QSAR Research Articles

Towards Designer Photocatalysts: Structure-Property Relationships in 2,6-Diaryl-pyryliums.

Fully organic photocatalyst systems are highly attractive, not merely because they are transition-metal free, but more importantly due to their unique and often potent reactivity. A detailed understanding of the various redox states, both ground and excited state, and specifically what structural parameters control them is therefore crucial for harnessing the full potential of these systems in organic synthesis. However, unlike their organometallic counterparts, detailed structure-property relationships for organic photocatalysts are largely absent from the literature. In this study, we demonstrate linear free-energy relationships across a range of key photophysical and electrochemical properties of 2,6-diarylpyryliums. Electronic absorption and emission maxima can be carefully tuned over the ranges of 83 nm and 102 nm respectively. Intramolecular charge transfer (ICT) interactions were revealed in cases of substitution with polarizable heavy-atoms. A strong linear dependence of ground state reduction potentials on substituent electronics was observed. Notably, the excited state reduction potential, E*red, could be controlled over a range of nearly 1000 mV. Systematic errors in computational modeling of ground and excited state redox potentials were identified and corrected. We believe the quantitative structure-property relationships identified here provide foundational tools for rational and predictive organic photocatalyst design.

Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy.

It is imperative to comprehend the mechanisms that underlie drug toxicity in order to enhance the efficacy and safety of novel therapeutic agents. The capacity to identify molecular pathways that contribute to drug-induced toxicity has been significantly enhanced by recent developments in omics technologies, such as transcriptomics, proteomics, and metabolomics. This has enabled the early identification of potential adverse effects. These insights are further enhanced by computational tools, including quantitative structure-activity relationship (QSAR) analyses and machine learning models, which accurately predict toxicity endpoints. Additionally, technologies such as physiologically based pharmacokinetic (PBPK) modeling and micro-physiological systems (MPS) provide more precise preclinical-to-clinical translation, thereby improving drug safety assessments. This review emphasizes the synergy between sophisticated screening technologies, in silico modeling, and omics data, emphasizing their roles in reducing late-stage drug development failures. Challenges persist in the integration of a variety of data types and the interpretation of intricate biological interactions, despite the progress that has been made. The development of standardized methodologies that further enhance predictive toxicology is contingent upon the ongoing collaboration between researchers, clinicians, and regulatory bodies. This collaboration ensures the development of therapeutic pharmaceuticals that are more effective and safer.

VIRTUAL DESIGN OF NOVEL OF ORALLY BIOAVAILABLE PIPERAZINE INHIBITORS OF ENOYL-ACYL CARRIER PROTEIN REDUCTASE OF MYCOBACTERIUM TUBERCULOSIS WITH FAVORABLE PHARMACOKINETIC PROFILES

Background: During the previous decade, Anti-tuberculosis therapies were confronted with drug-resistant strains. We report here virtual design and evaluation of novel piperazine (PPZ) as inhibitors of InhA-Mt endowed with favorable predicted pharmacokinetic profiles. Method: Three-dimensional (3D) models of InhA-PPZx complexes were prepared by in situ modifications of the crystal structure of InhA-PPZ1 (Protein Data Bank (PDB) entry code: 1P44), the reference compound of a training set of 12 PPZs with known experimental inhibitory potencies (IC50exp). First, in the search for active conformation of the PPZ 1-12, we built a gas phase quantitative structure-activity relationships (QSAR) model, linearly correlating the computed enthalpy of the InhA-PPZ complex formation and the pIC50exp. Finally, the VCL filtered by Lipinski’s rule-of-five was screened by the PH4 model and the potencies of the new PPZ analogs obtained were evaluated with the initial QSAR and their pharmacokinetic profile was evaluated. Results: The VCL of 310,500 PPZs s was filtered down to 19,044 analogs by the Lipinski’s rule. The five-point PH4 screening retained 50 new potent PPZs with predicted inhibitory potencies IC50pre up to 100-times better than that of PPZ1 (IC50exp=160 nM). Predicted pharmacokinetic profile of the new analogs showed enhanced cell membrane permeability, side effect and high human oral absorption compared to current anti-tuberculosis candidates Conclusions: Combining molecular modeling and PH4 in virtual screening of the VL resulted in the proposed novel potent antituberculotic agent candidates with favorable pharmacokinetic profiles. Peer Review History: Received 21 July 2024; Reviewed 13 September 2024; Accepted 20 October; Available online 15 November 2024 Academic Editor: Dr. Asia Selman Abdullah, Pharmacy institute, University of Basrah, Iraq, asia_abdullah65@yahoo.com Average Peer review marks at initial stage: 6.0/10 Average Peer review marks at publication stage: 7.5/10 Reviewers: Antonio José de Jesus Evangelista, Federal University of Ceará, UFC, Brazil, tony_biomed@hotmail.com Asmaa Ahmed Mohamed Ahmed Khalifa, Pharos University, Alexandria, Egypt, asmaa.khalifa@pua.edu.eg

QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool

Building reliable and robust quantitative structure–property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred’s modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a “plug-and-play” manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred’s functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred.Scientific ContributionQSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models.

Quantitative Structure-Activity Relationship Models to Predict Cardiac Adverse Effects.

Drug-induced cardiotoxicity represents one of the most common causes of attrition of drug candidates in preclinical and clinical development. For this reason, the evaluation of cardiac toxicity is essential during drug development and regulatory review. In the present study, drug-induced postmarket adverse event combinations from the FDA Adverse Event Reporting System were extracted for 2002 drugs using 243 cardiac toxicity-related preferred terms (PTs). These PTs were combined into 12 groups based on their clinical relevance to serve as training sets. The optimal classification scheme was determined using a combination of data sources that included drug labeling information, published literature, clinical study data, and postmarket surveillance data. Two commercial QSAR platforms were used to construct 12 models, including general cardiac toxicity, cardiac ischemia, heart failure, cardiac valve disease, myocardial disease, pericardial disease, structural heart disease, cardiac arrhythmia, Torsades de Pointes, long QT syndrome, atrial fibrillation and ventricular arrhythmia, and cardiac arrest. The cross-validated performance for the new models reached a sensitivity of up to 80% and negative predictivity of up to 80%. These new models covering a wide range of cardiac endpoints will provide fast, reliable, and comprehensive predictions of potential cardiotoxic compounds in drug discovery and regulatory safety assessment.

The Role of Simulation Science in Public Health at the Agency for Toxic Substances and Disease Registry: An Overview and Analysis of the Last Decade.

Environmental exposures are ubiquitous and play a significant, and sometimes understated, role in public health as they can lead to the development of various chronic and infectious diseases. In an ideal world, there would be sufficient experimental data to determine the health effects of exposure to priority environmental contaminants. However, this is not the case, as emerging chemicals are continuously added to this list, furthering the data gaps. Recently, simulation science has evolved and can provide appropriate solutions using a multitude of computational methods and tools. In its quest to protect communities across the country from environmental health threats, ATSDR employs a variety of simulation science tools such as Physiologically Based Pharmacokinetic (PBPK) modeling, Quantitative Structure-Activity Relationship (QSAR) modeling, and benchmark dose (BMD) modeling, among others. ATSDR's use of such tools has enabled the agency to evaluate exposures in a timely, efficient, and effective manner. ATSDR's work in simulation science has also had a notable impact beyond the agency, as evidenced by external researchers' widespread appraisal and adaptation of the agency's methodology. ATSDR continues to advance simulation science tools and their applications by collaborating with researchers within and outside the agency, including other federal/state agencies, NGOs, the private sector, and academia.

