Molecular Modeling Approach Research Articles

Screening and design of PARP12 inhibitors from traditional Chinese medicine small molecules using computational modeling and simulation

The poly (ADP-ribose) polymerase (PARP) family of enzymes plays a pivotal role in orchestrating a multitude of cellular processes, including DNA repair mechanisms, transcriptional regulation, and modulation of immune responses. Within this family, PARP12 emerges as a noteworthy candidate for targeted cancer therapeutics. Consequently, this investigation endeavors to screen and design potential PARP12 inhibitors derived from traditional Chinese medicinal compounds by employing sophisticated molecular modeling and computational medicinal chemistry approaches. The compound RBN2397 is utilized as a benchmark, and the binding efficacies of the newly identified small molecules are assessed against a spectrum of criteria, encompassing molecular interactions, binding free energy, and extensive post-simulation analyses. The outcomes demonstrated that the identified small molecules, specifically tcm8650 and its derivative XC-1, possess remarkable binding affinities and exhibit reduced binding free energies compared to RBN2397. The molecular docking and interaction profiles of these compounds were also comprehensively scrutinized. Moreover, ADMET profiling meticulously evaluated the pharmacokinetic profiles and physicochemical characteristics of these promising molecules and their projected human physiological impact. These computational studies indicated their potential therapeutic applicability and predicted acceptable safety profile, advocating their further exploration as viable candidates in cancer treatment.

Designing the next generation of polymers with machine learning and physics-based models

Abstract The development of next-generation polymers necessitates optimizing several key properties simultaneously, a task that is expensive and infeasible using traditional trial-and-error experimental approaches. A promising alternative is employing a combination of machine learning and physics-based tools to rapidly screen the polymer design space and provide suggestions of new polymers that meet the critical properties required for industrial applications. In this study, we introduce a comprehensive workflow that utilizes machine learning and molecular modeling approaches to design new polymers with the focus on improving five polymer properties: (1) glass transition temperature, (2) dielectric constant, (3) refractive index, (4) stress optic coefficient, and (5) linear coefficient of thermal expansion. Using a small dataset ( < 200 unique polymers), we developed quantitative structure-property relationships (QSPRs) models to accurately predict the experimental polymer properties for both homo- and co-polymer systems. We tested several ML algorithms and identified the best models for predicting these polymer properties, achieving test set R 2 greater than 0.77 across all properties. We then explored new polymers by creating a library of over ∼10 000 homopolymers using R-group enumeration tools and applied the trained QSPR models to rapidly predict the five polymer properties. The predictions of QSPR models were used to create a multi-parameter optimization score, which helped downselect the large polymer space to ∼10 promising candidates. The properties of these selected polymer candidates were subsequently validated with classical molecular dynamics simulations and density functional theory, revealing a strong correlation with the QSPR model predictions. Finally, one of the top candidates was validated by experiments, which showed good agreement against QSPR and physics-based models. Our workflow underscores the power of combining data-driven and theoretical methods in the polymer design process given a small dataset size, offering a valuable resource for experimentalists looking to leverage computer-aided strategies in materials innovation.

The Entropy of Mixing in Self-Assembly and the Role of Surface Tension in Modeling the Critical Micelle Concentration

A theory for the micelle formation of nonionic head-tail amphiphiles (detergents) in aqueous solutions is derived based on the traditional molecular thermodynamic modeling approach and a variant of the Flory–Huggins theory that goes beyond lattice models. The theory is used to analyze experimental values for the critical micelle concentration of n-alkyl-ß-D-maltosides within a mass action model. To correlate those parts of the micellization free energy, which depend on the transfer of hydrophobic molecule parts into the aqueous phase, with molecular surfaces, known data for the solubility of alkanes in water are reanalyzed. The correct surface tension to be used in connection with the solvent-excluded surface of the alky tail is ~30 mN/m. This value is smaller than the measured surface tension of a macroscopic alkane–water interface, because the transfer free energy contains a contribution from the incorporation of the alkane or alkyl chain into water, representing the change in free volume in the aqueous phase. The Flory–Huggins theory works well, if one takes into account the difference in liberation free energy between micelles and monomers, which can be described in terms of the aggregation number as well as the thermal de Broglie wavelength and the free volume of the detergent monomer.

