3D Quantitative Structure-activity Relationship Models Research Articles

Integration of 3D-QSAR, molecular docking, and machine learning techniques for rational design of nicotinamide-based SIRT2 inhibitors

Selective inhibitors of sirtuin-2 (SIRT2) are increasingly recognized as potential therapeutics for cancer and neurodegenerative diseases. Derivatives of 5-((3-amidobenzyl)oxy)nicotinamides have been identified as some of the most potent and selective SIRT2 inhibitors reported to date (​Ai et al., 2016​; ​Ai et al., 2023​, ​Baroni et al., 2007​). In this study, a 3D-QSAR (3D-Quantitative Structure-Activity Relationship) model was developed using a dataset of 86 nicotinamide-based SIRT2 inhibitors from the literature, along with GRIND-derived pharmacophore models for selected inhibitors. External validation parameters emphasized the reliability of the 3D-QSAR model in predicting SIRT2 inhibition within the defined applicability domain. The interpretation of the 3D-QSAR model facilitated the generation of GRIND-derived pharmacophore models, which in turn enabled the design of novel SIRT2 inhibitors. Furthermore, based on molecular docking results for the SIRT1–3 isoforms, two classification models were developed: a SIRT1/2 model using the Naive Bayes algorithm and a SIRT2/3 model using the k-nearest neighbors algorithm, to predict the selectivity of inhibitors for SIRT1/2 and SIRT2/3. External validation parameters of the selectivity models confirmed their predictive power. Ultimately, the integration of 3D-QSAR, selectivity models and prediction of ADMET properties facilitated the identification of the most promising selective SIRT2 inhibitors for further development.

Discovery of Novel Acethydrazide-Containing Flavonol Derivatives as Potential Antifungal Agents.

In this study, a series of novel hydrazide-containing flavonol derivatives was designed, synthesized, and evaluated for antifungal activity. In the in vitro antifungal assay, most of the target compounds exhibited potent antifungal activity against seven tested phytopathogenic fungi. In particular, compound C32 showed the best antifungal activity against Rhizoctonia solani (EC50 = 0.170 μg/mL), outperforming carbendazim (EC50 = 0.360 μg/mL) and boscalid (EC50 = 1.36 μg/mL). Compound C24 exhibited excellent antifungal activity against Valsa mali, Botrytis cinerea, and Alternaria alternata with EC50 values of 0.590, 0.870, and 1.71 μg/mL, respectively. The in vivo experiments revealed that compounds C32 and C24 were potential novel agricultural antifungals. 3D quantitative structure-activity relationship (3D-QSAR) models were used to analyze the structure-activity relationships of these compounds. The analysis results indicated that introducing appropriate electronegative groups at position 4 of a benzene ring could effectively improve the anti-R. solani activity. In the antifungal mechanism study, scanning electron microscopy and transmission electron microscopy analyses revealed that C32 disrupted the normal growth of hyphae by affecting the structural integrity of the cell membrane and cellular respiration. Furthermore, compound C32 exhibited potent succinate dehydrogenase (SDH) inhibitory activity (IC50 = 8.42 μM), surpassing that of the SDH fungicide boscalid (IC50 = 15.6 μM). The molecular dynamics simulations and docking experiments suggested that compound C32 can occupy the active site and form strong interactions with the key residues of SDH. Our findings have great potential for aiding future research on plant disease control in agriculture.

3D-QSAR-Directed Synthesis of Halogenated Coumarin-3-Hydrazide Derivatives: Unveiling Their Potential as SDHI Antifungal Agents.