QSAR prediction of toxicity for a new 1,2,4-triazole derivatives with 2-bromo-5-methoxyphenyl fragment

New derivatives of 1,2,4-triazole are promising research targets due to their unique biological properties, including antimicrobial, antifungal, antitumor, and antioxidant activities. The introduction of the 2-bromo-5-methoxyphenyl fragment into the triazole structure potentially enhances these properties. However, the issue of toxicity for such compounds remains a critical factor for their further application. To reduce experimental costs and time, QSAR (Quantitative Structure-Activity Relationship) methods are widely applied, allowing to predict compounds toxicity based on their molecular structure. The aim of this study was to evaluate the toxicity of new derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols, their acids, and esters using the QSAR method to predict parameters of acute toxicity (LD50) and to assess the influence of various radicals on the toxicity of the compounds. Materials and methods. The objects of this study were derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols, synthesized at the Department of Toxicological and Inorganic Chemistry of Zaporizhzhia State Medical and Pharmaceutical University. The nearest neighbor method was used for toxicity evaluation, applying the Toxicity Estimation Software Tool (TEST). The prediction of rats lethal dose (LD50) was based on the structural similarity of the studied compounds with known substances that have experimental toxicity data. Results. The QSAR analysis revealed that structural modifications in the derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols significantly influence their toxicity. Specifically, increasing the size of the radicals, especially through the introduction of aromatic fragments, contributed to the enhanced safety of the compounds, as evidenced by the increase in LD50 values. The highest LD50 values were observed for compounds containing phenyl radicals. Conclusions. The results of this study indicate the feasibility of using QSAR models to predict the toxicity of 1,2,4-triazole derivatives containing a 2-bromo-5-methoxyphenyl fragment. The observed trend of increasing safety with the introduction of larger aromatic radicals can be used for the rational design of new compounds with improved toxicological properties.

Machine learning-driven prediction of EBNA1 inhibitors against Epstein–Barr virus in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC), particularly prevalent in regions such as Malaysia, is a significant health concern often linked to Epstein-Barr virus (EBV) infection. The EBV nuclear antigen 1 (EBNA1), crucial for EBV survival and NPC tumorigenicity, has emerged as a potential therapeutic target for EBV-positive NPC. In this study, we utilized quantitative structure-activity relationship (QSAR) models to predict potential inhibitors of EBNA1. These models were developed based on the molecular fingerprints of known EBNA1 inhibitors, using both classification and regression approaches. Our QSAR classification models demonstrated consistently high precision, recall, F1 score, and accuracy scores across the training set. The top-performing models, constructed using logistic regression algorithms, achieved perfect precision scores of 1.000 in the test set evaluation. These models’ recall, F1 score, and accuracy scores were 0.571, 0.727, and 0.667, respectively. On the other hand, the best-performing model among the regression models was built using the sequential minimal optimization regression algorithm, achieving a correlation coefficient of 0.703. The mean absolute error and root mean square error of this QSAR regression model were 0.173 and 0.217, respectively, whereas the relative absolute error was 0.689. We screened the enamine advanced compound library using this regression model to predict compounds with potential EBNA1 inhibitory effects. This led to the identification of the top 10 compounds with the most promising predicted EBNA1 inhibitory properties.