Mechanistic insights into the interaction of anxiolytic drugs with human serum albumin in a ternary system utilizing spectroscopic and molecular modeling approaches

In recent years, buspirone has been co-administered with sertraline to resolve sexual disorders caused by sertraline. Therefore, the present study was conducted to investigate the interaction effect of two antidepressants and anxiolytic drugs, sertraline and buspirone, on human serum albumin (HSA) using spectroscopic and molecular docking techniques. Fluorescence emission spectroscopy and molecular docking were used to calculate the binding affinity and determine the best binding sites for these two drugs. Additionally, UV-visible and circular dichroism spectroscopy were performed to investigate the effect of these drugs on the conformational changes of HSA. The results showed that both drugs have a strong ability to quench the fluorescence of HSA through a static mechanism, and cause structural changes in HSA. It was also found that binding of sertraline and buspirone to HSA is spontaneous (ΔG° <) and hydrophobic interactions, van der Waals forces and hydrogen bonds play a significant role in these interactions in the ternary system. In addition, molecular docking data showed that both drugs bind with high affinity to the Trp residue in subdomain IIA. The binding constants (Kb) for (HSA-SRH)-BSH and (HSA-BSH)-SRH were equal to 6.30 and 3.99, respectively, at 298 K. This study demonstrates that the presence of the second drug (buspirone/sertraline) affects the interaction and binding affinity of the first drug (sertraline/buspirone) to human serum albumin.

Synthesis, Characterizations, Anti-Diabetic and Molecular Modeling Approaches of Hybrid Indole-Oxadiazole Linked Thiazolidinone Derivatives

Objective: To synthesize hybrid compounds of indole and oxadiazole in search of highly effective anti-diabetic therapeutic agent. Methods: With the goal of advancing diabetes research, our group designed and synthesized a library of 15 compounds based on indole-derived oxadiazole bearing varied substituted thiazolidinone via a multistep synthetic route. 13C-NMR, 1H-NMR and HREI-MS were applied for the characterization of all the synthesized compounds. Their biological inhibitory activity against diabetic enzymes, i.e., α-amylase and α-glucosidase was also determined. Results: Compound 7, 9 and 15 exhibited excellent inhibition against α-amylase and α-glucosidase than the standard acarbose (IC50 = 8.50 ± 0.10 µM for α-amylase and 9.30 ± 0.30 µM for α-glucosidase. To ensure the inhibitory actions of these potent analogs in molecular docking, an in silico approach was used. To determine the drug likeness of the reported analogs, an ADMET investigation was also carried out to explore the nature of the designed compounds if used as a drug. Conclusion: Fluoro-substituted analog 15 has stronger inhibition profile against both enzymes. All the potent compounds can be used as effective anti-diabetic therapeutic agents in future.

Experimental and molecular modeling approach on the corrosion behavior of mild steel in HCl solution with quinoxaline inhibitors

Two new quinoxaline derivatives namely (E)-3-(2-chlorostyryl)quinoxalin-2(1H)-one (CSQO) & (E)-3-(2-(thiophen-2-yl)vinyl)quinoxaline-2(1H)one (TVQO) were synthesized and identified through the following methods like infrared (IR) analysis, mass spectrometry,1H NMR and 13C NMR. Weight loss, electrochemical techniques [PDP&SIE], surface analytical techniques [SEM&UV-Visible], quantum chemical calculations [DFT], and MD simulations have all been used to examine the effectiveness of CSQO and TVQO in preventing the corrosion of mild steel. According to the findings of the experiments, the inhibitors CSQO and TVQO effectively suppress the corrosion of mild steel in 1 M HCl solution, with maximum corrosion inhibition of 97.0 and 95.5% at 10−3 M for CSQO and TVQO, respectively. Based on PDP investigations, CSQO and TVQO function as mixed-type inhibitors, and their adsorption on the MS surface follows the Langmuir adsorption isotherm and the mode of adsorption is chemisorption. Both surface investigations (SEM and UV-Visible) have validated the protective layer that developed on the metal surface. DFT calculations and MD simulations were used to study the association between inhibitor molecular structure [CSQO and TVQO] and inhibitory efficacy, and they closely match the experimental findings.

Structure-Based Discovery of Phytocompounds from Azadirachta indica as Potential Inhibitors of Thioredoxin Glutathione Reductase in Schistosoma mansoni.

