Fig. 1. These calculated parameters are consistent with those reported in the literature [18]. The calculations were performed using Cs point group symmetry, with the most stable conformer exhibiting C1 point group symmetry. Group theory analysis reveals that for a stable conformer of the caffeine molecule, there are 25 stretching coordinates, 43 bending coordinates, and 13 torsional coordinates. Bond lengths(BL), expressed in Ångstroms (Å), signify the distances between bonded atoms, while bond angles, in degrees (◦), denote the angular relationships between adjacent bonds within molecules. Discrepancies between computational and experimental values offer insights into the accuracy of computational methods, aiding in their refinement. Consistency suggests the reliability and robustness of the computational approach, facilitating a deeper understanding of molecular structures and enabling the refinement of computational models to align with experimental observations. From the provided data, we can observe instances where both the bond lengths and bond angles show relatively high and comparable values between computational (optimized at wb97xd) and experimental outcomes. For example, the bond between atoms C₄ and O₁₀ (C₄-O₁₀) exhibits a BL of approximately 1.213 Å computationally & 1.223 Å experimentally, indicating a relatively short and consistent bond length. Similarly, the bond angle N₆-C₁₄-H₁₅ shows computational and experimental values of around 124.6° and 123.2° respectively, indicating a wide angle that remains consistent across computational and experimental analyses. These consistent and relatively high values suggest robustness in the computational method’s ability to predict these specific bond lengths and angles, providing confidence in its accuracy and reliability. The bond lengths of CAFI’s C₁₃-H₂₃‧‧‧O₉ (2.99 Å), C₁₁-H₁₆‧‧‧O₉ (2.24 Å) and C₁₂-H₁₉‧‧‧O₁₀ (2.29 Å) exhibit abrupt variations as well. C-H‧‧‧O intramolecular H-bonding occurs amid the methyl group in the ring & C = O group, with bond distances of less than three between H₂₃‧‧‧O₉, H₁₆‧‧‧O₉ and H₁₉‧‧‧O₁₀.

Fig. 1

Optimized structure of CAFI molecule for gas phase

Table 1 Optimized bond length of CAFI obtained by wb97xd /6-311 + + G (d, p) method for gas phases and solvents

Vibrational analysis

The structural parameters were optimized to calculate CAFI vibrational frequencies at the abovesaid level. Figure 2 displays the observed FTIR spectra of all extracted caffeine samples. A comprehensive vibrational band assignment for caffeine has already been addressed. Theoretical vibrational frequencies were computed using DFT. Table 2 compares frequencies observed in the FTIR spectrum with those calculated by DFT.

Fig. 2

Experimental and theoretical FT-IR of CAFI molecule

Table 2 Experimental and calculated vibrational frequencies of CAFI by wb97xd/6-311 + + G(d, p) basis set

Hydro carbon vibrations

Heterocyclic aromatic compounds with nitrogen typically display a hydrocarbon stretching vibration band, typically ranging between 3100 and 3000 cm^− 1 [19]. In this study Comparison amid evaluated & experimental vibrational spectra of caffeine anhydrous in the CH-stretching region appeared at 3259 cm^− 1 and experimentally at 3358 cm^− 1 respectively. The wave number that DFT estimated for this mode agrees with the reported actual result in contrast [20]. Our reckonings support these feeble structure bands and assign them as asymmetric νCH₃ vibrations of the methyl group in this DFT method 3196 to 3140 cm^− 1 and observed spectra 3110 cm^− 1. In calculated symmetric vibration 3078 to 3069 cm^− 1 respectively values arentabulated. The in-plane and out-plane rocking and wagging vibrations for the CH₃ group have been calculated values see table. Reported these bands as CH₃ rocking vibration with appears to be ambiguous [21, 22].

Carbonyl vibrations

The C = O stretching of aromatic aldehydes is influenced by the ring structure, leading to a unique stability. This stability rises from the delocalization of ED within the ring, making the bond stronger than anticipated. The vibrational spectra of the carbonyl group exhibit characteristic bands, with their frequencies serving as markers for studying various compounds. The strength of these bands can be enhanced through conjugation or the creation of H-bonds. The carbonyl group appears in various compounds, including alkaloids like caffeine, which are noteworthy for their abundance in plants and medicinal significance. A distinct absorption band highest, accredited toward the C = O stretching vibration, typically falls within the range of 1850–1550 cm^− 1 [23, 24]. Pyrimidines containing hydroxyl groups usually adopt the keto form and display a strong absorption band in this region due to tautomerism. This band’s intensity in the infrared spectrum, coupled with its relatively interference-free nature, facilitates easy recognition [20]. In our analysis, the molecule with two carbonyl groups exhibits strong absorption bands at 1690 and 1642 cm^− 1 in the FTIR spectra, aligning with calculated DFT values at 1673 and 1619 cm^− 1. Analyzing the nature of the carbonyl group using classical chemical tests or contemporary IR absorptions offers valuable structural insights. The precise position of the carbonyl stretching absorption peak, typically falling within the range of 1680 –1630 cm^− 1, is crucial. Moreover, the stretching frequency of a carbonyl group decreases with an upsurge in the number of attached alkyl groups. The C = O inplane bending and out-of-plane bending were observed at 770 &398 cm^− 1 other reported in Table 2 respectively.

