Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Modern drug discovery for inflammatory bowel disease: The role of computational methods

Inflammatory bowel diseases (IBDs) comprising ulcerative colitis, Crohn’s disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract. IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized. Cell therapy, intestinal microecology, apheresis therapy, exosome therapy and small molecules are emerging therapeutic options for IBD. Currently, it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD. Several small molecule inhibitors are being developed as a promising alternative for IBD therapy. The use of highly efficient and time-saving techniques, such as computational methods, is still a viable option for the development of these small molecule drugs. The computer-aided (in silico) discovery approach is one drug development technique that has mostly proven efficacy. Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner. This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods. Some computational approaches to IBD genomic studies, target identification, and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Application of a fluorine strategy in the lead optimization of betulinic acid to the discovery of potent CD73 inhibitors

The ecto-5'-nucleotidase (CD73) is an important enzyme in the adenosine pathway and catalyzes the extracellular hydrolysis of adenosine monophosphate (AMP) yielding adenosine which is involved in the inflammation and immunosuppression. Inhibitors of CD73 have potential as novel immunotherapy agents for the treatment of cancer and infection. In this study, we discovered a series of fluorinated betulinic acid derivatives as potent CD73 inhibitors by a fluorine scanning strategy. Among these, three compounds ZM522, ZM553 and ZM557 exhibited inhibitory activity with IC₅₀ values of 0.56 uM, 0.74 uM and 0.47 uM, respectively. In addition, these compounds showed a 7-fold, 5-fold and 8-fold increase in activity compared to the positive control drug α, β-methylene adenosine diphosphate (APCP) against the human CD73 enzyme. Two of these (ZM522 and ZM553) also exhibited effective interferon gamma (INF-γ) elevation and indicated the regulation of rescued T cell activation. Therefore, our study provides both a lead optimization strategy and potential compounds for further development of small molecule CD73 inhibitors.

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Background

Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA).

Results

The 2D-QSAR modeling results showed GFA-MLR ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9192, Q2 = 0.8767, R2adj = 0.8991, RMSE = 0.0959, $$R_{{{\text{test}}}}^{2}$$ R test 2  = 0.8943, $$R_{{{\text{pred}}}}^{2}$$ R pred 2  = 0.7745) and GFA-ANN ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9227, Q2 = 0.9212, RMSE = 0.0940, $$R_{{{\text{test}}}}^{2}$$ R test 2  = 0.8831, $$R_{{{\text{pred}}}}^{2}$$ R pred 2  = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9620, Q2 = 0.643) and CoMSIA_SED ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.8770, Q2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> − 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents.

Conclusion

The drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski’s rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Influenza virus disease is one of the most infectious diseases responsible for many human deaths, and the high mutability of the virus causes drug resistance effects in recent times. As such, it became necessary to explore more inhibitors that could avert future influenza pandemics. The present research utilized some in-silico modelling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions on some 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase (NA) inhibitors. The 2D-QSAR modelling results revealed GFA-MLR (R train 2 =0.8414, Q2 = 0.7680) and GFA-ANN (R train ​ 2 =0.8754, Q2 = 0.8753) models with the most relevant descriptors (MATS3i, SpMax5_Bhe, minsOH and VE3_D) for predicting the inhibitory activities of the molecules which has passed the global criteria of accepting QSAR models. The results of the 3D-QSAR modelling results showed that CoMFA_ES (R train ​ 2 =0.9030, Q2 = 0.5390) and CoMSIA_EA (R train 2 =0.880, Q2 = 0.547) models are having good predicting ability among other developed models. The molecules were virtually screened via molecular docking simulation with the active site of NA protein receptor (pH1N1) which confirms their resilient potency when compared with zanamivir standard drug. Molecule 11 as the most potent molecule formed more H-bond interactions with the key residues such as TRP178, ARG152, ARG292, ARG371, and TYR406 that triggered the catalytic reactions for NA inhibition. Furthermore, six (6) molecules (9, 10, 11, 17, 22, and 31) with relatively high inhibitory activities and docking scores were identified as the possible leads for in-silico exploration of novel NA inhibitors. The drug-likeness and ADMET predictions of the lead molecules revealed non-violation of Lipinski's rule and good pharmacokinetic profiles respectively, which are important guidelines for rational drug design. Hence, the outcome of this study overlaid a solid foundation for the in-silico design and exploration of novel NA inhibitors with improved potency.