Schistosomiasis, a parasitic disease caused by Schistosoma species such as S. haematobium, S. mansoni, and S. japonicum, poses a significant global health burden. The thioredoxin glutathione reductase (TGR) enzyme, crucial for maintaining the parasite's redox balance and preventing oxidative stress, has been identified as a promising target for anti-schistosomal drug development. This study aims to identify potential TGR inhibitors from Azadirachta indica phytochemicals using molecular modeling approaches. We screened 60 compounds derived from A. indica bark and leaves through molecular docking to assess their binding affinity, followed by the evaluation of binding-free energies for the most promising candidates. Drug-likeness and pharmacokinetic properties were assessed, and molecular dynamics simulations were conducted to explore the conformational stability of the protein-ligand complexes. Our findings revealed that several A. indica compounds exhibited significantly lower docking scores (up to -9.669 kcal/mol) compared to the standard drug praziquantel (-4.349 kcal/mol). Notably, Isorhamnetin, Isomargolonone, Nimbaflavone, Quercetin, and Nimbionol demonstrated strong interactions with TGR, although Isorhamnetin showed potential mutagenicity. Further binding free energy calculations and molecular dynamics simulations confirmed the stability of Isomargolonone, Nimbionol, and Quercetin as potential TGR inhibitors. In conclusion, these findings suggest that Isomargolonone, Nimbionol, and Quercetin warrant further experimental validation as promising candidates for anti-schistosomal therapy.

Discovery of novel dihydro-pyrimidine hybrids: insight into the design, synthesis, biological evaluation and absorption, distribution, metabolism and excretion studies.

Aim: By keeping in aspects, the pharmacological potential of heterocyclic compounds, pyrimidine-based compounds were designed, synthesized and evaluated for α-amylase inhibitory potential.Materials & methods: Five new series 1a-l, 2a-d, 3a-d, 4a-d and 5a-d of 1,2,3,4-tetrahydroprimidine-5-carboxylate derivatives were designed by de novo method by taking Alogliptin as reference compound. Here in we describe synthesis and characterization of compounds as potential α-amylase inhibitor.Results: Structure activity relationship (SAR), in vitro analysis and molecular modelling approaches generate compounds 1h, 1i, 1k and 4c as potential lead with good α-amylase inhibitory selection. However, compound 1k failed the criteria of optimization as drug lead by ADME studies while all other compounds showed optimum range for all in silico ADME parameters.Conclusion: Therefore, these compounds can serve as potential lead candidate in developing anti-diabetic therapy.

Calculating the surface energies of crystals on a face-specific and whole particle basis: Case study of the α- and β-polymorphic forms of L-glutamic acid

Molecular-scale modelling for predicting surface energies on a face-specific and whole particle basis is applied to all the crystallographically-independent surfaces of L-glutamic acid forms. The predicted data is found to be in good general agreement with measured surface energies using inverse gas chromatography and Washburn capillary rise techniques with the former revealing higher values compared to the prediction, perhaps consistent with the polar (zwitterionic) nature of this material. This fusion of experimental and computational data provides a high-fidelity definition of the face-by-face breakdown of the energetic anisotropy of the crystals. There is increasing industrial interest in defining the potential impact of whole particle properties on the performance of formulated drug product and their manufacturability especially as the community accelerates the molecule to medicine journey. The overall molecular modelling approach highlights its application in designing ingredients for optimising face-specific particle surface energies for product formulatability particularly in early phase process development.

In-silico screening of Staphylococcus aureus antibacterial agents based on Isatin scaffold: QSAR, molecular docking, molecular dynamics simulation and DFT analysis

The design of novel antimicrobial drugs is necessary due to the increasing resistance to many existing antibiotics. Considering that the drug discovery process requires a lot of time and resources, computational chemistry and molecular docking as the basic components in the drug discovery process, can help the acceleration of this prolonged process and minimise the research costs. In this study, 64 molecules containing the Isatin scaffold were subjected to quantitative structure–activity relationship analysis to identify the necessary features for designing effective antimicrobial agents. Three chemometrics methods were employed to establish a connection between structural parameters and antibacterial effects. MLR was identified as the best model, demonstrating high accuracy compared to the other models. Based on the obtained model, ten novel lead compounds were designed, and their antibacterial potency of these compounds was assessed. The outputs of the molecular docking study revealed that compound S4 fits well in the ATP-binding site and blocks ATPase activity. Additionally, it was observed that compound S4 exhibited adequate binding affinity against the DNA gyrase target. An appropriate relationship was observed between QSAR, DFT and molecular modelling approaches. Therefore, compound S4 can be considered as a promising candidate for further development as an antibacterial agent.