Nitro Carbon vibrations

The ν(C-N) frequency attributed to caffeine at 1548 cm^− 1 closely aligns with the calculated value of 1549 cm^− 1. Similarly, the ν(C = N) assigned at 1398 cm^− 1 corresponds well with DFT-calculated values ranging from 1398 cm^− 1 to 1291 cm^− 1. In the N-methyl Pyridinium ion, the ν(C-N) due to the methyl group attached to the N atom in the side chain is typically detected around 1075 cm^− 1, while in theophylline, strong bands occur at 1098 cm^− 1. For caffeine, the C-N vibrations are observed at 1059 cm^− 1. Concerning aromatic vibrations, the ring ν(C-C) occur between 1625 –1430 cm^− 1 [25]. The specific position of these modes is influenced less by the nature of the substituent and more by the form of substitution around the ring. Generally, these bands vary in intensity and are observed within the ranges of “1625–1590, 1590–1575, 1540–1470, 1465–1430, and 1380–1280 cm^− 1”, as reported by Varsanyi [23]. In CAFI, frequencies occurring at 1286 cm^− 1 and 1256 cm^− 1 are allocated to the ν(C-C) mode of caffeine.

Bending and deformation vibrations

Earlier researchers have identified the spectral ranges of 557–636 cm^− 1 and 393–535 cm^− 1 as indicative of the deformation vibrations associated with the kenotic carbonyl group [26]. Notably, the C = O deformation bands are detected at 658 –622 cm^− 1. Additionally, the C = O deformation frequency assigned at 626 cm^− 1 closely corresponds per the calculated value of 610 cm^− 1 obtained through the DFT method. Further examination of the FTIR spectra reveals the presence of ring stretching and ring breathing modes of vibration at 420 cm^− 1 and 450 cm^− 1, separately. These modes serve as characteristic markers of alkaloids, with the NC out-of-plane bending.

Hirshfeld surfaces (HS)analyses

The Crystal Explorer software was employed to create the molecular fingerprint plots (FPs) and conduct HS analyses [27]. The HS delineates the boundary between the central reference molecule and its neighboring molecules within the crystal lattice, defining a distinct region in crystal space. This spatial division facilitated the examination of intermolecular interactions via fingerprint analysis within crystalline environments [28]. HS analysis enables the visualization and quantification of non-covalent interactions(NCI) decisive for maintaining the stability of crystal packing. Parameters such as “curvature, shape index, d_norm, and electrostatic potential” can be represented on the HS. The d_norm property, reflecting the spaces (di and de) amid nuclei inside & outside the HS relative to vdW radii, is pivotal. In the diagram, white regions indicate contacts close to vdW radii, while red and blue areas signify petite and longer inter-contact distances, respectively. HS studies explore various aspects, including the examination of tiny molecules within macromolecule cavities and the exploration of correlations between contact strength and melting points. Short-range intermolecular connections are highlighted as red areas on the HS when dnorm mapping is applied (Fig. 3). These circular, red-colored regions denote the presence of HB between adjoining molecules. These red patches on the surface are instrumental in facilitating intermolecular HB formation, with the O acceptor & H donor atoms in caffeine occupying crucial positions. Fingerprint plots enable the recognition and comparison of different interaction types. The red regions highlight close contacts, primarily hydrogen bonding interactions, which are critical for docking efficiency. Comparative maps across solvents indicate variability in interaction density, affecting binding efficiency and biological activity.

Fig. 3

Hirshfeld surface analysis of CAFI molecule

Figure 4 displays all of the 2D FPs of all interactions surrounding the asymmetric unit. It measures how many different types of inter-contacts the molecule forms within the crystal. It is found that the HS (45.6%) is mostly contributed by the H—H inter-contacts, which seem to be the main factor influencing the packing of the crystal structure. The attractive interatomic interactions larger contribution as N—H = 12.3%, O----H = 24%, C—C = 4.5%, and C—N = 6.9% in engaged in H- bonding interactions. The C—H, N—H, N-N, and C-O contacts of relatively smaller contributions.

Fig. 4

Fingerprint plots of CAFI molecule

Transition studies

UV analysis

A powerful method for accurately studying electronic transitions in all compounds involves UV–Vis technique [29, 30]. To assess the system’s connections, electronic spectra for CAIF were hypothetically projected for the gas phase and compared with experimental outcomes. The outcomes are shown in Fig. 5. Table 3 contains a list of the highest absorption, band gap and oscillator strength (f), both experimental and theoretical. Using data from experiments, the electronic spectrum of CAFI reveals high and moderate absorption bands at 267 nm in methanol solvent. The absorption spectrum that has been calculated shows peaks at 290, 293,298, and 297 nm for gas and solvents. The absorption peak’s blue shift is probably an outcome of interactions among molecules of hydrogen bonds. The peak at 290 293 298 and 297 nm is ascribed to n→π* transitions, which start with the negatively charged molecule’s LP of electrons and move to the π* orbital between the Methyltheophylline ring. Usually, these shifts lead to a charge being separated because the π* system receives additional electrons whereas the n framework remains electron-deficient. In contrast, the transition at 290, 293, 298, and 297 nm has a strength 0.139, 0.0, 0.242, 0.189 and 0.244. The strongest oscillator-driven electronic shifts originate from “H-> L (95%), H-> L (95%), H-> L (96%) and H-> L (95%)”.