Antioxidant and Antitumor Activities of Newly Synthesized Hesperetin Derivatives

Hesperetin is a class of natural products with a wide range of sources and remarkable biological activities. In this study, we described the synthesis of a series of novel hesperetin derivatives and evaluated the in vitro antioxidant and antitumor activity of these compounds. Eleven novel compounds were synthesized in moderate yields. The compounds synthesized in this work exhibited antioxidant activities against DPPH and ABTS free radicals in a dose-dependent manner. Among them, compound 3f had the best antioxidant activity, with IC₅₀ of 1.2 μM and 24 μM for DPPH and ABTS, respectively. The antitumor activity of the compounds against human cancer cell lines, such as breast MCF-7, liver HepG2, and cervical Hela, was determined by a standard 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay. Three compounds had moderate IC₅₀ values. Interestingly, compound 3f had better biological activity than hesperetin, which matches the prediction by Maestro from Schrödinger. Therefore, the new hesperidin derivative is a promising drug for the treatment of cancer due to its effective antitumor activity. The results also suggested that the antitumor activities of hesperetin derivatives may be related to their antioxidant activities.

Taxifolin Targets PI3K and mTOR and Inhibits Glioblastoma Multiforme

Glioblastoma multiforme (GBM), the most common malignant primary brain tumor, has a very poor prognosis. With increasing knowledge of tumor molecular biology, targeted therapies are becoming increasingly integral to comprehensive GBM treatment strategies. mTOR is a key downstream molecule of the PI3K/Akt signaling pathway, integrating input signals from growth factors, nutrients, and energy sources to regulate cell growth and cell proliferation through multiple cellular responses. mTOR/PI3K dual-targeted therapy has shown promise in managing various cancers. Here, we report that taxifolin, a flavanone commonly found in milk thistle, inhibited mTOR/PI3K, promoted autophagy, and suppressed lipid synthesis in GBM. In silico analysis showed that taxifolin can bind to the rapamycin binding site of mTOR and the catalytic site of PI3K (p110α). In in vitro experiments, taxifolin inhibited mTOR and PI3K activity in five different glioma cell lines. Lastly, we showed that taxifolin suppressed tumors in mice; stimulated expression of autophagy-related genes LC3B-II, Atg7, atg12, and Beclin-1; and inhibited expression of fatty acid synthesis-related genes C/EBPα, PPARγ, FABP4, and FAS. Our observations suggest that taxifolin is potentially a valuable drug for treating GBM.

Functionalized Homologues and Positional Isomers of Rabbit 15-Lipoxygenase RS75091 Inhibitor

BACKGROUND

RS75091 is a cinnamic acid derivative that has been used for the crystallization of the rabbit ALOX15-inhibitor complex. The atomic coordinates of the resolved ALOX15-inhibitor complex were later used to define the binding sites of other mammalian lipoxygenase orthologs, for which no direct structural data with ligand has been reported so far.

INTRODUCTION

The putative binding pocket of the human ALOX5 was reconstructed on the basis of its structural alignment with rabbit ALOX15-RS75091 inhibitor. However, considering the possible conformational changes the enzyme may undergo in solution, it remains unclear whether the existing models adequately mirror the architecture of the ALOX5 active site.

METHODS

In this study, we prepared a series of RS75091 derivatives using a Sonogashira coupling reaction of regioisomeric bromocinnamates with protected acetylenic alcohols and tested their inhibitory properties on rabbit ALOX15.

RESULTS

A bulky pentafluorophenyl moiety linked to either ortho- or metha-ethynylcinnamates via aliphatic spacer does not significantly impair the inhibitory properties of RS75091.

CONCLUSION

Hydroxylated 2- and 3-alkynylcinnamates may be suitable candidates for incorporation of an aromatic linker group like tetrafluorophenylazides for photoaffinity labeling assays.