Design of Novel HIV-1 Protease Inhibitors with Favorable Oral Properties using a Molecular Modelling Approach

Acquired immunodeficiency syndrome (AIDS) is a chronic and potentially fatal transmissible disease caused by the Human Immunodeficiency Virus (HIV). Since its discovery in 1981, an estimated 85 million cases and 40 million AIDS related deaths have occurred worldwide. Among the two types of HIV, HIV-1 accounts for over 90% of reported cases. Throughout the years, multiple drugs have been approved for the treatment of AIDS. However, these drugs face many drawbacks such as toxic side effects, non-optimal pharmacodynamic profile and drug resistance due to virus mutation. This study aims to design novel potent HIV-1 protease inhibitors that overcome these drawbacks through molecular modelling methods. Pubchem database was screened for potential lead compounds. Results were filtered through two phases of ADMET and docking studies. Finally, the chosen lead compound was optimized through fragment replacement to obtain the novel inhibitors.

Could Mushrooms' Secondary Metabolites Ameliorate Alzheimer Disease? A Computational Flexible Docking Investigation.

Herein, we highlight the significance of molecular modeling approaches prior to in vitro and invivo studies; particularly, in diseases with no recognized treatments such as neurological abnormalities. Alzheimer disease is a neurodegenerative disorder that causes irreversible cognitive decline. Toxicity and ADMET studies were conducted using the Qikprop platform in Maestro software and Discovery Studio 2.0, respectively, to select the promising skeletons from more than 45 reviewed compounds isolated from mushrooms in the last decade. Using rigid and flexible molecular docking approaches such as induced fit docking (IFD) in the binding sites of β-secretase (BACE1) and acetylcholine esterase (ACHE), promising structures were screened through high precision molecular docking compared with standard drugs donepezil and (2E)-2-imino-3-methyl-5,5-diphenylimidazolidin-4-one (OKK) using Maestro and Cresset Flare platforms. Molecular interactions, binding distances, and RMSD values were measured to reveal key interactions at the binding sites of the two neurodegenerative enzymes. Analysis of IFD results revealed consistent bindings of dictyoquinazol A and gensetin I in the pocket of 4ey7 while inonophenol A, ganomycin, and fornicin fit quite well in 4dju demonstrating binding poses very close to native ligands at ACHE and BACE1. Respective key amino acid contacts manifested the least steric problems according to their Gibbs free binding energies, Glide XP scores, RMSD values, and molecular orientation respect to the key amino acids. Molecular dynamics simulations further confirmed our findings and prospected these compounds to show significant in vitro results in their future pharmacological studies.

Prediction of crude oil physical properties based on molecular compositional modeling approach

AbstractIn this study, the Structural Units‐Bonding Matrix will be introduced into the crude oil characteristics analysis, specifically engineered to depict the molecular intricacies of petroleum substances. This methodology synergizes the bonding matrix with structural units, assigning the bonding matrix to the foundational layer, while the structural units adorn the surface layer. Through meticulous analysis of crude oil data, this study identified the core and branched molecular constituents of crude oil, facilitating the digital articulation of these molecules. Leveraging this foundational work, this study developed composition models for five quintessential types of crude oil, enabling the precise prognostication of their macroscopic physical properties. Further, by evaluating the viscosity‐temperature characteristics of these selected crude oils, this study established a predictive model for the viscosity–temperature relationship of crude oils, grounded in the quantitative structure–property relationship method. This approach not only augments this study understanding of crude oil molecular composition but also enhances the predictive accuracy of their physical properties, heralding a significant advancement in the field of petroleum molecular science.

