Application of DFT Calculations in Designing Polymer-Based Drug Delivery Systems: An Overview

Poly[(N-acryloyl glycine)-co-(acrylamide)]-induced cell growth inhibition in heparanase-driven malignancies

In the present work, glycine, the monomer N-acryloylglycine (NAG), and polymeric units of poly[(N-acryloylglycine)-co-(acrylamide)] p(NAG-co-Ac) are examined using density functional theory (DFT), and experimental evidence is provided for their use in the therapy of cancer with a poor prognosis. Glycine plays a pivotal role in cell survival, and most anti-cancer agents alter glycine metabolomics and suppress cancer cell proliferation. Herein, we have utilized Frontier Molecular Orbital theory (FMO), and the results revealed that the introduction of acrylamide/divinyl benzene into the glycine-based polymer increased its biological activity by lowering the energy band gap. Heparanase and proteases are important in invasive tumor progression and worsening of prognosis. In this context, we have synthesized co-polymeric p(NAG-co-Ac) and revealed its protease inhibitory activities. It is revealed that the cross-linked homo-polymeric and cross-linked hetero-polymeric tetrameric arrangements inhibit heparanase activity via interacting at heparanase binding domain II (HBDII) with a docking score of ∼-11.08 kcal mol-1 (Ki) and at heparanase binding domain III (HBD III). The bathochromically shifted CD spectrum shows that the hydrogel interacts with heparanase and disturbs the secondary protein structure of the synthesized p(NAG-co-Ac) polymer. It is found that the synthesized p(NAG-co-Ac) hydrogel has anti-proliferative activity, acts as a migratory inhibitor of cancer cells, and favors programmed cell death. Further, the p(NAG-co-Ac) hydrogel exhibited anti-angiogenic behavior. In conclusion, p(NAG-co-Ac), with its anti-angiogenic and anti-tumorigenic capabilities, has a future as a potential anticancer polymer for the treatment of heparanase-driven invasive malignancies without using any additional anticancer drugs, and is promising for cancer treatment.

Application of Density Functional Theory to Molecular Engineering of Pharmaceutical Formulations

This review systematically examines the pivotal applications of the Density Functional Theory (DFT) in drug formulation design, emphasizing its capability to elucidate molecular interaction mechanisms through quantum mechanical calculations. By solving the Kohn-Sham equations with precision up to 0.1 kcal/mol, DFT enables accurate electronic structure reconstruction, providing theoretical guidance for optimizing drug-excipient composite systems. In solid dosage forms, DFT clarifies the electronic driving forces governing active pharmaceutical ingredient (API)-excipient co-crystallization, predicting reactive sites and guiding stability-oriented co-crystal design. For nanodelivery systems, DFT optimizes carrier surface charge distribution through van der Waals interactions and π-π stacking energy calculations, thereby enhancing targeting efficiency. Furthermore, DFT combined with solvation models (e.g., COSMO) quantitatively evaluates polar environmental effects on drug release kinetics, delivering critical thermodynamic parameters (e.g., ΔG) for controlled-release formulation development. Notably, DFT-driven co-crystal thermodynamic analysis and pH-responsive release mechanism modeling substantially reduce experimental validation cycles. While DFT faces challenges in dynamic simulations of complex solvent environments, its integration with molecular mechanics and multiscale frameworks has achieved computational breakthroughs. This work offers interdisciplinary methodology support for accelerating data-driven formulation design.