The pursuit of new succinate dehydrogenase (SDH) inhibitors is a leading edge in fungicide research and development. The use of 3D quantitative structure-activity relationship (3D-QSAR) models significantly enhances the development of compounds with potent antifungal properties. In this study, we leveraged the natural product coumarin as a molecular scaffold to synthesize 74 novel 3-coumarin hydrazide derivatives. Notably, compounds 4ap (0.28 μg/mL), 6ae (0.32 μg/mL), and 6ah (0.48 μg/mL) exhibited exceptional in vitro effectiveness against Rhizoctonia solani, outperforming the commonly used fungicide boscalid (0.52 μg/mL). Furthermore, compounds 4ak (0.88 μg/mL), 6ae (0.61 μg/mL), 6ah (0.65 μg/mL), and 6ak (1.11 μg/mL) showed significant activity against Colletotrichum orbiculare, surpassing both the SDHI fungicide boscalid (43.45 μg/mL) and the broad-spectrum fungicide carbendazim (2.15 μg/mL). Molecular docking studies and SDH enzyme assays indicate that compound 4ah may serve as a promising SDHI fungicide. Our ongoing research aims to refine this 3D-QSAR model further, enhance molecular design, and conduct additional bioactivity assays.

3D-QSAR, ADMET, and molecular docking studies of aztreonam analogs as E. colis inhibitors.

The development of multidrug resistant strains of extended-spectrum β-lactamase-producing Escherichia coli has become a global problem; therefore, the discovery of new antibacterial agents is the only available solution. To improve and propose new compounds with antibacterial activity, the three-dimensional quantitative structure-activity relationship and molecular docking studies were carried out on Aztreonam analogs as E. coli inhibitors in DNA gyrase B. This study's 3D-Quantitative structure-activity relationship model was created using on the Comparative Molecular Field Analysis and the Comparative Molecular Similarity Indices Analysis. Using the Comparative Molecular Field Analysis (Q 2 = 0.73; R 2 = 0.82), excellent predictability was achieved, and the best Comparative Molecular Similarity Indices Analysis model (Q 2 = 0.88; R 2 = 0.9). The generated model's ability to predict outcomes was assessed through external validation using a test set compound and an applicability domain technique. In this study, the steric, electrostatic, and hydrogen bond acceptor fields played a key role in antibacterial activity. The results of the molecular docking revealed that the newly generated compound A6 has the highest binding affinity with DNA gyrase B. It forms 10 hydrogen bonds with amino acid residues of Asn104, Asn274, Asn132, Ser70, Ser237, Thr105, Glu273, and 2 salt bridges with amino acid residues of Ser70 and Glu273 and one pi-pi interacting with Gys271 amino acid residue in the binding site of 5G1, and this result was validated by a new assessment method. We created some novel, highly effective DNA gyrase B inhibitors based on the earlier findings, and the most accurate model predicted their inhibitory actions. The ADMET characteristics and pharmacological similarity of these novel inhibitors were also examined. These findings would be very beneficial in guiding the optimization process for the identification of novel drugs that can address the issue of multiple drug resistance.

Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease.

The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo∧2 = 0.5548, Q_lto∧2 = 0.5278, R∧2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo∧2 = 0.7033, Q_lto∧2 = 0.6827, Q_lmo∧2 = 0.6305, R∧2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria's upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4's stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.

Exploration of Structure-Activity Relationship Using Integrated Structure and Ligand Based Approach: Hydroxamic Acid-Based HDAC Inhibitors and Cytotoxic Agents.

The present study aimed to establish significant and validated quantitative structure-activity relationship (QSAR) models for histone deacetylase (HDAC) inhibitors and correlate their physicochemical, steric, and electrostatic properties with their anticancer activity. We have selected a dataset from earlier research findings. The target and ligand molecules were procured from recognized databases and incorporated into pivotal findings such as molecular docking (XP glide), e-pharmacophore study and 3D QSAR model designing study (phase). Docking revealed molecule 39 with better docking score and well binding contact with the protein. 3D QSAR analysis, which was performed for partial least squares factor 5 reported good 0.9877 and 0.7142 as R2 and Q2 values and low standard of deviation: 0.1049 for hypothesis AADRR.139. Based on the computational outcome, it has been concluded that molecule 39 is an effective and relevant candidate for inhibition of HDAC activity. Moreover, these computational approaches motivate to discover novel drug candidates in pharmacological and healthcare sectors.