Deciphering the multi-target therapeutic capabilities of Ocimum tenuiflorum Linn. Compounds against systemic lupus erythematosus and inflammatory bowel disease: a network pharmacology and molecular modelling approach

The coexistence of Systemic Lupus Erythematosus (SLE) and Inflammatory Bowel Disease (IBD) is a rare and hard-to-diagnose multisystem autoimmune disorder. Allopathic treatment approaches often fall short in managing both conditions simultaneously, as specific medications targeting this dual manifestation are lacking. In such instances, herbal medicine can offer a potential solution through its holistic approach. Ocimum tenuiflorum (O. tenuiflorum) a rich source of bioactive compounds belonging to Lamiaceae family. This study employs network pharmacology and molecular modelling to unveil the multi-target and multi-pathway mechanisms of O. tenuiflorum as a complementary therapy. A total of 423 common targets were obtained, among which AKT1, TNF, SRC, EGFR, HIF1A, HSP9AA, BCL2, and STAT3 were identified as the key targets. Lastly, molecular modelling validated the strong binding affinity between O. tenuiflorum ‘s compounds and the identified targets. In conclusion, these investigations provide new insight for further study of O. tenuiflorum towards the management of SLE and IBD.

Pinpointing prime structural attributes of potential MMP-2 inhibitors comprising alkyl/arylsulfonyl pyrrolidine scaffold: a ligand-based molecular modelling approach validated by molecular dynamics simulation analysis

ABSTRACT MMP-2 overexpression is strongly related to several diseases including cancer. However, none of the MMP-2 inhibitors have been marketed as drug candidates due to various adverse effects. Here, a set of sulphonyl pyrrolidines was subjected to validation of molecular modelling followed by binding mode analysis to explore the crucial structural features required for the discovery of promising MMP-2 inhibitors. This study revealed the importance of hydroxamate as a potential zinc-binding group compared to the esters. Importantly, hydrophobic and sterical substituents were found favourable at the terminal aryl moiety attached to the sulphonyl group. The binding interaction study revealed that the S1′ pocket of MMP-2 similar to ‘a basketball passing through a hoop’ allows the aryl moiety for proper fitting and interaction at the active site to execute potential MMP-2 inhibition. Again, the sulphonyl pyrrolidine moiety can be a good fragment necessary for MMP-2 inhibition. Moreover, some novel MMP-2 inhibitors were also reported. They showed the significance of the 3rd position substitution of the pyrrolidine ring to produce interaction inside S2′ pocket. The current study can assist in the design and development of potential MMP-2 inhibitors as effective drug candidates for the management of several diseases including cancers in the future.

Characterization of Phytochemical Inhibitors of the COVID-19 Primary Protease Using Molecular Modelling Approach

Characterization of Phytochemical Inhibitors of the COVID-19 Primary Protease Using Molecular Modelling Approach

Halogen Bonding Hot Spots as a Constraint in Virtual Screening: A Case Study of 5-HT7R.

The recently developed and used molecular modeling approach to search for privileged amino acids for halogen bonding (XB hot spots) through XSAR sets has been applied to 5-HT7R. Herein, among all identified 5-HT7R XB hot spots, the S5x42 was employed in a virtual screening protocol as a constraint. Through a designed virtual screening protocol, 63 XSAR sets (156 compounds) were selected from more than 8 million commercially available compounds and examined using in vitro assay toward 5-HT7R. A 68% accuracy was found in predicting halogenated derivatives with higher affinity for 5-HT7R than their unsubstituted analogs. Moreover, it was observed that a halogen bond formed between S5x42 and a chlorine atom at the 3-position of the arylpiperazine fragment caused the most remarkable, 35.4-fold increase in binding affinity for 5-HT7R when compared to the nonhalogenated analog. Interestingly, molecular dynamics simulations showed the formation of a bifurcated halogen bond with S5x42.

In silico design of multi-epitope vaccines against the hantaviruses by integrated structural vaccinology and molecular modeling approaches.