Fig. 5

Experimental and theoretical UV–Visible spectra in gas and solvent phases

Table 3 Experimental and theoretical UV–vis characteristics of CAFI in different solvents

Frontier Molecular Orbitals

Electron states are frequently described as “HOMO” & “LUMO” in the context of molecular investigation. HOMO epitomizes an electron that is in the outermost layer and functions as a donating e⁻, while LUMO represents an electron that is in the inner shell and can receive electrons [31, 32]. In this study, the HOMO was discovered to have an energy level that spanned − 5.898 to -6.191 eV, whereas the LUMO was determined to be between − 1.176 to -1.729 eV. An assessment of the energy gap (Eg) among the HOMO & LUMO was achieved to establish the stability characteristic of CAFI molecule, as demonstrated in Fig. 6. This gap varied between 5.898 and 6.191 eV in diverse solvent conditions, suggesting electron affinity (EA) values of between 1.176 and 1.729 eV, respectively. Furthermore, the electrophilicity index proved to be a useful predictor of the molecule’s possible biological function. It was estimated at around 3.388 eV for gas, 2.663 eV for ethanol, 3.514 eV for heptane, and 2.966 eV for methanol. Different solvents, including methanol, ethanol, and hexane, contributed different ionization potentials (IP) and equivalent abundances (EA) that affected the biological function of the sample. These unique energy trends stayed constant throughout these solvents. Table 4 presents these results together with the associated energy parameters. Moreover, the electronic solution characteristics in various solvent conditions were evaluated using the IEFPCM solvation concept. The FMO Eg for CAFI were around 4.227 eV (gas), 4.792 eV (ethanol), 4.462 eV (heptane), and 4.591 eV(methanol). Interestingly, a greater chemical hardness value (2.396 eV) found in heptane solvent and a lower EA in ethanol solvent equate to a higher EA with a greater Eg, suggesting a harder molecule [33]. A useful indicator of the molecule’s possible biological activity is the electrophilicity index, which may be evaluated at 3.388 (gas), 2.663 (ethanol), 3.514 (heptane), and 2.966 (methanol) eV. Furthermore, ICT emphasizes the transfer of electrical charge from the e⁻ source (C = O group) to the electron-accepting (CH₃) groups. The EA varies between 1.176 and 1.729 eV, with different solvents influencing the ionization potential and electron affinity trends. Ethanol solvent shows a lower EA, whereas heptane has a greater chemical hardness value (2.396 eV). Ethanol’s smaller gap (4.792 eV) supports greater electronic interactions in polar settings, whereas gas-phase Eg (4.227 eV) indicates moderate reactivity.The biological context higher electrophilicity, which promotes enzyme binding and metabolic activity, is generally correlated with lower Eg. CAFI is a flexible choice for pharmacological research because of its electrophilicity index, which varies depending on the solvent. Heptane’s higher chemical hardness (2.396 eV) reflects reduced reactivity in non-polar environments, correlating to decreased drug efficacy in such conditions. Methanol and ethanol environments reveal moderate electron affinity and favorable solvation energy, indicating better interaction potential with biological molecules [34].

Fig. 6

Frontier molecular orbitals of CAFI molecule with different solvents

Table 4 Calculated energy values of title compound by B3LYP /6-311 + + G(d, p) for gas and solvents

NBO Analysis

The 2nd -order perturbation model investigation of the Fock matrix in the NBO basis for Caffeine, performed at the abovesaid level of theory, delves into the nuanced electron density redistribution and donor-acceptor interactions critical for elucidating the molecule’s stability and electronic structure (Second order perturbation theory analysis of Fock matrix in NBO basis of Caffeine shown in Table 5). The analysis unveils a series of interactions between various molecular orbitals, shedding light on the intricate interplay of electrons within the caffeine molecule [35]. Firstly, the σ → σ* transition between the C₁-N₂ and N₃-C₅ bonds demonstrates ED transfer from σ bond of C₁-N₂ to the antibonding σ* orbital of N₃-C₅, resulting in a stabilization energy of 3.91 kcal/mol. This interaction signifies the contribution of sigma electrons to the overall stability of the molecule. Furthermore, the π → π* transition between C₁-C₅ and N₆-C₁₄ bonds highlights the delocalization of ED from π bond of C₁-C₅ to antibonding π* orbital of N₆-C₁₄, yielding a substantial stabilization energy of 21.84 kcal/mol. This π → π* interaction underscores the significance of pi electrons in stabilizing the molecular structure. Additionally, lone pair (LP) contributions play a pivotal role in stabilizing the molecule, as demonstrated by interactions involving LP orbitals. For instance, interactions amid the LP of N₂ and the π* antibonding orbital of C₁-C₅ result in a significant stabilization energy of 44.24 kcal/mol. Similarly, interactions among the LP of N₃ & π* antibonding orbital of C₁-C₅ yield a E⁽²⁾ of 59.53 kcal/mol. These interactions underscore the importance of lone pair electrons in stabilizing the molecular framework through orbital interactions. Moreover, interactions involving the LP of N₇ donating to the σ* antibonding orbitals of C₄-O₁₀ exhibit notable stabilization energies, emphasizing the role of lone pairs in modulating molecular stability. Specifically, the interaction between N₇ and C₄-O₁₀ results in a E⁽²⁾ of 73.04 kcal/mol, showcasing the substantial influence of lone pair interactions on molecular stability. Lastly, the π → π* interaction between N₆-C₁₄ and C₇-C₅ bonds emerges as particularly significant, with a remarkable E⁽²⁾ of 115.59 kcal/mol. This interaction underscores extensive delocalization of pi electrons within CAFI molecule, contributing appreciably to its overall stability and electronic structure.In summary, the detailed analysis of donor-acceptor interactions and lone pair contributions delivers expensive insights into the electronic structure and stability of caffeine, facilitating a deeper understanding of its chemical properties and reactivity. Lone pair interactions show how lone pairs stabilize the molecular framework. The substantial electron mobility highlighted by π → π* delocalization interactions is frequently linked to increased reactivity and possible biological activity.