High accuracy prediction of dipeptide angiotensin-converting enzyme (ACE) inhibitory activity by improving the credibility of the 3D-quantitative structure-activity relationship (3D-QSAR) model database and investigating inhibition mechanism

The 3D-QSAR model is one of the most effective techniques for predicting the peptide bioactivity based on their structure properties. The accuracy of model prediction is highly dependent on the bio-activity of the peptides in its dataset. However, the same peptide used for constructing the database was reported to have obviously different activities in different published articles, which may significantly decrease the prediction accuracy. Based on this, we chose ACE inhibitory (ACE-I) dipeptides that can be absorbed directly into the human small intestine as research object. Ten dipeptides were randomly selected from the literatures for molecular docking with ACE and synthesized to test their activity. Ultimately, a linear equation was developed between bio-activities and docking scores, which was used to revalidate the reliable ACE-I activities of all reported dipeptides for constructing a credible dataset of 3D-QSAR model. The prediction accuracy was significantly enhanced and the model we established had a high Q2 = 0.840 and R2 = 0.998. Furthermore, dipeptide CW was predicted and tested in vitro to achieve the highest ACE-I activity (IC50 = 0.16 μM), and the inhibition mechanism was also investigated. This study introduced a new method for predicting bio-activity with high precision using the 3D-QSAR model.

New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods.

To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure-activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R2 = 0.900 (training set); Q2 = 0.774 and Pearson's R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.

Molecular Design and Mechanism Analysis of Phthalic Acid Ester Substitutes: Improved Biodegradability in Processes of Sewage Treatment and Soil Remediation.

Phthalic acid esters (PAEs) have the characteristics of environmental persistence. Therefore, improving the biodegradability of PAEs is the key to reducing the extent of ecological harm realized. Firstly, the scoring function values of PAEs docking with various degrading enzymes in sewage treatment were calculated. Based on this, a 3D-quantitative structure-activity relationship (3D-QSAR) model for PAE biodegradability was built, and 38 PAE substitutes were created. By predicting the endocrine-disrupting toxicity and functions of PAE substitutes, two types of PAE substitutes that are easily degraded by microorganisms, have low toxicity, and remain functional were successfully screened. Meanwhile, the differences in the mechanism of molecular degradation difference before and after PAE modification were analyzed based on the distribution characteristics of amino acid residues in the molecular docking complex. Finally, the photodegradability and microbial degradability of the PAE substitutes in the soil environment was evaluated. From the 3D-QSAR model design perspective, the modification mechanism of PAE substitutes suitable for sewage treatment and soil environment degradation was analyzed. We aim to improve the biodegradability of PAEs at the source and provide theoretical support for alleviating the environmental hazards of using PAEs.

3D-QSAR assisted identification of selective CYP1B1 inhibitors: an effective bioisosteric replacement/molecular docking/electrostatic complementarity analysis.

Cytochrome P450-1B1 is a majorly overexpressed drug-metabolizing enzyme in tumors and is responsible for inactivation and subsequent resistance to a variety of anti-cancer drugs, i.e., docetaxel, tamoxifen, and cisplatin. In the present study, a 3D quantitative structure-activity relationship (3D-QSAR) model has been constructed for the identification, design, and optimization of novel CYP1B1 inhibitors. The model has been built using a set of 148 selective CYP1B1 inhibitors. The developed model was evaluated based on certain statistical parameters including q2 and r2 which showed the acceptable predictive and descriptive capability of the generated model. The developed 3D-QSAR model assisted in understanding the key molecular fields which were firmly related to the selective CYP1B1 inhibition. A theoretic approach for the generation of new lead compounds with optimized CYP1B1 receptor affinity has been performed utilizing bioisosteric replacement analysis. These generated molecules were subjected to a developed 3D-QSAR model to predict the inhibitory activity potentials. Furthermore, these compounds were scrutinized through the activity atlas model, molecular docking, electrostatic complementarity, molecular dynamics, and waterswap analysis. The final hits might act as selective CYP1B1 inhibitors which could address the issue of resistance. This 3D-QSAR includes several chemically diverse selective CYP1B1 receptor ligands and well accounts for the individual ligand's inhibition affinities. These features of the developed 3D-QSAR model will ensure future prospective applications of the model to speed up the identification of new potent and selective CYP1B1 receptor ligands.