Computer prediction of toxicity of new S-alkyl derivatives of 1,2,4-triazole

1,2,4-Triazole derivatives are of researchers’ significant interest due to their diverse biological properties, such as antimicrobial, anti-inflammatory, anticancer, and antioxidant activities. The integration of a 2-bromo-4-fluorophenyl fragment into the triazole structure can significantly enhance these activities. However, the evaluation of the toxicity of such compounds remains a critically important aspect for their practical application. To reduce the time and cost of experimental studies, QSAR (Quantitative Structure-Activity Relationship) methods are actively used, allowing the prediction of toxicity based on the molecular structure of compounds. Aim of the study. To assess the toxicity of new S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-R-1,2,4-triazol-3-thiols using the QSAR method, specifically to predict acute toxicity parameters (LD50), and to determine the influence of different (length) radicals on the toxicity of these compounds. Materials and methods. The objects of the virtual study were derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols. They were evaluated at the Department of Toxicological and Inorganic Chemistry of the Zaporizhzhia State Medical and Pharmaceutical University. The toxicity assessment was conducted using the nearest neighbor method via the Toxicity Estimation Software Tool (TEST). The prediction of the lethal dose (LD50) for rats was based on the structural similarity of the studied compounds with known substances, for which experimental toxicity data are available. Results. The conducted QSAR analysis demonstrated that structural changes in S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols significantly affect the predicted toxicity. The primary factor influencing the changes in LD50 values is the variation of radicals at the 5th position of the triazole ring. Conclusions. The results of the study showed, that the toxicity of new S-alkyl derivatives of triazol-3-thiols depends on the type of alkyl substituent. Compounds with propyl to heptyl fragments exhibit increased toxicity, while derivatives with thiol, octyl, nonyl, and decyl residues are characterized by lower toxicity.

Quantitative Structure-Property Relationship (QSPR) Modeling of Central Nervous System (CNS) Drug Activity using Molecular Descriptors

Background: L-type amino acid transporter-1 is a drug that stimulates the func-tions of the brain’s central nervous system. Membrane transporters have evolved, leading to a distinct approach in L-type amino acid transporter-1 drug delivery. One of the trans-porters used for transporting drugs across biological membranes is the L-type amino acid transporter-1. It is widely discussed in the medicinal field. Objectives: Numerous investigations indicate a close connection between the properties of alkanes and the diversity of central nervous system drugs in the brain, specifically log P and molecular weight. One important study that analyzes structural properties is focused on topological descriptors. Recently, topological indices have found application in the de-velopment of quantitative structure-activity relationships. These indices are correlated with the physicochemical properties of BCNS-acting drugs and their biological activity. Methods: The study employs significant methods of calculating topological indices: the edge set partition method and the Djokovi´c-Winkler relation (cut method) are utilized to calculate the values of these descriptors. Results: The results of distance and degree-based topological descriptors have been de-rived. The strong correlation between topological descriptors and the physicochemical properties of BCNS-acting drugs has been studied. Conclusions: This article identifies important topological features for various CNS medi-cations, aiming to support researchers in understanding the properties of molecules and their biological activity. Furthermore, we demonstrate how strongly these behaviors cor-respond to the physicochemical properties of central nervous system drugs.

Analysis of octane isomer properties via topological descriptors of line graphs

Currently, graph-based molecular structure descriptors are frequently used in QSAR (Quantitative Structure-Activity Relationship) and QSPR (Quantitative Structure-Property Relationship) modeling of the properties of chemical compounds. Octane isomers have an important role in the testing of such relationships. In this study, we compute some well-known topological descriptors of the molecular graphs of isomeric octanes and their line graphs, both indices and coindices. Then we analyze the correlation coefficients between the physicochemical properties of the octane isomers and some well-known topological indices and their coindices of the molecular graphs and the line molecular graphs of the octane isomers, to find the particular topological descriptors that have the best predictive values.

Comparative evaluation and QSAR modeling of developmental neurotoxicity of novel brominated flame retardants in zebrafish