In Silico Detection and Conveyance Feasibility of Antifungal Prodrug Flucytosine on the Surface of Pristine and Germanium-Doped SiC Nanosheet

The work describes a novel sensing and transportation feasibility of the well-established antifungal drug Flucytosine (5-FC) using a 2D Silicon carbide (SiC) and Germanium-doped Silicon carbide (Ge@SiC) nanosheet via PBE level of Density functional theory. The computational study revealed that the drug molecules adhere to SiC and Ge@SiC sheets, maintaining their structural properties through physisorption on SiC and chemisorption on Ge@SiC. The charge transfer process associated with the adsorption is observed by Lowdin charge analysis and both the SiC and Ge@SiC sheets are identified as a feasible oxidation-based nanosensor for the drug. The results of electronic property calculation revealed a reduction in bandgap by 48.2% and 44.8% on SiC and Ge@SiC sheets respectively on adsorption of the drug, highlighting SiC nanosheet to be used as a bandgap-based sensing device. Sensing response at room temperature and human body temperature suggested that, the SiC sheet has an excellent selectivity to Flucytosine drug. The drug's desorption efficiency from the carrier is analyzed using recovery time analysis at different temperatures and frequencies, suggesting the SiC nanosheet to be a better candidate. Together, the study highlights the potential sensing ability of SiC nanosheet for Flucytosine in contrast to the existing 0-D nanostructures.

Exploring common genomic biomarkers to disclose common drugs for the treatment of colorectal cancer and hepatocellular carcinoma with type-2 diabetes through transcriptomics analysis

Type 2 diabetes (T2D) is a crucial risk factor for both colorectal cancer (CRC) and hepatocellular carcinoma (HCC). However, so far, there was no study that has investigated common drugs against HCC and CRC during their co-occurrence with T2D patients. Consequently, patients often require multiple disease-specific multiple drugs, which can lead toxicities and adverse effects to the patients due to drug-drug interactions. This study aimed to identify common genomic biomarkers (cGBs) and associated pathogenetic mechanisms underlying CRC, HCC, and T2D to uncover potential common therapeutic compounds against these three diseases. Firstly, we identified 86 common differentially expressed genes (cDEGs) capable of separating each of CRC, HCC and T2D patients from control groups based on transcriptomic profiling. Of these cDEGs, 37 genes were upregulated and 49 were downregulated. Genetic association studies based on average of Log2 fold-change (aLog2FC) of cDEGs suggested a genetic association among CRC, HCC and T2D. Subsequently, six top-ranked cDEGs (MYC, MMP9, THBS1, IL6, CXCL1, and SPP1) were identified as common genomic biomarkers (cGBs) through protein-protein interaction (PPI) network analysis. Further analysis of these cGBs with GO-terms and KEGG pathways revealed shared pathogenetic mechanisms of three diseases, including specific biological processes, molecular functions, cellular components and signaling pathways. The gene co-regulatory network analysis identified two transcription factors (FOXC1 and GATA2) and three miRNAs (hsa-mir-195-5p, hsa-mir-124a-3p, and hsa-mir-34a-5p) as crucial transcriptional and post-transcriptional regulators of the cGBs. Finally, cGBs-guided seven candidate drugs (Digitoxin, Camptosar, AMG-900, Imatinib, Irinotecan, Midostaurin, and Linsitinib) as the common treatment against T2D, CRC and HCC were identified through molecular docking, cross-validation, and ADME/T (Absorption-Distribution-Metabolism-Excretion-Toxicity) analysis. Most of these findings received support by the literature review of diseases specific individual studies. Thus, this study offers valuable insights for researchers and clinicians to improve the diagnosis and treatment of CRC and/or HCC patients during the co-occurrence of T2D.

Experimental and theoretical studies on structural changes in the microtubule affinity-regulating kinase 4 (MARK4) protein induced by N-hetarenes: a new class of therapeutic candidates for Alzheimer's disease

Introduction

Alzheimer's disease (AD) is a neurodegenerative disorder that progressively affects the cognitive function and memory of the affected person. Unfortunately, only a handful of effective prevention or treatment options are available today. Microtubule affinity-regulating kinase 4 (MARK4) is a serine/threonine protein that plays a critical role in regulating microtubule dynamics and facilitating cell division. The dysregulated expression of MARK4 has been associated with a range of diseases, including AD.