Table 5 Second order perturbation theory analysis of Fock matrix in NBO basis of caffeine using wb97xd/6-311 + + g(d, p)

^a E(2) means energy of hyper conjugative interaction (stabilization energy).

^b Energy difference between donor and acceptor i and j NBO orbitals.

^c F(i, j) is the Fock matrix element between i and j NBO orbitals.

Electron hole analysis

Using hole-electron toolbox to investigate several types of electron excited states that occur inside structures. Even though the majority of changes in the UV spectrum occur among the ground state and 3 excited states, electron-hole investigation has mainly concentrated on each of these states [36]. Electron (green)& hole (blue) dispersions were created using the Multiwfn 3.8 programme [15]across an entire set of three excited levels that were discovered using the DFT method. The outcomes achieved for different levels of excitement are shown in Table 6. Only holes and electrons are mostly confined in the Methyltheophylline ring in the first exciton; however, 3rd and 2nd exciting phases, both holes and electrons transfer through the Methyltheophylline ring to the oxygen atom are due to electronegative groups shown in Fig. 7.

Fig. 7

Spreading of electron–hole pair over the material for three excited states of thiamine in gas phase and solvents

The wider distribution of electrons, corresponding with all excited states, is indicated by the higher raised extent of the electron spreading, and simultaneously the median hole and the electrons exhibit the H index. sThe hole and electron locations differ significantly because not all index values are low. The τ value provides an objective assessment of the type of state of excitement a charged particle experiences during a particular molecule. The Δr index provides an impartial quantity of the type of electron excitation; higher Δr under specific chemical conditions indicates higher charge transfers. As the void amongst the primary distribution regions of the hole&electron increases, D index value drops. The D index of the initial excited state mode.

Table 6 Excitation energy (E), D index, Dr index, t index, Sr function for different excited states for the title compound in gas phase and solvents

NLO properties

There are new fields created when electromagnetic radiation travels through a NLO material and modifies its propagation characteristics, which include amplitude, phase, frequency, and other aspects. Applications like optical changing, frequency shifting, optical memory, and reasoning may employ NLO material if these modifications are significant [37]. A compound’s aptitude for NLO uses is mainly dictated by its static first-order hyperpolarizability (β₀). Therefore, DFT is widely employed to study organic NLO materials. Table 7 summarizes the estimated NLO parameters utilized for CAFI in gas & solvents. Comparing a molecule’s total dipole moment (µ) and β₀ to the values of urea’s analogous parameters is a popular method of evaluating its NLO activity. Concerning urea, “dipole moment (µ) and β₀ are 1.3732 Debye and 0.3728 × 10^− 30 esu” [38], respectively. In contrast, µ and β₀ for CAFI are found to be. 577 (gas), 2.112 (ethanol), 1.771 (heptane) and 2.121 (methanol) Debye and 1.553* 10^− 30 (gas), 2.284* 10^− 30 (ethanol), 2.272* 10^− 30 (heptane) and 2.301* 10^− 30 (methanol), respectively. Remarkably, the β₀ for CAFI in methanol, gas, ethanol, and heptane are roughly 6.17, 6.09, 6.13, and 6.17 times superior than urea. This implies that CAFI has a great deal of promise for use in NLO applications. Understanding that the NLO action is caused by ICT resulting from electron clouds moving through the π-conjugated [39] electron configuration is critical.

Table 7 The value of calculated dipole moment µ (D), polarizability and first order hyperpolarizability of CAFI molecule for different phase