Hantaviruses are single-stranded RNA viruses belonging to the family Bunyaviridae that causes hantavirus cardiopulmonary syndrome (HCPS) and hemorrhagic fever with renal syndrome (HFRS) worldwide. Currently, there is no effective vaccination or therapy available for the treatment of hantavirus, hence there is a dire need for research to formulate therapeutics for the disease. Computational vaccine designing is currently a highly accurate, time and cost-effective approach for designing effective vaccines against different diseases. In the current study, we shortlisted highly antigenic proteins i.e., envelope, and nucleoprotein from the proteome of hantavirus and subjected to the selection of highly antigenic epitopes to design of next-generation multi-epitope vaccine constructs. A highly antigenic and stable adjuvant was attached to the immune epitopes (T-cell, B-cell, and HTL) to design Env-Vac, NP-Vac, and Com-Vac constructs, which exhibit stronger antigenic, non-allergenic, and favorable physiochemical properties. Moreover, the 3D structures were predicted and docking analysis revealed robust interactions with the human Toll-like receptor 3 (TLR3) to initiate the immune cascade. The total free energy calculated for Env-Vac, NP-Vac, and Com-Vac was -50.02 kcal/mol, -24.13 kcal/mol, and -62.30 kcal/mol, respectively. In silico cloning, results demonstrated a CAI value for the Env-Vac, NP-Vac, and Com-Vac of 0.957, 0.954, and 0.956, respectively, while their corresponding GC contents were 65.1%, 64.0%, and 63.6%. In addition, the immune simulation results from three doses of shots released significant levels of IgG, IgM, interleukins, and cytokines, as well as antigen clearance over time, after receiving the vaccine and two booster doses. Our vaccines against Hantavirus were found to be highly immunogenic, inducing a robust immune response that demands experimental validation for clinical usage.

Exploring the nuclear proteins, viral capsid protein, and early antigen protein using immunoinformatic and molecular modeling approaches to design a vaccine candidate against Epstein Barr virus

The available Epstein Barr virus vaccine has tirelessly harnessed the gp350 glycoprotein as its target epitope, but the result has not been preventive. Right here, we designed a global multi-epitope vaccine for EBV; with special attention to making sure all strains and preventive antigens are covered. Using a robust computational vaccine design approach, our proposed vaccine is armed with 6–16 mers linear B-cell epitopes, 4–9 mer CTL epitopes, and 8–15 mer HTL epitopes which are verified to induce interleukin 4, 10 & IFN-gamma. We employed deep computational mining coupled with expert intelligence in designing the vaccine, using human Beta defensin-3—which has been reported to induce the same TLRs as EBV—as the adjuvant. The tendency of the vaccine to cause autoimmune disorder is quenched by the assurance that the construct contains no EBNA-1 homolog. The protein vaccine construct exhibited excellent physicochemical attributes such as Aliphatic index 59.55 and GRAVY − 0.710; and a ProsaWeb Z score of − 3.04. Further computational analysis revealed the vaccine docked favorably with EBV indicted TLR 1, 2, 4 & 9 with satisfactory interaction patterns. With global coverage of 85.75% and the stable molecular dynamics result obtained for the best two interactions, we are optimistic that our nontoxic, non-allergenic multi-epitope vaccine will help to ameliorate the EBV-associated diseases—which include various malignancies, tumors, and cancers—preventively.

Radioiodinated Tau Imaging Agent III Molecular Modeling, Synthesis, and Evaluation of a New Tau Imaging Agent, [125I]ISAS in Post-Mortem Human Alzheimer's Disease Brain.

Using a molecular modeling approach for Tau-binding sites, we modified our previously reported imaging agent, [125I]INFT, for the potential improvement of binding properties to Tau in an Alzheimer's disease (AD) brain. Two new derivatives, namely [125I]ISAS and [125I]NIPZ, were designed, where binding energies at site 1 of Tau were -7.4 and -6.0 kcal/mole, respectively, compared to [125I]INFT (-7.6 kcal/mole). The radiosynthesis of [125I]ISAS and [125I]NIPZ was carried out by using iodine-125 and purified chromatographically to achieve >90% purity. In vitro binding affinities (IC50) for Tau were as follows: INFT = 7.3 × 10-8 M; ISAS = 4.7 × 10-8 M; NIPZ > 10-6 M. The binding of [125I]ISAS to gray matter (GM) correlated with the presence of Tau in the AD brain, confirmed by anti-Tau immunohistochemistry. [125I]NIPZ did not bind to Tau, with similar levels of binding observed in GM and white matter (WM). Four radiotracers were compared and the rank order of binding to Tau was found to be [125I]IPPI > [125I]INFT > [125I]ISAS >>> [125I]NIPZ with GM/WM ratios of [125I]IPPI = 7.74 > [125I]INFT = 4.86 > [125I]ISAS = 3.62 >> [125I]NIPZ = 1.24. The predictive value of Chimera-AutoDock for structurally related compounds binding to the Tau binding sites (measured as binding energy) was good. A binding energy of less than -7 kcal/mole is necessary and less than -8 kcal/mole will be more suitable for developing imaging agents.