Methods

In this study, we synthesized a series of N-hetarenes via Pd(0)-catalyzed Suzuki-Miyaura cross coupling reaction. All compounds were characterized using multi-spectroscopic techniques and evaluated for their activity against the MARK4 enzyme through ATPase inhibition assays. The experimental data was further supported by computational and quantum chemical calculations. We also computed the drug-likeness, bioavailability, and toxicity (ADME/T) profiles of the compounds.

Results

Six new 4-(6-(arylpyrimidin-4-yl)piperazine-1-carboximidamides 5-10 were prepared in good yields. ATPase inhibition assay conducted on these compounds demonstrated IC50 values in micromolar range (5.35 ± 0.22 to 16.53 ± 1.71 μM). Among the tested compounds, 4-(6-(p-tolyl)pyrimidin-4-yl)piperazine-1-carboximidamide (5; IC50 = 5.35 ± 0.22 μM) and 4-(6-(benzo[b]thiophen-2-yl)pyrimidin-4-yl)piperazine-1-carboximidamide (9; IC50 = 6.68 ± 0.80 μM) showed the best activity. The binding constant (K), as determined by the fluorescence quenching assay was estimated to be 1.5 ± 0.51 × 105 M-1 for 5 and 1.14 ± 0.26 × 105 M-1 for 9. The results of molecular docking and MD simulation studies against MARK4 (PDB: 5ES1) indicated that compounds were able to bind the ATP binding pocket of the MARK4, leading to its stabilization. Additionally, ADME/T analysis revealed a high degree of drug-likeness of the compounds.

Conclusion

We demonstrated that 4-(6-(arylpyrimidin-4-yl)piperazine-1-carboximidamides) are a promising class of N-hetarenes for developing next-generation anti-AD drugs. The reported class of compounds inhibited MARK4 activity in-vitro at micromolar concentration by targeting the ATP-binding pocket. These findings provide valuable insights for future drug design.

Plant-derived Bisphenol C is a drug candidate against Nipah henipavirus infection: an in-vitro and in-silico study of Pouzolzia zeylanica (L.) Benn

Pouzolzia zeylanica (PZ) is a widely distributed medicinal herb throughout Bangladesh, especially in tribal regions. The present study focused on evaluating the bioactivity like antioxidant, cytotoxicity, anti-hemolytic activity through in-vitro assessment and predicted potential antiviral compounds against Nipah virus employing in-silico approaches from stem extract of P. zeylanica. The bioactivities of stem extract showed potent antioxidant and anti-hemolytic activity. Comparatively, its cytotoxicity, with an IC50 of 123.786 ± 1.328 µg/ml, suggests moderate toxicity, making it a potential source for therapeutics. Through GCMS analysis, 17 compounds were identified from the stem extract. On the other hand, the potent ligand targeting attachment glycoprotein, the key factor during the host-pathogen attachment and disease (encephalitis) progression, of the Nipah virus (NiV-G) was predicted through in-silico approaches employing ADMET analysis, molecular docking, quantum mechanics (QM) and molecular dynamic simulation (MDS). With a docking score of - 7.4kCal/mol in molecular docking analysis between phytochemicals and NiV-G, Bisphenol-C (CID6620) has been identified as a potent ligand among the phytochemicals present in PZ stem extract. The QM analysis suggests kinetic stability with better chemical reactivity and the docked complex was found stable in MDS for 100 ns. Based on all those evaluations, the compound could be considered a potent ligand for NiV-G and indicates a promising antiviral drug candidate.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00328-2.