Non covalent interactions

ELF and LOL

The distribution of electrons around the compound is determined and interpreted using ELF and LOL. The basic concept of ELF extent is the Pauli repulsion, which was because of an surplus of K.E density [40]. Observing opposite spin-pair behaviour or a single electron is highest are characterized by an ELF value close to 1, indicating the region of maximum Pauling repulsion. Conversely, regions with a minimum ELF value are characterized by an ELF value close to 0 [41]. By Pauli forces, because of their highly localized electrons, the characteristics of the most strongly repelled regions explained the atomic shells, chemical bonds, and lone pair electrons [42]. To characterize the topology of CAFI Multiwfn program is used, which explains the molecular bonding connections such as the existence of electron bonding or nonbonding and shell structure through the colour maps. The ELF values, which are presented in a colour scale and shaded contours on a geographical map, can be represented graphically in both two and three dimensions, as depicted in Fig. 8. The series consists of a red colour for high ELF values (1.0), a green colour for moderate ELF values(ca. 0.7), and a blue colour for the lower end of the scale. Elevated ELF zones of hydrogen atoms in Fig. 8 indicate highly restricted bonded and non-bonded arrangements. Methyltheophylline ring are enveloped by a blue cloud, suggesting low electron localization values. Conversely, a red province adjacent H-atoms (H23, H24, H17 and H18)) on the ELF map signifies intense electron localization, due to C-H‧‧‧O HB attributed to LP of electrons and covalent interactions wherein non-bonded attraction points of O atom converging towards the electron shell’s ELF holes hydrogen in Fig. 8. similar for gas and solvents. In general, LOL presents a more definitive and impeccable portrayal than ELF. The red region is surrounded by H23, H24, H17 and H18 wherein same for gas and solvents. Similarly, in the LOL analysis, a blue cloud surrounds Methyltheophylline ring, while a red region encircles hydrogen atoms (H23, H24, H17 and H18), affirming both low and intense electron localization, respectively.

Fig. 8

(1) Electron localization function (ELF) map, (2) Localized orbital locator (LOL) maps in the gas and solvent phases

RDG analysis

For bioactive compounds, the stability and properties of molecules are determined by non-covalent interactions [43]. The NCI are a function of ED and the 2nd eigenvalue of the Hessian matrix [44, 45]. Concerning the solvent environment, the type and strength of the connections will vary. By via dissimilar solvents such as gas, ethanol, methanol, and heptane, we can study the reactivity, stability, &bonding nature of the component. vdW and repellent steric interactions explain the positive bonds between hydrogen and the environment [46]. From there, the higher density slope indicates a stronger interaction, and the lower density slope is given by a lower electron interaction percentage. Using the graphical representation, we can explain the effectiveness and the bond interactions. The “scatter graph is generated among RDG & sign (λ₂), where sign (λ₂) represents the second Eigenvalue of the ED”. When the sign of (λ2) is positive, repulsive interactions become apparent because of bonding interactions, while attractive interactions are disclosed because of non-bonding interactions when the value of (λ2) is negative. The blue RDG plot, which spans from − 0.05 to -0.02 a.u., illustrates an additional beneficial interaction. The significant repulsion caused by the steric outcome is observed in the positive 0.005 to 0.05 a.u. zones, as shown by the presence of red flaky specks at the center of Methyltheophylline ring confirming C-H‧‧‧O contact. The RDG diagram (Fig. 9) demonstrates the occurrence of vdW interactions within the range of -0.015 to -0.005, attributed to CH₃ group, through the presence of green colour.

Fig. 9

2D scatter and Isosurface density plots illustrating the non-bonded interactions of CAFI molecule

Charge scrutiny

Fukui analysis

The location’s reactivity and the hydrogen bonding interactions such as electrophilic and nucleophilic reactions will be studied using fukui function. The comprehended electrostatic potential values are shown in the table S3. In the context of the fukki function, the mulliken analysis serves as the fundamental basis utilized in DFT to provide precise quantitative data regarding the nucleophilicity or electrophilicity of individual atoms [47]. The active site of the molecule can be studied by adding or removing a charge to the molecular system. It helps to study the active sites and the reactivity by the maximum electrophilic and nucleophilic sites. The nucleophilic, electrophilic& radial attacks are signified by f⁺, f^– and f^o respectively. A function is developed to accurately identify the quantitative reactivity and spatial selectivity descriptors within a molecule. This function involves splitting the electron density of a “neutral atom(N) into cationic species(N-1) and anionic species(N + 1)” using the Hirshfeld methodology [48].

To compute the fukki functions the below-mentioned variables have been utilised. The formula for calculating the force of an assault is given by f ⁺(r) = q (N + 1), where f ⁺(r) represents the force, q is a positive value, r is a variable, and N is a constant. The formula for a negative assault is f⁻(r) = q(N) - q(N-1), while the formula for a neutral attack is f^o(r) = (q(N + 1) - q(N-1))/2. The equations still use symbol q to represent accumulated charge. In the Fukui analysis, C1 (~-0.064e), O10 (~-0.06e), N3(~-0.057e) and C12 (~-0.0455e) are identified as having the highest electrophilic and C8 (~ 0.07685e), C14 (~ 0.07757e) and O9 (~ 0.0318e) for nucleophilic reactivity for gas & solvent phases due to deactivation of the ring occurs due to extensive delocalization, where it is surrounded by a electronegative group [49]. This deactivation is further facilitated by the negatively charged atom, which induces a withdrawal of charge impact on the ring. Consequently, a resonant charge-discharging action takes place on the Methyltheophylline ring and hence deactivation of the ring results in changes.

