Molecular Field Analysis Research Articles

Molecular field analysis in half-titanocene complexes: computational study towards data-driven in silico optimization of single-site olefin polymerization catalysts

The control of multiple reaction outcomes in olefin polymerization has been demonstrated through data-driven catalyst optimization based on molecular field analysis, using data collected from DFT-based transition-state calculations.

Synthesis of novel purine derivatives: Antiplatelet aggregation activity evaluation and 3D‐QSAR analysis

AbstractCardiovascular disease caused by platelet aggregation is a serious threat to human health, so antiplatelet aggregation has great significance to treat the disease. Since, purine derivatives are important molecules with antiplatelet aggregation activity, it is very essential to study the relationship between purine structure and antiplatelet aggregation effect, which could help us to find antithrombotic drugs. This article describes the modification of molecular substituents with purine structures as backbone and evaluates their antiplatelet aggregation activity. 3D‐QSAR analysis of a series of novel purine derivatives synthesized was performed based on self‐organized molecular field analysis (SOMFA). Significant correlation coefficients (SOMFA, r2 = 0.821, rcv2 = 0.807, F value = 283.500, SEE = 0.229) were obtained, and the model prediction ability was validated using the test set. The results suggest that rational modification of the substituent groups can provide a basis for the development of more effective drug molecules.

Integrated Computational Analysis on Some Indolo-quinoline Derivatives for the Development of Novel Antiplasmodium Agents: CoMFA, Pharmacophore Mapping, Molecular Docking and ADMET Studies

Background: The development of multi-resistant strains of the Plasmodium parasite has become a global problem. Therefore, designing of new antimalarial agents is an exclusive solution. Objective: To improve the activity and identify potentially efficacious new antimalarial agents, integrated computational perspectives such as pharmacophore mapping, 3D-QSAR and docking study have been applied to a series of indolo-quinoline derivatives. Methods: The pharmacophore mapping generated various hypotheses based on key functional features and the best hypothesis ADRRR_1 revealed that indolo-quinoline scaffold is essential for antimalarial activity. 3D-QSAR model was established based on CoMFA and CoMSIA models by using 30 indolo-quinoline analogues as training set and the rest of 19 as test set. Results: The molecular field analysis (MFA) with PLS (partial least-squares) method was used to develop significant CoMFA (q2=0.756, r2=0.996) and CoMSIA (q2=0.703, r2=0.812) models. The CoMFA and CoMSIA models showed good predictive ability with r2 pred values of 0.9623 and 0.9214 respectively. Docking studies were performed by using pfLDH to identify structural insight into the active site and results signify that the quinoline nitrogen acts as a hydrogen bond acceptor region to facilitate interaction with Glu122. Finally, designed molecules were screened through the ADMET tool to evaluate the pharmacokinetic and drug-likeness parameters. Conclusion: Thus, these studies suggested that established models have good predictability and would help in the optimization of newly designed molecules that may produce potent antimalarial activity.

Molecular Field Analysis Using Computational-Screening Data in Asymmetric N-Heterocyclic Carbene-Copper Catalysis toward Data-Driven In Silico Catalyst Optimization

Abstract A molecular-field-based regression analysis using computational screening data for N-heterocyclic carbene (NHC)-Cu-catalyzed asymmetric carbonyl additions of a silylboronate to aldehydes is reported. A computational screening was performed to collect enantioselectivity data (ΔΔG‡: energy differences between the transition states leading to each enantiomer) via transition-state (TS) calculations using density functional theory (DFT) methods. A molecular field analysis (MFA) was carried out using the obtained calculated ΔΔG‡ values and TS structures (30 samples in total). Important structural information for enantioselectivity extracted by the MFA was visualized on the TS structures, which provided insight into an asymmetric induction mechanism. Based on the obtained information, chiral NHC ligands were designed, which showed improved enantioselectivity in these carbonyl additions (designed ligands: up to 96% ee, initial training samples: up to 73% ee).

Molecular field analysis for data-driven molecular design in asymmetric catalysis.