Synthesis of (E)/(Z)-Verbenone Oxime Ethers and Photoresponsive Behavior to Herbicidal Activity.

The accumulation of residual active herbicides in the environment can cause a series of problems. It is thus meaningful to explore a photoresponsive herbicide, whose activity can be weakened under the action of light to reduce the negative effect. To this purpose, a series of (E)/(Z)-verbenone oxime ethers were designed, synthesized, and characterized. Oxime ether groups were adopted as the trigger switches. The preliminary screening for herbicidal activity showed that some of them exhibited better or comparable effects than that of the commercial herbicide flumioxazin against Brassica campestris and Echinochloa crusgalli. Meanwhile, five pairs of the target compounds with significantly different herbicidal effects between E- and Z-forms were further investigated for their reversible isomerization reaction and the accompanying variation of herbicidal activity. As a result, the maximum conversion rates were around 50%, and the herbicidal effect of the resulting mixture of E- and Z-isomers decreased outstandingly. The phototransformation mechanism of a pair of isomers (E)-4a and (Z)-4a was preliminarily explored. Besides, a reasonable and effective 3D-quantitative structure-activity relationship model (r2 = 0.984 and q2 = 0.571) was established and the binding mode was also investigated by molecular docking.

Investigation of structure–activity relationship: In silico studies of [1, 2, 4]triazolo[4, 3-a]pyridine ureas as P38 kinase inhibitors

P38 kinases are the members of serine/threonine kinases family and play a vital role in the progression of inflammation. In the past two decades, numerous p38 kinase inhibitors have been reported, and few of them have failed in clinical trials. Recently, some of the p38 kinase inhibitors have entered in clinical trials for the treatment of Alzheimer’s disease. A potential opportunity exists for medicinal chemistry for the discovery of potent and safe p38 kinase inhibitors. In view of this challenging opportunity, the present manuscript is aimed towards development of a 3D quantitative structure–activity relationship (QSAR) model and the docking and dynamic simulation studies. A statistically robust 3D QSAR model was developed by employing 21 training set molecules which is attributed with appreciating cross-validation coefficient (q2) of 0.0.6269 and conventional correlation coefficient (r2) of 0.8783 respectively. The predicted correlation coefficient (r2 pred) was found to be 0.8644 and standard error of 0.3331. The molecular docking analysis of all the p38 kinase inhibitors revealed that the analogs were well docked into the DFG (Asp-Phe-Gly motif) out pocket of p38 kinase and exhibited hydrogen bond interactions with Asp186 and Lys71. Extension of docking studies to the molecular dynamics simulation study informed that the ligand displayed the strong conformational stability within the active site of p38 kinase forming maximum two hydrogen bonds until 100 ns respectively.

Discovery of new chemotypes of dual 5-HT2A/D2 receptor antagonists with a strategy of drug design methodologies.

Aim: Through the application of structure- and ligand-based methods, the authors aimed to create an integrative approach to developing a computational protocol for the rational drug design of potent dual 5-HT2A/D2 receptor antagonists without off-target activities on H1 receptors. Materials & methods: Molecular dynamics and virtual docking methods were used to identify key interactions of the structurally diverse antagonists in the binding sites of the studied targets, and to generate their bioactive conformations for further 3D-quantitative structure-activity relationship modeling. Results & conclusion: Toward the goal of finding multi-potent drugs with a more effective and safer profile, the obtained results led to the design of a new set of dual antagonists and opened a new perspective on the therapy for complex brain diseases.