DFT investigation of iron-doped boron nitride nanoparticles for anastrozole drug delivery and molecular interaction

The development of efficient drug delivery systems is critical for improving therapeutic outcomes and reducing side effects in cancer treatment. This study investigates the potential of iron-doped boron nitride nanoparticles (Fe-BNNPs) as a nanocarrier for Anastrozole, a key aromatase inhibitor used in breast cancer therapy. Using density functional theory (DFT), we systematically analyzed the interaction mechanisms between Anastrozole and Fe-BNNPs, focusing on binding energies, electronic properties, and structural stability. Our results reveal a strong adsorption of Anastrozole on Fe-BNNPs, with binding energies ranging from - 0.6 to - 1.4 eV, indicating a stable and efficient drug-carrier interaction. Iron doping significantly enhances the reactivity of BNNPs, improving drug loading and release capabilities. Nanoparticles passivated with -H and -OH groups and functionalized with iron nanoclusters were examined, demonstrating that -H passivation yields more stable structures compared to -OH, despite minor variations in electronic properties such as energy gaps (e.g., 2.51 eV for -H vs. 2.54 eV for -OH). The incorporation of iron nanoclusters further increases the binding energy of Anastrozole by approximately 40%, highlighting its role in optimizing drug-nanocarrier interactions. Optical absorption spectra reveal distinct peaks for Anastrozole adsorption on -H and -OH passivated surfaces, providing a clear indicator of interaction states. These findings underscore the potential of Fe-BNNPs as a promising nanocarrier for targeted Anastrozole delivery, offering enhanced precision and therapeutic efficacy.

Molecular Dynamics Simulation of Structural Assembly and Hydration of Hyaluronic Acid in Salt Aqueous Buffer

Hyaluronic acid (HA) is a nonsulfonated glycosaminoglycan critical in tissue development, physiology, and disease processes. To develop biomimetic in vitro models based on HA, it is important to understand the interaction of this polymer in its pristine form and with physiological solvents. However, atomistic simulations of HA chains are computationally challenging, especially when studying interactions with salts. To tackle this challenge, this study combined quantum mechanical (QM) calculations and molecular dynamics (MD) simulations to investigate HA's structure and behavior. This multiscale approach balances accuracy and computational efficiency. QM calculations emphasize the role of weak noncovalent hydrogen bonds in stabilizing d-glucuronic acid with N-acetyl-d-glucosamine. MD results show that more HA layers lead to a larger structure, higher water sensitivity, and increased dynamic and interlayer complexity. Our QM and MD simulations shed light on the structural dynamics and interactions of HA polymers and HA hydrogels, aiding in their design and optimization for biomedical applications and bridging computational and experimental approaches.

Theoretical investigation of structure and electronic properties in Cisplatin-citrate complexes

Cisplatin (CDDP) is an effective Platinum (Pt) based anticancer drug used in chemotherapy. However, its effectiveness is limited due to its instability in solvents, along with the side effects it causes due to DNA damage. Nanoparticles (NPs) were developed in vitro to address these issues by loading CDDP into various types of NPs, including metal, lipid, and biological NPs. Citrate was employed as a biocompatible compound in nanomedicine to reduce cytotoxicity and enhance stability. In our study, the physicochemical and electronic properties of CDDP and citrate have been investigated using density functional theory (DFT), with a comparison of their behavior in water and DMSO. Additionally, TD-DFT was applied to analyze the UV-Vis spectra results. Six complexes have been proposed to better understand the interaction between citrate and CDDP. The results demonstrated that the CDDP could form stable complexes with citrate in both water and DMSO, and the considered complexes exhibited UV-Vis spectra within the experiment range. The frontier orbitals, electron densities mapping, and electrostatic potential analysis revealed that complex 5, where citrate di-substituted on two chlorides, is the most likely and effective complex. In summary, our investigation sheds light on the potential of CDDP-citrate complexes to address the limitations of CDDP, offering insights into their stability and interaction in solvents and highlighting the promising efficacy of specific complex formations for future therapeutic applications.