The novel brominated flame retardants (NBFRs) have received wide concerns due to their ubiquitous occurrence in the environment and their potential risks to ecosystems and human health. However, the toxicity data of NBFRs are still lacking, especially their toxicity comparison data, and toxicity predictions for untested NBFRs are extremely limited. In this study, eight commonly used NBFRs and decabromodiphenyl ether (BDE209) were selected to compare their toxicity at concentrations between 0.03 and 3.69 μM, by exposing zebrafish embryos until 120 h post-fertilization (hpf) and evaluating 18 toxicity indicators including basic development indicators and a series of behavioral indicators. The toxicity potency of the tested compounds ranked by the total number of significantly affected endpoints were pentabromobenzene (PBB) ≈ 2,4,6-tribromophenol (TBP) > BDE209 ≈ bis(2-ethylhexyl) tetrabromophthalate (TBPH) > pentabromotoluene (PBT) ≈ 2-ethylhexyl-2,3,4,5-tetrabromobenzoate (EHTBB) > 1,2-bis(2,4,6-tribromophenoxy)ethane (BTBPE) > hexabromobenzene (HBB) > decabromodiphenyl ethane (DBDPE). Almost all the tested compounds affected the locomotor behavior of zebrafish larvae, suggesting that the refined behavioral indicators were sensitive endpoints. Furthermore, the quantitative structure-activity relationship (QSAR) model we developed suggested that molecular surface area (MSA) might be the critical factor for determining the developmental neurotoxicity of NBFRs to zebrafish larvae, except for congeners with larger molecules (e.g. DBDPE, BTBPE). These findings would contribute to elucidating the toxicity differences among various NBFRs and provide important references for their toxicity prediction.

Oxygen vacancies-enriched spent lithium-ion battery cathode materials loaded catalytic membrane for effective peracetic acid activation and organic pollutants degradation

Advanced oxidation processes combined with membrane filtration technique offer a promising approach for pollution mitigation and catalyst recovery. Herein, a waste ternary lithium-ion battery cathode material of LiNixCoyMnzO2 (LNCM) loaded polytetrafluoroethylene (PTFE) membrane was synthesized for peracetic acid (PAA) activation (LNCM-PTFE/PAA) and 2,4,6-trichlorophenol (TCP) degradation. Such a novel membrane, with a catalyst loading of 10 mg (0.796 mg/cm2) of LNCM achieved 96.4 % removal of TCP (2 mg/L) within 20 min in neutral pH. The redox cycles of surface metals (such as Co3+/Co2+, Ni3+/Ni2+, and Mn4+/Mn3+/Mn2+) in spent LNCM efficiently enhance charge transfer and mediated PAA activation. And intrinsic oxygen vacancies in LNCM facilitated PAA adsorption and its cleavage. The resulting carbon-centered radicals (R-C•, CH3C(O)OO•) and 1O2 are identified as the primary reactive species that collaboratively participate in TCP degradation. Quantitative structure-activity relationship analysis demonstrated a substantial reduction in product toxicity. The successful practical application of the LNCM-PTFE/PAA membrane was exemplified by treating chlorophenol industrial wastewater. This study presents a new LNCM-PTFE/PAA catalytic membrane for high-efficiency water treatment and a novel perspective for green utilization of waste LNCM.

Highly Cytotoxic Cryptophycin Derivatives with Modification in Unit D for Conjugation.

Cytotoxic payloads for drug conjugates suitable for directed tumor therapy need to be highly potent and require a functional group for conjugation with the homing device (antibody, peptide, or small molecule). Cryptophycins are cyclodepsipeptides that stand out from the realm of natural products due to their extraordinarily high cytotoxicity. However, the installation of a suitable conjugation handle without compromising the toxicity is highly challenging. The unit D, natively 2-hydroxyisocaproic acid (leucic acid), was envisaged as a promising attachment site based on structural information from X-ray analysis. A versatile, scalable and efficient synthetic route towards conjugable cryptophycins with modification in unit D was developed and an array of new cryptophycin analogues was synthesized. Several derivatives, especially those containing lipophilic groups with low steric demand such as alkylated amino groups, exhibit low picomolar cytotoxicity often combined with efficacy against multidrug-resistant tumor cells. The newly established cryptophycin analogues comprise a broad range of relevant functional groups used as conjugation handles, among them amino, hydroxy, carboxy, as well as sulfur-containing derivatives. X-ray crystallographic analysis of a tubulin-bound cryptophycin together with quantitative structure activity relationship manifested rationales for the synthesis of most potent cryptophycin derivatives and further confirmed the suitability of modifications in unit D.