This review highlights the recent advances (2019-present) in the use of MFA (molecular field analysis) for data-driven catalyst design, enabling to improve selectivities/reaction outcomes in asymmetric catalysis. Successful examples of MFA-based molecular design and how to design molecules by MFA are described, including how to generate and evaluate MFA-based regression models, and future challenges in MFA-based molecular design in molecular catalysis.

A Combined Approach of Pharmacophore Modeling, QSAR Study, Molecular Docking and In silico ADME/Tox Prediction of 4-Arylthio & 4-Aryloxy-3- Iodopyridine-2(1H)-one Analogs to Identify Potential Reverse Transcriptase Inhibitor: Anti-HIV Agents.

Reverse transcriptase is an important therapeutic target to treat AIDS caused by the Human Immunodeficiency Virus (HIV). Despite many effective anti-HIV drugs, reverse transcriptase (RT) inhibitors remain the cornerstone of the drug regimen to treat AIDS. In the present work, we have expedited the use of different computational modules and presented an easy, costeffective, and high throughput screening method to identify potential reverse transcriptase inhibitors. A congeneric series of 4-Arylthio & 4-Aryloxy-3- Iodopyridine-2(1H)-one analogs having anti-HIV activity were subjected to structure-based 2D, 3D QSAR, Pharmacophore Modeling, and Molecular Docking to elucidate the structural properties required for the design of potent HIV-RT inhibitors. Prediction of preliminary Pharmacokinetic and the Drug Likeliness profile was performed for these compounds by in silico ADME study. The 2D and 3D- QSAR models were developed by correlating two and three-dimensional descriptors with activity (pIC50) by sphere exclusion method and k-nearest neighbor molecular field analysis approach, respectively. The significant 2D- QSAR model developed by Partial Least Square is associated with the Sphere Exclusion method (PLS-SE), having r2 and q2 values 0.9509 and 0.8038, respectively. The 3D-QSAR model by Step Wise variable selection method (SW-kNN MFA) is more significant, which has a cross-validated squared correlation coefficient q2= 0.8509 and a non-crossvalidated correlation coefficient pred_r2= 0.8102. The pharmacophore hypothesis was developed, which comprised 5 features includes 3 aliphatic regions (Ala), 1 H-bond donor (HDr) and 1 H-bond acceptor (HAc). Docking studies of the selected inhibitors with the active site of reverse transcriptase enzyme showed hydrogen bond and π - π interaction with LYS-101, LYS-103, TYR- 181, TYR-188 and TRP-229 residues present at the active site. All the candidates with good bioavailability and ADMET drug likeliness properties. The results of the present work provide more useful information and important structural insights for the discovery, design of novel and potent reverse transcriptase inhibitors with high therapeutic windows in the future.

Data-driven catalyst optimization for stereodivergent asymmetric synthesis by iridium/boron hybrid catalysis

Asymmetric catalysis enabling divergent control of multiple stereocenters remains challenging in synthetic organic chemistry. Although machine-learning-based optimization of molecular catalysis is a rapidly growing research field, the use of such approaches for catalyst design to achieve stereodivergent asymmetric synthesis producing multiple reaction outcomes, such as constitutional selectivity, diastereoselectivity, and enantioselectivity, is unprecedented. Here, we report the straightforward identification of asymmetric two-component iridium/boron hybrid catalyst systems for α- C -allylation of carboxylic acids. Structural optimization of the chiral ligands for iridium catalysts was driven by molecular-field-based regression analysis with a dataset containing overall 32 molecular structures. The catalyst systems enabled selective access to all the possible isomers of chiral carboxylic acids bearing contiguous stereocenters. This chemoselective and stereodivergent, asymmetric catalysis is applicable to late-stage structural modifications of drugs and their derivatives. Practical regression-based, data-driven, chiral catalyst design and optimization method 3-Dimensional, quantitative structure-selectivity relationships and molecular field analysis Control/improvement of four reaction outcomes with regression analysis Stereodivergent asymmetric synthesis of α-allyl carboxylic acids Chen et al. report that data-driven catalyst design facilitates stereodivergent asymmetric synthesis, which remains challenging in organic synthesis. This research indicates that digital transformations (DXs) of organic synthesis (e.g., the use of machine-leaning and materials-informatics techniques) enable analysis and control of complicated reactions, opening new avenues in molecular catalysis.