QSAR tool for optimization of nitrobenzamide pharmacophore for antitubercular activity

Tuberculosis (TB) is a leading cause of death worldwide from a single infectious agent, Mycobacterium tuberculosis (MTB), especially due to the development of resistant strains and its co-infections in HIV. Quantitative-structure activity relationship (QSAR) studies aid rapid drug discovery. In this work, 2D and 3D QSAR studies were carried out on a series of nitrobenzamide derivatives to design newer analogues for antitubercular activity. 2D QSAR was performed using MLR on a data set showing antitubercular activity. The 3D-QSAR studies were performed by kNN–MFA using simulated annealing variable selection method. Alignment of given set of molecules was carried out by the template-based alignment method and then was used to build the 3D-QSAR model. Robustness and predictive ability of the models were evaluated by using various traditional validating parameters. Different physiochemical, alignment-based, topological, electrostatic, and steric descriptors were generated, which indicated the key structural requirements for optimizing the pharmacophore for better antitubercular activity. For 2D QSAR, the best statistical model was generated using SA-MLR method (r2 = 0.892, q2 = 0.819) while 3D QSAR model was derived using the SA KNN method (q2 = 0.722). The positively contributing descriptors can be incorporated to design new chemical entities for future study.

Development of 2D and 3D Quantitative Structure Activity Relationship Models of Thiazole Derivatives for Antimicrobial Activity

A series of 20 molecules of Aryl Thiazole derivatives reported in literature Khan M S et al (2009) were used for development of 2D and 3D quantitative structure activity relationship (QSAR) models. The data set of 20 molecules were divided into training and test set in the ratio of 70:30, The biological activity was converted to logarithmic scale (pIC50) in mathematical operation mode of the software. The statistically significant 2D-QSAR models for G+ inhibition activity are r2 =0.9521 and q2 = 0.8619 and 3D QSAR results for internal (q2 = 0.8283,) and external (predictive r2 = 0.4868,) validation criteria. Thus, 3D QSAR models showed that electrostatic effects at the area of [E_685 (-10.00-0.10.00)+E_460(-0.4530-0.273)+E_531(-0.6212-0.2506)]dominantly determine the binding affinities. 2D QSAR studies revealed that T_C_C_4 descriptors were major contributing descriptor in case of G+ inhibition activity. 3D QSAR Methods were performed using kNN-MFA method. The results derived may be useful in further designing novel more potent agents.

Structure-Activity Relationship Studies on Novel Antiviral Agents for Norovirus Infections.

Human norovirus is the leading cause of acute gastroenteritis worldwide, affecting every year 685 million people. Norovirus outbreaks are associated with very significant economic losses, with an estimated societal cost of 60 billion USD per year. Despite this, no therapeutic options or vaccines are currently available to treat or prevent this infection. An antiviral therapy that can be used as treatment and as a prophylactic measure in the case of outbreaks is urgently needed. We previously described the computer-aided design and synthesis of novel small-molecule agents able to inhibit the replication of human norovirus in cell-based systems. These compounds are non-nucleoside inhibitors of the viral polymerase and are characterized by a terminal para-substituted phenyl group connected to a central phenyl ring by an amide-thioamide linker, and a terminal thiophene ring. Here we describe new modifications of these scaffolds focused on exploring the role of the substituent at the para position of the terminal phenyl ring and on removing the thioamide portion of the amide-thioamide linker, to further explore structure-activity relationships (SARs) and improve antiviral properties. According to three to four-step synthetic routes, we prepared thirty novel compounds, which were then evaluated against the replication of both murine (MNV) and human (HuNoV) norovirus in cells. Derivatives in which the terminal phenyl group has been replaced by an unsubstituted benzoxazole or indole, and the thioamide component of the amide-thioamide linker has been removed, showed promising results in inhibiting HuNoV replication at low micromolar concentrations. Particularly, compound 28 was found to have an EC50 against HuNoV of 0.9 µM. Although the most active novel derivatives were also associated with an increased cytotoxicity in the human cell line, these compounds represent a very promising starting point for the development of new analogues with reduced cytotoxicity and improved selectivity indexes. In addition, the experimental biological data have been used to create an initial 3D quantitative structure-activity relationship model, which could be used to guide the future design of novel potential anti-norovirus agents.