Quantitative structure–activity relationships of chemical bioactivity toward proteins associated with molecular initiating events of organ-specific toxicity

The adverse outcome pathway (AOP) concept has gained attention as a way to explore the mechanism of chemical toxicity. In this study, quantitative structure–activity relationship (QSAR) models were developed to predict compound activity toward protein targets relevant to molecular initiating events (MIE) upstream of organ-specific toxicities, namely liver steatosis, cholestasis, nephrotoxicity, neural tube closure defects, and cognitive functional defects. Utilizing bioactivity data from the ChEMBL 33 database, various machine learning algorithms, chemical features and methods to assess prediction reliability were compared and applied to develop robust models to predict compound activity. The results demonstrate high predictive performance across multiple targets, with balanced accuracy exceeding 0.80 for the majority of models. Furthermore, stability checks confirmed the consistency of predictive performance across multiple training-test splits. The results obtained by using QSAR predictions to identify known markers of adversities highlighted the utility of the models for risk assessment and for prioritizing compounds for further experimental evaluation.Scientific contributionThe work describes the development of QSAR models as tools for screening chemicals with potential systemic toxicity, thus contributing to resource savings and providing indications for further better-targeted testing. This study provides advances in the field of computational modeling of MIEs and information from AOP which is still relatively young and unexplored. The comprehensive modeling procedure is highly generalizable, and offers a robust framework for predicting a wide range of toxicological endpoints.

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.

A Comparative Study: Domination Parameters and Physico-Chemical Properties of Benzenoid Hydrocarbons

In graph theory, a graph is a mathematical structure that consists of a set of vertices and a set of edges connecting those pairs of vertices. Let G = ( V , E ) be a graph, a dominating set for G is a subset D ⊆ V of vertices such that every vertex in V ∖ D has at least one neighbor in D. The domination number of G, denoted by γ ( G ) , is the minimum cardinality of a dominating set in G. Recently, Khan (2023) performed a comparative study of seven important domination parameters with the π -electronic energy of benzenoid hydrocarbons, with a new theoretical approach. Physico-chemical properties are crucial for understanding the physical and chemical behavior of a molecule. In this paper, we perform a comparative study of those seven domination parameters with the physico-chemical properties of benzenoid hydrocarbons. We find the values of those domination parameters for the 22 lower benzenoid hydrocarbons and follow the statistical approach described by Khan (2023). The two physico-chemical properties to be chosen are the normal boiling point T b , a representative for intermolecular and van-der-Waals type interactions, whereas, the standard enthalpy/heat of formation Δ H f o is referred to represent the thermal properties of a molecular graph. This study further enhances that the performance of paired domination number is exceptional among all other parameters and highly correlated with both of the physico-chemical properties. Its correlation coefficient is 0.9981 (respectively, 0.9726) with T b (respectively, Δ H f o ). The outstanding correlation coefficient of paired domination number ensures further usage in quantitative structure-property relationship (QSPR) models.

Quantitative structure properties relationship ([formula omitted]) analysis for physicochemical properties of nonsteroidal anti-inflammatory drugs (NSAIDs) usingVe degree-based reducible topological indices

Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) are a class of medications that are used for different therapeutic uses. They effectively alleviate pain, reduce inflammation, and manage fever. These drugs are available in various forms. NSAIDs are prescribed by healthcare professionals to address a wide range of symptoms, from headaches and dental pain to conditions like arthritis and muscle stiffness. In this work, we use ve-degree-based reducible topological descriptors in quantitative structure-property relationship (QSPR) analysis to estimate the physicochemical properties of NSAIDs. In the first step, we have developed a MAPLE-based code to compute the reducible ve-degree-based topological descriptors of NSAIDs. Then, a linear regression model was used to estimate four physicochemical properties of seventy NSAIDs. It has been observed that two physicochemical properties, namely Molecular Weight and Complexity show a very strong correlation with the reducible ve-degree-based topological descriptors. For both cases, the value of correlation coefficient is greater than 0.9. Finally, quadratic and cubic regression models were constructed, and a comparative analysis with these models is presented. These results may help enhance the understanding of NSAIDs medication structures and aid in predicting their pharmacological activity.