Natural atomic charge analysis

Atomic charge analysis is crucial for knowing the electrical structure and behavior of molecules employed in diverse scientific disciplines using DFT. Moreover, the application of QM computations to molecular systems relies on utilization of natural atomic charges. In the molecule being studied, atoms (C4, C8, C5, and C14) have a relatively high positive charge in the presence of gas (0.8501, 0.6704, 0.3835, 0.264), ethanol (0.27643, 0.38749, 0.6805, 0.86097), heptane (0.26835, 0.38473, 0.67402, 0.854), and methanol (0.27666, 0.38758, 0.68069, 0.86117). However, the next oxygen atom demonstrates a negative charge in the presence of oxygen atoms (O10, O9) in gas (-0.6445, -0.6321), ethanol (-0.6802, -0.6625), heptane (-0.6579, -0.6433), and methanol (-0.6809, -0.6631). The hydrogen atoms (H15, H22, H21, H20, H18, H17, H24, H23, H16, H19) exhibit a relatively positive charge when they engage in electron sharing with adjacent carbon atoms (table S4). The presence of oxygen atoms with strong electronegativity actively engaging in hydrogen bonding interactions (C-H…O) leads to a notably larger positive charge on C5 compared to other carbon atoms in all phases. The significant negative charges of carbon and nitrogen atoms are evident in the given values. For carbon C11, C12, C13, C1 the noted negative values of gas (-0.3681, -0.3649, -0.3524, -0.03759), ethanol (-0.3688, -0.36637, -0.3556, -0.0345), heptane (-0.3686, -0.3657, -0.3540, -0.0369), methanol (-0.3688, -0.3663, -0.3556, -0.0344), and N6, N7, N3, N2 gas (-0.5278, -0.5201, 0.4829, -0.36946), as well as in solvents it is approximately (-0.558, -0.514, -0.475, -0.361).

MEP studies

MEP analysis provides a deep relationship between molecular makeup and physiochemical characteristics, including 3D charge dispersion, chemical responsiveness, HB relationships, and molecule relative polarity [50]. This analytical technique demonstrates that positive electrostatic potential is produced by atomic nuclei rejecting protons in places that have minimal electron concentrations, whereas negative electrostatic potential is caused by protons being drawn to locations that have an elevated electron density. The interactions of electrons and nuclei with a unit positive test charges reveal the positive and negative contact energy at specific places within a molecule [51]. When investigating the reactive response of a CAFI chemical, the CHELPG atomic charge [52]estimation method is utilized. By designating atomic charges, this technique enables the reconstruction of MEP at different points in the molecule. For the gas &solvent phases, the theoretically anticipated MEP values(V(r)) and related electrostatic potential values for every atom are displayed. Computed MEP maps of gas, ethanol, heptane, and methanol are among the media that can be used to help interpret changes in chemical reactivity sites and electrostatic potential within CAFI in various environmental conditions. These MEP charts, shown in Fig. 10, illustrate the relative reactivity of the atoms in CAFI, emphasizing the chemically sensitive active areas. These areas are distinguished by a color grading scheme that shows the electrostatic potential distribution visually by moving from red (larger negative area) to blue (greater positive space) [53].

Fig. 10

MEP map for CAFI with different phases

Based on the data shown in Fig. 10, it can be demonstrated that the oxygen atom in the methyltheophylline ring is the most reactive part of the chemical in all phases of CAFI because it has the maximum electron abundance. Following closely behind is the amino moiety’s nitrogen atom, which appears as a subsequent vulnerable location to chemical reactions. Electronegative nitrogen & oxygen atoms are symbolized in red patches in the MEP visualization image, signifying regions of negative potential. In contrast, regions of positive potential are indicated by blue patches that encircle a large no. of H-atoms among the CH and CH₃ groups. The intensity of the red & blue regions in Fig. 10 defines the absolute magnitudes of negative & positive potential, accordingly. The study also showed that carbon atoms (C₈, C₄, and C₅) in combination with lone N and O atoms possess considerable positive charges in all phases. In particular, hydrogen atoms in the ring N-connected methyl group (H_23,24) have significant positive charges that fluctuate from 0.151 in the gas phase to 0.156 in ethanol and methanol, and 0.154 in heptane (Table 8). Also, the highest electronegative point charge in all phases for gas (-0.571 e), ethanol (-0.644 e), heptane (-0.597 e), and methanol (-0.645 e) is found to be held by the N₆ atom. O₁₀ (-22.396 a.u.) has a value of V(r) that is significantly greater than the others, revealing that it is one of the more prominent atoms among those mapping. MEP charts depict atoms with identical electrophilic and nucleophilic potentials in both gaseous & solvated phases.

Table 8 Electrostatic potential values V(r) and their fitting electrostatic point charges on each atom of CAFI molecule computed at wb97xd/6-311 + + G(d, p) basis set for different phases