Design and identification of two novel resveratrol derivatives as potential LSD1 inhibitors.

Background: Overexpression of LSD1 is associated with the occurrence of many diseases, including cancers, which makes LSD1 a significant target for anticancer drug research. Methodology & Results: With the aid of 3D quantitative structure-activity relationship models established with 34 reported resveratrol derivativeLSD1 inhibitors, derivatives 35-40 were designed. Absorption, distribution, metabolism and excretion calculations showed that they may have good bioavailability and drug likeness. Additionally, 35 and 37 presented good antitumor effects in an in vitro antiproliferative assay. Molecular docking and molecular dynamics simulationresults indicated that 35 and 37 can establish extensive interactions with LSD1. Conclusion: The results of computational prediction and experimental validation suggest that 35 and 37 are effective antitumor inhibitors, which provides some ideas and directions for the development of new anticancer LSD1 inhibitors.

Molecular modeling studies of [4-(3H-benzoimidazol-5-yl)-pyrimidin-2-yl]-amine-based CDK4 inhibitors.

Aim: CDK4 is a promising target for breast cancer therapy. This study aimed to explore the structure-activity relationship of CDK4 inhibitor abemaciclib analogs and design potent CDK4 inhibitors for breast cancer treatment. Methods & results: A faithful 3D quantitative structure-activity relationship model was established by molecular docking, comparative molecular field analysis and comparative molecular similarity index analysis based on 56 abemaciclib analogs. Molecular dynamics simulation studies revealed the key residues of the interaction between CDK4 and inhibitors. Four novel inhibitors with satisfactory predicted binding affinity to CDK4 were designed. Conclusion: The 3D quantitative structure-activity relationship and molecular dynamics simulation studies provide valuable insight into the development of novel CDK4 inhibitors.

QSAR Studies on Neuraminidase Inhibitors as Anti-influenza Agents

The present study aimed to establish significant and validated quantitative structure-activity relationship (QSAR) models for neuraminidase inhibitors and correlate their physicochemical, steric, and electrostatic properties with their anti-influenza activity. We have developed and validated 2D and 3D QSAR models by using multiple linear regression, partial least square regression, and k-nearest neighbor-molecular field analysis methods. 2D QSAR models had q2: 0.950 and pred_r2: 0.877 and 3D QSAR models had q2: 0.899 and pred_r2: 0.957. These results showed that the models werere predictive. Parameters such as hydrogen count and hydrophilicity were involved in 2D QSAR models. The 3D QSAR study revealed that steric and hydrophobic descriptors were negatively contributed to neuraminidase inhibitory activity. The results of this study could be used as platform for design of better anti-influenza drugs.

Pan-Cancer Analysis and Drug Formulation for GPR139 and GPR142.

GPR (G protein receptor) 139 and 142 are novel foundling GPCRs (G protein-coupled receptors) in the class “A” of the GPCRs family and are suitable targets for various biological conditions. To engage these targets, validated pharmacophores and 3D QSAR (Quantitative structure-activity relationship) models are widely used because of their direct fingerprinting capability of the target and an overall accuracy. The current work initially analyzes GPR139 and GPR142 for its genomic alteration via tumor samples. Next to that, the pharmacophore is developed to scan the 3D database for such compounds that can lead to potential agonists. As a result, several compounds have been considered, showing satisfactory performance and a strong association with the target. Additionally, it is gripping to know that the obtained compounds were observed to be responsible for triggering pan-cancer. This suggests the possible role of novel GPR139 and GPR142 as the substances for initiating a physiological response to handle the condition incurred as a result of cancer.