Design and synthesis of 7-O-1,2,3-triazole hesperetin derivatives to relieve inflammation of acute liver injury in mice

Based on the previous research results of our research group, to further improve the anti-inflammatory activity of hesperetin, we substituted triazole at the 7-OH branch of hesperetin. We also evaluated the anti-inflammatory activity of 39 new hesperetin derivatives. All compounds showed inhibitory effects on nitric oxide (NO) and inflammatory factors in lipopolysaccharide-induced RAW264.7 cells. Compound d5 showed a strong inhibitory effect on NO (half maximal inhibitory concentration = 2.34 ± 0.7 μM) and tumor necrosis factor-α, interleukin (IL)-1β, and (IL-6). Structure-activity relationships indicate that 7-O-triazole is buried in a medium-sized hydrophobic cavity that binds to the receptor. Compound d5 can also reduce the reactive oxygen species production and significantly inhibit the expression of inducible NO synthase and cyclooxygenase-2 through the nuclear factor-κB signaling pathway. In vivo results indicate that d5 can reduce liver inflammation in mice with acute liver injury (ALI) induced by CCI₄. In conclusion, d5 may be a candidate drug for treating inflammation associated with ALI.

A Review on Applications of Computational Methods in Drug Screening and Design

Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.

Improved method of structure-based virtual screening based on ensemble learning

Virtual screening has become a successful alternative and complementary technique to experimental high-throughput screening technologies for drug design. Since the scoring function of docking software cannot predict binding affinity accurately, how to improve the hit rate remains a common issue in structure-based virtual screening. This paper proposed a target-specific virtual screening method based on ensemble learning named ENS-VS. In this method, protein–ligand interaction energy terms and structure vectors of the ligands were used as a combination descriptor. Support vector machine, decision tree and Fisher linear discriminant classifiers were integrated into ENS-VS for predicting the activity of the compounds. The results showed that the enrichment factor (EF) 1% of ENS-VS was 6 times higher than that of Autodock vina. Compared with the newest virtual screening method SIEVE-Score, the mean EF 1% and AUC of ENS-VS (mean EF 1% = 52.77, AUC = 0.982) were statistically significantly higher than those of SIEVE-Score (mean EF 1% = 42.64, AUC = 0.912) on DUD-E datasets; and the mean EF 1% and AUC of ENS-VS (mean EF 1% = 29.73, AUC = 0.793) were also higher than those of SIEVE-Score (mean EF 1% = 25.56, AUC = 0.765) on eight DEKOIS datasets. ENS-VS also showed significant improvements compared with other similar research. The source code is available at https://github.com/eddyblue/ENS-VS.

Virtual screening has become a successful alternative and complementary technique to experimental high-throughput screening technologies for drug design. This paper proposed a target-specific virtual screening method based on ensemble learning named ENS-VS.

In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs

Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies.

The investigation of structure-activity relationship of polyamine-targeted synthetic compounds from different chemical groups

The polyamine (PA) metabolism is involved in cell proliferation and differentiation. Increased cellular PA levels are observed in different types of cancers. Products of PA oxidation induce apoptosis in cancer cells. These observations open a perspective to exploit the enzymes of PA catabolism as a target for anticancer drug design. The substances capable to enhance PA oxidation may become potential anticancer agents. The goal of our study was to explore how the mode of ligand binding with a PA catabolic enzyme is associated with its stimulatory or inhibitory effect upon PA oxidation. Murine N1-acetylpolyamine oxidase (5LFO) crystalline structure was used for molecular docking with ligands of various chemical structures. In vitro experiments were carried out to evaluate the action of the tested compounds upon PA oxidative deamination in a cell-free test system from rat liver. Two amino acid residues (Aps211 and Tyr204) in the structure of 5LFO were found to be significant for binding with the tested compounds. 19 out of 51 screened compounds were activators and 17 were inhibitors of oxidative deamination of PA. Taken together, these results enabled to construct a recognition model with characteristic descriptors depicting activators and inhibitors. The general tendency indicated that a strong interaction with Asp211 or Tyr204 was rather typical for activators. The understanding of how the structure determines the binding mode of compounds with PA catabolic enzyme may help in explanation of their structure-activity relationship and thus promote structure-based drug design.