Genotoxic effects of NDMA-contaminated ranitidine on Allium cepa cells and unveiling carcinogenic mechanisms via DFT and molecular dynamics simulation study

This study investigated the potential genotoxic and carcinogenic effects of N-nitrosodimethylamine (NDMA), a hazardous compound found in ranitidine formulations that are used to treat excessive stomach acid. The study first examined the effects of NDMA-contaminated ranitidine formulation on Allium cepa root growth and mitotic activity. The results demonstrated dose-dependent decreases in both root growth and mitotic index indicating genotoxicity and cell division disruption. Elevated concentrations of ranitidine correlated with increased chromosomal aberrations indicating genotoxic capabilities. These outcomes underscored that NDMA contaminated ranitidine exposure triggers genotoxicity hampering cell division and inducing chromosomal aberrations. Electronic characteristics of NDMA revealed its electrophilic nature suggesting its capability to create covalent adducts with DNA bases fostering genotoxic and carcinogenic characteristics. Molecular docking analysis showed the interactions of NDMA with DNA including hydrogen bonds and carbon-hydrogen interactions with nucleotide bases forming DNA adducts. Molecular dynamics simulations showcased the dynamic behavior of the DNA-NDMA complex over time with structural fluctuations. Dynamic hydrogen bond fluctuations implied interactive intricacies between solute and solvent molecules. Overall, this study illuminates how NDMA-contaminated ranitidine could trigger DNA damage and potentially contribute to carcinogenesis. It emphasizes the urgency of minimizing exposure to this perilous and hazardous compound.

Innovative Use of an Injectable, Self-Healing Drug-Loaded Pectin-Based Hydrogel for Micro- and Supermicro-Vascular Anastomoses

Microvascular surgery plays a crucial role in reconnecting micrometer-scale vessel ends. Suturing remains the gold standard technique for small vessels; however, suturing the collapsed lumen of microvessels is challenging and time-consuming, with the risk of misplaced sutures leading to failure. Although multiple solutions have been reported, the emphasis has predominantly been on resolving challenges related to arteries rather than veins, and none has proven superior. In this study, we introduce an innovative solution to address these challenges through the development of an injectable lidocaine-loaded pectin hydrogel by using computational and experimental methods. To understand the extent of interactions between the drug and the pectin chain, molecular dynamics (MD) simulations and quantum mechanics (QM) calculations were conducted in the first step of the research. Then, a series of experimental studies were designed to prepare lidocaine-loaded injectable pectin-based hydrogels, and their characterization was performed by using Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and rheological analysis. After all the results were evaluated, the drug-loaded pectin-based hydrogel exhibiting self-healing properties was selected as a potential candidate for in vivo studies to determine its performance during operation. In this context, the hydrogel was injected into the divided vessel ends and perivascular area, allowing for direct suturing through the gel matrix. While our hydrogel effectively prevented vasospasm and facilitated micro- and supermicro-vascular anastomoses, it was noted that it did not cause significant changes in late-stage imaging and histopathological analysis up to 6 months. We strongly believe that pectin-based hydrogel potentially enhanced microlevel arterial, lymphatic, and particularly venous anastomoses.

Density functional theory (DFT) studies in HDAC-based chemotherapeutics: Current findings, case studies and future perspectives

Density Functional Theory (DFT) is a quantum chemical computational method used to predict and analyze the electronic properties of atoms, molecules, and solids based on the density of electrons rather than wavefunctions. It provides insights into the structure, bonding, and behavior of different molecules, including those involved in the development of chemotherapeutic agents, such as histone deacetylase inhibitors (HDACis). HDACs are a wide group of metalloenzymes that facilitate the removal of acetyl groups from acetyl-lysine residues situated in the N-terminal tail of histones. Abnormal HDAC recruitment has been linked to several human diseases, especially cancer. Therefore, it has been recognized as a prospective target for accelerating the development of anticancer therapies. Researchers have studied HDACs and its inhibitors extensively using a combination of experimental methods and diverse in-silico approaches such as machine learning and quantitative structure-activity relationship (QSAR) methods, molecular docking, molecular dynamics, pharmacophore mapping, and more. In this context, DFT studies can make significant contribution by shedding light on the molecular properties, interactions, reaction pathways, transition states, reactivity and mechanisms involved in the development of HDACis. This review attempted to elucidate the scope in which DFT methodologies may be used to enhance our comprehension of the molecular aspects of HDAC inhibitors, aiding in the rational design and optimization of these compounds for therapeutic applications in cancer and other ailments. The insights gained can guide experimental efforts toward developing more potent and selective HDAC inhibitors.