Biological assessment

Docking

Molecular docking is a useful method for discovering novel pharmaceuticals. The scientific validation of CAFI emphasis to a chemical’s drug-like properties [54, 55]. RCSB was utilized to determine the structures of the kidney disease target proteins. The optimum framework of the CAFI molecule serves as the ligand’s base. High determination crystal structure kidney disease was taken from the PDB website (PDB ID: 2V90, 1FP3 and 4U6T) [56]. AutoDock Suite 4.2.6 has been utilized to investigate the relationships between ligands and proteins [57,58,59,60]. For proteins to undergo docking evaluation, they must possess pharmaceutical activity, stability, and appropriate crystallographic resolutions (< 2), obtained from sources such as the RCSB. The ligand’s three-dimensional interactions with multiple target proteins are illustrated in Fig. 11 [61]. Following the reduction of its energy level, the ligand is prepared for docking and is employed in receptor-ligand interactions. The AutoDock scoring tool provides visual representations of the molecule’s components in Fig. 11, while Table 9 offers a comprehensive summary of the best-scored conformers, detailing factors such as inhibition constants, interaction energies and hydrogen bonds with their respective distances. Considering both amino acid purity and structural integrity, the antimicrobial proteins were selected 2V90, 1FP3 and 4U6T were chosen based on their suitability. The Ramachandran plot, as depicted in Fig. 12, underscores the proteins’ favorable interaction potential, with 96.3%, 90.6.1%, and 89.2% of amino acids falling within permissible regions, indicating their structural stability and viability for further investigation.

Fig. 11

3D interactions of molecular docking with suitable proteins

Fig. 12

Ramachandran plots of CAFI molecule

The target protein’s docked value also binding energy value are closely coupled; a greater docking score is associated with a larger negative in value binding energy (B.E). The B.E of proteins 2V90,1FP3,4U6T are calculated to be -3.78, -4.08, -3.35 kcal/mol, respectively. Their consistent inhibition constants are 1.68,1.02 and 3.35 mM. With a B.E. of -4.08 kcal/mol, the 1FP3 protein has the lowest among the four proteins under investigation. Four amino acids, HIS382, ARG60, HIS248 and PHE 377 with an intermolecular energy of − 4.08 kcal/mol, are involved in this interaction. 59.14 is the calculated RMSD value between the targeted proteins. Drugs and receptors’ affinities for binding are largely determined by hydrogen bonding. Robust bonding through hydrogen relationships signifies an effective binding ability among the ligand & protein. The creation of H- bonds, specifically between electronegative oxygen atoms and hydrogen atoms, is seen in Fig. 11. The compound’s Kidney function stimulant efficacy is demonstrated by the interaction of CAFI with Kidney function stimulant proteins. Additionally, topological analyses of CAFI support the results of molecular docking experiments. When CAFI comes into contact with Kidney function stimulant, it becomes more effective against 1FP3. This suggests that CAFI may be a promising medication for the treatment of Kidney function stimulant.

Table 9 Summary of hydrogen bonding and molecular docking results of CAFI with kidney function stimulant

MD

The results obtained by molecular docking provide a clear understanding of the overall process by which a ligand binds to its target. In a protein structure the ligand bonding is further investigated by the MD stimulation of the ligand-protein complex [62]. In the realm of molecular dynamics, understanding interatomic potential necessitates the modelling of both covalent and non covalent interactions among molecules, which offers a through comprehension of its motion, connections and stability. Using comprehensive 100 ns molecular dynamics at an atom level, the docked complex of the ligand (CAFI) and protein (1FP3) demonstrated the greatest binding strength and was thoroughly investigated. In molecular dynamics simulations, maintaining neutrality and proper hydration of the system is crucial. Due to its ability to precisely represent the physical properties of water, the TIP3P water model is commonly used. In specific simulation environments, the model will be further refined for use through CHARMM modifications [63]. Continuing this, the system was subjected to an ensemble equilibration process for 1 ns each in NVT&NPT conditions. The adventure culminated with the extraction of key parameters from the final trajectory file, post the application of PBC corrections to the 100 ns atomistic MD trajectory, all made possible by the built-in functions of GROMACS2022 [64]. This intricate process paints a vivid picture of the complex yet fascinating world of molecular dynamics simulations. The parameters under consideration encompassed RMSD, RMSF, radius of gyration (Rg), count of HB, and the Solvent Accessible Surface Area (SASA) [65]. As shown by the trajectory, during the entire simulation, the docked ligand maintained its stability in the protein’s binding site. Suggesting a state of relative stability, the complex formed by the protein and ligand didn’t undergo any significant conformational shifts in the final three nanoseconds. The overall stability of the complex was illustrated by the RMSD stabilization. The possibility of instability conformational alterations in the protein-ligand complex was suggested from the significant variations in the RMSF. The consistent Rg further highlighted the protein’s stability. The fluctuation in the count of hydrogen bonds for both complexes is illustrated in Fig. 13. Moreover, the study of changes in the peptide’s surface area post ligand binding was carried out by examining the shifts in the SASA. Remarkably, both protein-ligand complexes showed a steady SASA, suggesting no major protein folding or unfolding. The mystery of the effective free energy of binding was unraveled using gmx_MMPBSA v1.5.2. Like frames in a film reel, snapshots from MD simulation were analyzed using MMPBSA calculations [66], unveiling the complex choreography of molecules. There’s an alternative representation for ΔG binding as well, which is ΔG binding = ΔH –TΔS. In this equation, ΔH stands for the enthalpy of binding and − TΔS represents the conformational entropy after the ligand binds. If the entropic term is disregarded, the resulting value is referred to as the effective free energy. This value is typically sufficient for likening relative binding free energies for systems that are similar. Navigating on a journey through the full 100 ns trajectory, we unearthed the treasure of the average effective free energy of binding, a golden − 8.20 ± 3.26 kcal/mol (refer to Table 10). Our scientific compass, the MMPBSA investigations, pointed towards a robust bond between the CAFI molecule and the protein (1FP3), a discovery that echoes the docking outcomes.