Quantitative read-across structure-property relationship (q-RASPR): a novel approach to estimate the bioaccumulative potential for diverse classes of industrial chemicals in aquatic organisms.

The Bioconcentration Factor (BCF) is used to evaluate the bioaccumulation potential of chemical substances in reference organisms, and it directly correlates with ecotoxicity. Traditional in vivo BCF estimation methods are costly, time-consuming, and involve animal sacrifice. Many in silico technologies are used to avoid the problems associated with in vivo testing. This study aims to develop a quantitative read across structure-property relationship (q-RASPR) model using a structurally diverse dataset consisting of 1303 compounds by combining quantitative structure-property relationship (QSPR) and read-across (RA) algorithms. The model incorporates simple, interpretable, and reproducible 2D molecular descriptors along with RASAR descriptors. The PLS-based q-RASPR model demonstrated robust performance with internal validation metrics (R2 = 0.727 and Q2(LOO) = 0.723) and external validation metrics (Q2F1 = 0.739, Q2F2 = 0.739, and CCC = 0.858). These results indicate that the q-RASPR model is statistically superior to the corresponding QSPR model. Furthermore, screening of 1694 compounds from the Pesticide Properties Database (PPDB) was performed using the PLS-based q-RASPR model for assessing the eco-toxicological bioaccumulative potential of various compounds, ensuring the external predictability of the developed model and confirming the real-world application of the developed model. This model offers a reliable tool for predicting the BCF of new or untested compounds, thereby helping to develop safe and environment-friendly chemicals.

Identification of novel DNA gyrase inhibitor by combined pharmacophore modeling, QSAR analysis, molecular docking, molecular dynamics, ADMET and DFT approaches

DNA gyrase, an ATP-dependent enzyme, plays a critical role in DNA replication, transcription, and recombination in Mycobacterium tuberculosis (MTB). While fluoroquinolones are effective antibacterial agents targeting DNA gyrase, their clinical use is often limited due to side effects and the emergence of bacterial resistance. In this study, we developed a quantitative structure-activity relationship (QSAR) model to predict the anti-tubercular activity of Quinoline-Aminopiperidine derivatives targeting the DNA gyrase enzyme, using a dataset of 48 compounds obtained from the literature. The QSAR model was validated using both internal and external validation metrics. Model 4, the best predictive model, demonstrated a strong fit with an R² of 0.8393, an adjusted R² (R²adj) of 0.8010, and a lack of fit (LOF) parameter of 0.0626. The QSAR results revealed that DNA gyrase inhibition is significantly influenced by factors such as partition coefficient, molecular flexibility, hydrogen bonding potential, and the presence of fluorine atoms. Twelve quinoline-aminopiperidine derivatives were designed, and their anti-tubercular activity was predicted using QSAR model-4. These compounds were further assessed for pharmacokinetic properties, toxicity, and binding affinity to DNA gyrase. Pharmacophore modeling was also performed and validated using MOE software. The final pharmacophore model includes the features of two aromatic hydrophobic features, one hydrogen bond acceptor, and one hydrogen bond donor. The results indicated that designed compounds QA-3 and dataset compounds C-34 exhibit favorable drug-likeness properties. Molecular dynamics simulations confirmed the stable binding of compounds QA-3 and C-34 to the DNA gyrase protein, highlighting their potential as promising anti-tubercular agents. The developed QSAR Model-4 will facilitate the prediction of anti-tubercular activity in Quinoline-Aminopiperidine derivatives.