DFT/TDDFT calculations of geometry optimization, electronic structure and spectral properties of clevudine and telbivudine for treatment of chronic hepatitis B

Chronic hepatitis B remains a worldwide health concern. Presently, many drugs, such as Clevudine and Telbivudine, are recommended for the treatment of chronic hepatitis B disease. For this purpose, the quantum chemical analysis of ELUMO-HOMO (Egap), ionization potential (IP), electron affinity (EA), electronegativity (EN), chemical hardness (η), chemical potential (μ), chemical softness (S), electrophilicity index (ω), electron accepting capability (ω+), electron-donating capability (ω-), Nucleophilicity index (N), additional electronic charge (∆Nmax), Optical softness (σ0) and Dipole Moment, IR and UV-Vis spectra, molecular electrostatic potential (MEP) profile, Mulliken charge analysis, natural bond orbital (NBO) were examined in this study. The dipole moment of the compounds suggests their binding pose and predicted binding affinity. The electrophilic and nucleophilic regions were identified, and techniques such as NBO, UV-Vis, and IR were used to gain insights into the molecular structure, electronic transitions, and potential drug design for Hepatitis B treatment. Calculations for this study were carried out using the Gaussian 09 program package coupled with the DFT/TDDFT technique. The hybrid B3LYP functional method and the 6-311++G(d, p) basis set were used for the calculations.

Design of new molecules against cervical cancer using DFT, theoretical spectroscopy, 2D/3D-QSAR, molecular docking, pharmacophore and ADMET investigations

Cervical cancer is a major health problem of women. Hormone therapy, via aromatase inhibition, has been proposed as a promising way of blocking estrogen production as well as treating the progression of estrogen-dependent cancer. To overcome the challenging complexities of costly drug design, in-silico strategy, integrating Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD), was applied to large representative databases of 39 quinazoline and thioquinazolinone compound derivatives. Quantum chemical and physicochemical descriptors have been investigated using density functional theory (DFT) and MM2 force fields, respectively, to develop 2D-QSAR models, while CoMSIA and CoMFA descriptors were used to build 3D-QSAR models. The robustness and predictive power of the reliable models were verified, via several validation methods, leading to the design of 6 new drug-candidates. Afterwards, 2 ligands were carefully selected using virtual screening methods, taking into account the applicability domain, synthetic accessibility, and Lipinski's criteria. Molecular docking and pharmacophore modelling studies were performed to examine potential interactions with aromatase (PDB ID: 3EQM). Finally, the ADMET properties were investigated in order to select potential drug-candidates against cervical cancer for experimental in vitro and in vivo testing.

Pentafuhalol-B, a Phlorotannin from Brown Algae, Strongly Inhibits the PLK-1 Overexpression in Cancer Cells as Revealed by Computational Analysis