Molecular Field Analysis Using Intermediates in Enantio-Determining Steps Can Extract Information for Data-Driven Molecular Design in Asymmetric Catalysis

Abstract We report that a regression technique, molecular field analysis (MFA), is useful to design molecules in asymmetric catalysis. We found that MFA using intermediate structures in an enantio-determining step enabled extraction and visualization of important 3D-structural information for improving enantioselectivity. Based on the visualized structural information, we designed a catalyst and substrate. DFT calculations indicated that enantioselectivities of the reactions using the compounds would improve significantly. We confirmed experimentally that the designed substrate exhibited higher enantioselectivity than those in the training set.

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes.

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

Preparation of low remanence temperature coefficient (RT – 300 °C) SmDy (Co, Fe, Cu, Zr)z magnets and molecular field analysis

Dy doped SmCo magnets with the nominal composition of Sm1−xDyx (Co, Fe, Cu, Zr)z were prepared by powder metallurgy method. The influence of Dy content on the remanence temperature coefficient and microstructure of magnets was investigated. XRD and TEM analysis showed that the magnets with different Dy content consisted of 2:17R cell phase and 1:5H cell boundary. Magnetic performance characterization showed that the remanence of the magnets decreased but the remanence temperature coefficient reduced as well with the increase of Dy content. When x was increased to 0.5, a comprehensive magnetic performance was obtained of which the remanence temperature coefficient (α) was 0.015%/°C in the temperature range of 20–300 °C and the maximum energy product descended tardily from 10.8 MGOe to 9.3 MGOe as the temperature rose from 20 °C to 300 °C. The two sub-lattice molecular field theory was used to calculate the temperature dependence of magnets remanence and to explain the temperature compensation effect of Dy content, the calculated results was in well correspondence with experimental results. Such method could also be used for the exploration of new thermal stability of magnets.

DEVELOPMENT OF A CREDIBLE QSAR AND K NEAREST NEIGHBOR MODELS FOR IMIDAZOLYL DERIVATIVES

Quantitative Structure-Activity Relationship studies were performed for correlating the imidazolyl derivatives and their activity using molecular modeling studies. The statistically significant best 2D model was having correlation coefficient = 0.8221 and cross-validated squared correlation coefficient = 0.7534 with external predictive ability of pred_r2 = 0.7716. Molecular field analysis was used to construct the best 3D-QSAR model showing good correlative and predictive capabilities in terms of q2 =0.6781 and pred_r2 =0.7299. The molecular field analysis (MFA) contour plots provided further understanding of the relationship between structural features of Imidazolyl derivatives and their activities which should be applicable to design newer potential antihypertensive agents.

PREDICTING POTENT ANTIHYPERTENSIVE COMPOUNDS OF PYRIDINE DERIVATIVES BY MOLECULAR MODELING APPROACH

A quantitative structure activity relationship study was performed on a series of pyridine substituted compounds as angiotensin II receptors for establishing quantitative relationship between biological activity and their physicochemical structural properties. Partial least square (PLS) methodology coupled with stepwise forward backward method were applied to derive QSAR models, which were further validated for statistical significance and predictive ability by internal and external validation. A 2D QSAR model was generated with correlation coefficient (r2) of 0.8874, significant cross-validated correlation coefficient (q2) of 0.7498, r2 for external test set (pred_r2) 0.7869. A Zscore_r2 value of 3.9853, Zscore_q2 value of 2.8642 and Zscore_ pred_r2 value of 1.0583 statistically validates the significance of the obtained QSAR model. Molecular field analysis (MFA) was used to construct the best 3D-QSAR model showing good correlative and predictive capabilities in terms of q2= 0.7264 and pred_r2= 0.6988. The quantitative structure activity relationship study provides important structural insights in the designing of potent antihypertensive molecules.