Fig. 13

(a) RMSD (Root Mean Square Deviation) of protein and ligand; (b) RMSF (Root Mean Square Fluctuation) of the amino acid residues; (c) Rg (Radius of Gyration) of the protein; (d) Hydrogen bond count of the protein-ligand; (e) SASA (Solvent Accessible Surface Area) for the protein complex

ADMET and drug-likeness

To study and identify how the medication will work, the observed properties of ADMET will be used. For this objective, CAFI was provided to observe in silico ADME properties were evaluated using pkcsm online server [67]. In addition, the Swiss ADME [68]online tool was employed to determine the lipophilicity, drug-likeness & bioavailability ratings of these compounds based on their physical and chemical properties [69]. By employing these methodologies, the molecule caffeine can achieve favorable outcomes in clinical trials, hence enhancing the prospects of it being deemed a promising candidate for treatment in the future. Drug-likeness research was undertaken to determine the potential efficacy of caffeine as a treatment. The drug-like properties of the molecule were assessed based on several key parameters, such as AlogP, the no. of rotatable bonds, the no of HB acceptors (HBA) and donors (HBD), and molar refractivity [70]. The drug-likeness test for the biological medicine caffeine is outlined in Table 11, which includes the criteria for evaluation. The ADMET features of the caffeine compound, including its excretion rate and absorption, are presented in the Table 12 [71].

Caffeine has a water solubility of -2.023, indicating that it may dissolve in water at 250 °C. Moreover, its intestinal absorption rate is 100%, indicating a potential rapid absorption [72]. Caffeine, is probable not a type of P-glycoproteinI/II inhibitor. This is an significant aspect to consider in the research of pharmacokinetics, as indicated by the Table 12. The prescribed dose is thought to have a uniform distribution throughout the bloodstream and does not lead to renal impairment or thirst. This conclusion is based on a Vdss of -0.595. The BBB value, measured at -0.268, indicates that CAFI is not effectively distributed in the cerebral cortex. This value is used to assess the ability of a medication to penetrate the brain. The permeability of the CNS is -2.977, which prevents caffeine from entering the CNS. An alteration in the rate at which drugs metabolized by cytochrome p450 are processed may be due to a modification in the metabolism of certain amino acids involved in detoxification. The Table 12 provides information indicating that caffeine does not have any effect on or hinder certain digestive processes. The whole clearance value (0.193) predicts the quantity of biological waste.

According to the findings, caffeine does not attach itself to the cationic transporters (OCT2) of this protein, which are necessary for the elimination of drugs through the kidneys. The AMES toxicity test can be employed to predict the toxicity of a chemical by assessing its potential to induce mutations. The table demonstrates that CAFI does not have any mutagenic effects. Due to its potential for ingestion and efficient elimination by the human body, caffeine has been identified as a viable option for medicinal application, as indicated by the discoveries of the ADMET research. The implementation of drug similarity limits was expected to lead to standards that fell within the range permitted by Lipinski’s calculation. Lipinski’s formulation is fulfilled by the no. of HBA and HBD in caffeine, which were determined to be 3 and 0, respectively. The molar refractivity, calculated to be 52.04, falls within the acceptable range. The quantity of HBA and donors satisfies Lipinski’s formula [73]. The logarithm of the P value, determined to be 1.79, indicates that the substance is lipophilic/hydrophobic. Due to its adherence to the Lipinski rule, CAFI has the potential to be utilized for treatment.

Table 10 Free energy of binding obtained using MMPBSA calculations (considering all frames from 100 ns)

Table 11 Drug likeness descriptors of CAFI

Table 12 ADMET parameters of CAFI molecule

Conclusion

CAFI’s structural, electrical, vibrational, and biological characteristics were thoroughly investigated using quantum chemical computations and spectroscopic techniques. Intramolecular hydrogen bonding arises among the methyl group in the ring & C = O group, with bond lengths of fewer than three among H₂₃‧‧‧O₉, H₁₆‧‧‧O₉, and H₁₉‧‧‧O₁₀. CAFI’s FT-IR spectra show increased agreement with its published findings. The assessment of FMO, UV spectrum evaluation, and CT contacts all affect the chemical’s bioactivity. The electronic properties of CAFI were investigated experimentally and theoretically using UV spectroscopy in conjunction with the TD-DFT method. CAFI’s HOMO-LUMO energy gaps were roughly 4.227 eV in gas, 4.792 eV in ethanol, 4.462 eV in heptane, and 4.591 eV in methanol, showing CT within the molecule based on the FMO energies. Studying FMO reveals the molecule’s initial non-toxicity and bioactivity, providing significant conceptual comprehension. The strong attractions, weakly attractive connections, and steric repulsions seen in CAFI were investigated using non-covalent interaction approaches. Fukui and MEP investigations indicate that the electronegative nitrogen and oxygen atoms in proteins derived from amino acids are accessible targets for nucleophiles and electrophiles. Specifically, the 1FP3 protein shows a low B.E of -4.08 kcal/mol, confirming the establishment of H-bonds among kidney disease proteins and CAFI through molecular docking assays.