Polo-like kinase-1 (PLK-1) is an essential mitotic serine/threonine (Ser/Thr) kinase that belongs to the Polo-like kinase (PLK) family and is overexpressed in non-small cell lung cancer (NSCLC) via promotion of cell division. Therefore, PLK-1 may act as a promising target for the therapeutic cure of various cancers. Although a variety of anti-cancer drugs, both synthetic and naturally occurring, such as volasertib, onvansertib, thymoquinone, and quercetin, are available either alone or in combination with other therapies, they have limited efficacy, especially in the advanced stages of cancer. To the best of our knowledge, no anticancer agent has been reported from marine algae or microorganisms to date. Thus, the aim of the present study is a high-throughput virtual screening of phlorotannins, obtained from edible brown algae, using molecular docking and molecular dynamic simulation analysis. Among these, Pentafuhalol-B (PtB) showed the lowest binding energy (best of triplicate runs) against the target protein PLK-1 as compared to the reference drug volasertib. Further, in MD simulation (best of triplicate runs), the PtB-PLK-1 complex displayed stability in an implicit water system through the formation of strong molecular interactions. Additionally, MMGBSA calculation (best of triplicate runs) was also performed to validate the PtB-PLK-1 complex binding affinities and stability. Moreover, the chemical reactivity of PtB towards the PLK-1 target was also optimised using density functional theory (DFT) calculations, which exhibited a lower HOMO-LUMO energy gap. Overall, these studies suggest that PtB binds strongly within the pocket sites of PLK-1 through the formation of a stable complex, and also shows higher chemical reactivity than the reference drug volasertib. The present study demonstrated the inhibitory nature of PtB against the PLK-1 protein, establishing its potential usefulness as a small molecule inhibitor for the treatment of different types of cancer.

Structure-based drug design, molecular dynamics simulation, ADMET, and quantum chemical studies of some thiazolinones targeting influenza neuraminidase

The genetic mutability of the influenza virus leads to the existence of drug-resistant strains which is dangerous, particularly with the lingering coronavirus disease (COVID-19). This necessitated the need for the search and discovery of more potential anti-influenza agents to avert future outbreaks. In furtherance of our previous in-silico studies on 5-benzyl-4-thiazolinones as anti-influenza neuraminidase (NA) inhibitors, molecule 11 was selected as the template scaffold for the structure-based drug design due to its good binding, pharmacokinetic profiling, and better NA inhibitory activity. As such, eighteen (18) new molecules (11a-r) were designed with better MolDock scores as compared with the template scaffold and the zanamivir reference drug. However, the dynamic stability of molecule 11a in the binding cavity of the NA target (3TI5) showed water-mediated hydrogen and hydrophobic bondings with the active residues such as Arg118, Ile149, Arg152, Ile222, Trp403, and Ile427 after the MD simulation for 100 ns. The drug-likeness and ADMET assessment of all designed molecules predicted non-violation of the stipulated thresholds of Lipinski's rule and good pharmacokinetic properties respectively. In addition, the quantum chemical calculations also suggested the significant chemical reactivity of molecules with their smaller band energy gap, high electrophilicity, high softness, and low hardness. The results obtained in this study proposed a reliable in-silico viewpoint for anti-influenza drug discovery and development.Communicated by Ramaswamy H. Sarma.

Development of QSRR model for hydroxamic acids using PCA-GA-BP algorithm incorporated with molecular interaction-based features

As a potent zinc chelator, hydroxamic acid has been applied in the design of inhibitors of zinc metalloenzyme, such as histone deacetylases (HDACs). A series of hydroxamic acids with HDAC inhibitory activities were subjected to the QSRR (Quantitative Structure-Retention Relationships) study. Experimental data in combination with calculated molecular descriptors were used for the development of the QSRR model. Specially, we employed PCA (principal component analysis) to accomplish dimension reduction of descriptors and utilized the principal components of compounds (16 training compounds, 4 validation compounds and 7 test compounds) to execute GA (genetic algorithm)-BP (error backpropagation) algorithm. We performed double cross-validation approach for obtaining a more convincing model. Moreover, we introduced molecular interaction-based features (molecular docking scores) as a new type of molecular descriptor to represent the interactions between analytes and the mobile phase. Our results indicated that the incorporation of molecular interaction-based features significantly improved the accuracy of the QSRR model, (R2 value is 0.842, RMSEP value is 0.440, and MAE value is 0.573). Our study not only developed QSRR model for the prediction of the retention time of hydroxamic acid in HPLC but also proved the feasibility of using molecular interaction-based features as molecular descriptors.