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP MODELING FOR PREDICTION OF PYRIDINE SUBSTITUTED ANALOGUES WITH K-NEAREST NEIGHBOR STUDIES

A quantitative structure activity relationship study was performed on a series of imidazo[4,5-b]pyridine substituted compounds as angiotensin II receptor antagonists for establishing quantitative relationship between activity and their physicochemical properties. The best quantitative structure activity relationship model was generated with correlation coefficient of 0.8318, cross validated correlation coefficient of 0.7142 and r2 for external test set 0.7965. Molecular field analysis was used to construct the best 3D-QSAR model using PLS method, showing good correlative and predictive capabilities in terms of q2 = 0.7264 and pred_r2 = 0.8164. These results will be useful for the design of new antihypertensive molecules.

Quantitative Structure Activity Relationship and Molecular Docking of Pim-1 Kinase Inhibitors

QSAR has established itself as an important means of virtual screening. Computational analysis of numerous drug molecules aided the facility to meaningfully screen specific ones for further use and experimentation. In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) associated simulations were done and stochastic models were developed considering 55 molecules of hydroxyflavanone, thaizolidene2, 4-dione, pyridone derivatives against proviral insertion site of moloney murine leukemia virus. As part of the study, molecular field analysis (MFA) was done along with receptor surface analysis (RSA). Moreover, the generalization ability of the developed model was rigorously validated using 12 test set molecules. Molecular docking exercises were performed to analyze the protein structure using the crystal structure of PIM-1 kinase. Good correlation between predicted fitness scores and the observed activities was obtained. The presence of the hydroxyl groups in B ring instead of a ring was found crucial and playing important role for the compound activity. B ring substitution showed enhanced activity in this scenario. Further, the steric descriptor coefficient present near the ortho-position of N4 amine group indicates that any steric group increases the activity. GOLD was used for molecular docking of 55 molecules considered to target PIM-1 ATP binding site. GOLD based predicted fitness scores showed excellent correlation with the observations found in the available experimental outcomes.

The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q2cv=0.610, Nopt=7, SEPcv=0.505, r2pr=0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development.

Molecular modeling studies of some thiazolidine-2,4-dione derivatives as 15-PGDH inhibitors

The quantitative structure–activity relationship (QSAR) studies were performed on a series of 34 thiazolidine-2,4-dione derivatives to find the structural features necessary for the inhibition of 15-PGDH. The multiple linear regression (MLR) and partial least square (PLS) regression coupled with stepwise (SW) feature selection methods were performed to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The statistically significant 2D-QSAR model having r 2 = 0.71, 0.71 and q 2 = 0.62, 0.62 with pred_r 2 = 0.93, 0.88 was developed by SW-PLS and SW-MLR consecutively. Molecular field analysis was used to construct the best 3D-QSAR model using k-nearest neighbor (kNN) method, which showed good correlative and predictive capabilities in terms of q 2 = 0.78 and pred_r 2 = 0.74. A docking study revealed the binding orientations of these inhibitors at active site amino acid residues (ASN-91, GLY-93, GLY-12, TRY-151) of 15-PGDH enzyme (PDB ID: 2GDZ).

Identification of 3-Nitro-2,4,6-trihydroxybenzamide Derivatives as Photosynthetic Electron Transport Inhibitors by QSAR and Pharmacophore Studies.

In the present investigation, quantitative structure-activity relationship (QSAR) analysis was performed on a data set consisting of structurally diverse compounds in order to investigate the role of their structural features on their photosynthetic electron transport Inhibitors. The best 2D-QSAR model was selected, having correlation coefficient r (2)=0.8544 and cross-validated squared correlation coefficient q (2)=0.7139 with external predictive ability of pred_r (2)=0.7753. The results obtained in this study indicate that the presence of hydroxy and nitro groups, expressed by the SsOHcount and SddsN (nitro) count, is the most relevant molecular property determining efficiency of photosynthetic inhibitory. Molecular field analysis was used to construct the best k-nearest neighbor (kNN-MFA)-based 3D-QSAR model using SA-PLS method, showing good correlative and predictive capabilities in terms of [Formula: see text] and [Formula: see text]. The pharmacophore model includes three features viz. hydrogen bond donor, hydrogen bond acceptor, and one aromatic feature. The developed model was found to be predictive and can be used to design potent photosynthetic electron transport activities prior to their synthesis for further lead modification.

Molecular Modeling Studies of Thiophenyl C-Aryl Glucoside SGLT2 Inhibitors as Potential Antidiabetic Agents.

A QSAR study on thiophenyl derivatives as SGLT2 inhibitors as potential antidiabetic agents was performed with thirty-three compounds. Comparison of the obtained results indicated the superiority of the genetic algorithm over the simulated annealing and stepwise forward-backward variable method for feature selection. The best 2D QSAR model showed satisfactory statistical parameters for the data set (r 2 = 0.8499, q 2 = 0.8267, and pred_r 2 = 0.7729) with four descriptors describing the nature of substituent groups and the environment of the substitution site. Evaluation of the model implied that electron-rich substitution position improves the inhibitory activity. The good predictive 3D-QSAR models by k-nearest neighbor (kNN) method for molecular field analysis (MFA) have cross-validated coefficient q 2 value of 0.7663 and predicted r 2 value of 0.7386. The results have showed that thiophenyl groups are necessary for activity and halogen, bulky, and less bulky groups in thiophenyl nucleus enhanced the biological activity. These studies are promising for the development of novel SGLT2 inhibitor, which may have potent antidiabetic activity.

Identification of 3-nitro-2, 4, 6-trihydroxybenzamide derivatives as photosynthetic electron transport inhibitors by QSAR and pharmacophore studies.

In the present investigation, QSAR analysis was performed on a data set consist of structurally diverse compounds in order to investigate the role of its structural features on their Photosynthetic Electron Transport Inhibitors. The herbicidal activity co-related with certain topological and hydrophobicity based descriptors, 3D descriptors dependent steric, electrostatic and hydrophobic. The best 2D QSAR model was selected, having correlation coefficient r2 = 0.8544 and cross validated squared correlation coefficient q2 = 0.7139 with external predictive ability of pred_r2 = 0.7753 was developed. The results obtained in this study indicate that hydroxy and nitro groups, as expressed by the SsOHcount, SddsN (nitro) count, is the most relevant molecular property determining efficiency of photosynthetic inhibitory. Molecular field analysis was used to construct the best k-nearest neighbor (kNN-MFA)-based 3DQSAR model using SA-PLS method, showing good correlative and predictive capabilities in terms of q2 = 0.7694 and pred_r2 = 0.7381. The influences of steric, electrostatic and hydrophobic field effects generated by the contribution plots are discussed. The pharmacophore model includes three features viz. hydrogen bond donor, hydrogen bond acceptor, and one aromatic feature was developed. The developed model was found to be predictive and can be used to design potent Photosynthetic Electron Transport activities prior to their synthesis for further lead modification. The results obtained suggest that the 3-nitro-2, 4, 6-trihydroxybenzamide analogues represent promising candidates for the development of new active principles targeting photosynthesis to be used as herbicides.

3D QSAR modeling of 4-nerolidylcatechol derivatives and virtual screening for identification of potent <i>plasmodium</i> inhibitor

The present study was aim to develop a three dimensional quantitative structure-activity relationships (3D QSAR) model based on the structure of 4-nerolidylcatechol (IC50=0.67 µM), a novel plant derived Plasmodium inhibitor and its derivatives for identification of efficient antimalarial lead. A statisti-cally validated Partial Least-Squares (PLS) based Molecular Field Analysis (MFA) model was built up using the training set of eight 4-nerolidylcatechol derivatives and their diverse conformers. A statistically reliable model with good predictive power (cross-validated correlation coefficient q2=0.769) was obtained. Hence, the generated model was used to screen a library of 30,000 compounds of chembridge database (http://www.chembridge.com). Results of drug likeness prediction and ADMET study has suggested six compounds as potential antimalarial/plasmodial lead.