Hybrid 2D/3D-quantitative structure-activity relationship studies on the bioactivities and molecular mechanism of antibacterial peptides.

Antimicrobial peptide (AMP) is the polypeptide, which protects the organism avoiding attack from pathogenic bacteria. Studies have shown that there were some antimicrobial peptides with molecular action mechanism involved in crossing the cell membrane without inducing severe membrane collapse, then interacting with cytoplasmic target-nucleic acid, and exerting antibacterial activity by interfacing the transmission of genetic information of pathogenic microorganisms. However, the relationship between the antibacterial activities and peptide structures was still unclear. Therefore, in the present work, a series of AMPs with a sequence of 20 amino acids was extracted from DBAASP database, then, quantitative structure-activity relationship (QSAR) methods were conducted on these peptides. In addition, novel antimicrobial peptides with  stronger antimicrobial activities were designed according to the information originated from the constructed models. Hence, the outcome of this study would lay a solid foundation for the in-silico design and exploration of novel antibacterial peptides with improved activity activities.

3D-QSAR, Scaffold Hopping, Virtual Screening, and Molecular Dynamics Simulations of Pyridin-2-one as mIDH1 Inhibitors.

Isocitrate dehydrogenase 1 (IDH1) is a necessary enzyme for cellular respiration in the tricarboxylic acid cycle. Mutant isocitrate dehydrogenase 1 (mIDH1) has been detected overexpressed in a variety of cancers. mIDH1 inhibitor ivosidenib (AG-120) was only approved by the Food and Drug Administration (FDA) for marketing, nevertheless, a range of resistance has been frequently reported. In this study, several mIDH1 inhibitors with the common backbone pyridin-2-one were explored using the three-dimensional structure-activity relationship (3D-QSAR), scaffold hopping, absorption, distribution, metabolism, excretion (ADME) prediction, and molecular dynamics (MD) simulations. Comparative molecular field analysis (CoMFA, R2 = 0.980, Q2 = 0.765) and comparative molecular similarity index analysis (CoMSIA, R2 = 0.997, Q2 = 0.770) were used to build 3D-QSAR models, which yielded notably decent predictive ability. A series of novel structures was designed through scaffold hopping. The predicted pIC50 values of C3, C6, and C9 were higher in the model of 3D-QSAR. Additionally, MD simulations culminated in the identification of potent mIDH1 inhibitors, exhibiting strong binding interactions, while the analyzed parameters were free energy landscape (FEL), radius of gyration (Rg), solvent accessible surface area (SASA), and polar surface area (PSA). Binding free energy demonstrated that C2 exhibited the highest binding free energy with IDH1, which was -93.25 ± 5.20 kcal/mol. This research offers theoretical guidance for the rational design of novel mIDH1 inhibitors.

Ligand-Based Design of Novel Quinoline Derivatives as Potential Anticancer Agents: An In-Silico Virtual Screening Approach.

In this study, using the Comparative Molecular Field Analysis (CoMFA) approach, the structure-activity relationship of 33 small quinoline-based compounds with biological anti-gastric cancer activity in vitro was analyzed in 3D space. Once the 3D geometric and energy structure of the target chemical library has been optimized and their steric and electrostatic molecular field descriptions computed, the ideal 3D-QSAR model is generated and matched using the Partial Least Squares regression (PLS) algorithm. The accuracy, statistical precision, and predictive power of the developed 3D-QSAR model were confirmed by a range of internal and external validations, which were interpreted by robust correlation coefficients (RTrain2=0.931; Qcv2=0.625; RTest2=0.875). After carefully analyzing the contour maps produced by the trained 3D-QSAR model, it was discovered that certain structural characteristics are beneficial for enhancing the anti-gastric cancer properties of Quinoline derivatives. Based on this information, a total of five new quinoline compounds were developed, with their biological activity improved and their drug-like bioavailability measured using POM calculations. To further explore the potential of these compounds, molecular docking and molecular dynamics simulations were performed in an aqueous environment for 100 nanoseconds, specifically targeting serine/threonine protein kinase. Overall, the new findings of this study can serve as a starting point for further experiments with a view to the identification and design of a potential next-generation drug for target therapy against cancer.

Cloud 3D-QSAR: a web tool for the development of quantitative structure-activity relationship models in drug discovery.

Effective drug discovery contributes to the treatment of numerous diseases but is limited by high costs and long cycles. The Quantitative Structure-Activity Relationship (QSAR) method was introduced to evaluate the activity of a large number of compounds virtually, reducing the time and labor costs required for chemical synthesis and experimental determination. Hence, this method increases the efficiency of drug discovery. To meet the needs of researchers to utilize this technology, numerous QSAR-related web servers, such as Web-4D-QSAR and DPubChem, have been developed in recent years. However, none of the servers mentioned above can perform a complete QSAR modeling and supply activity prediction functions. We introduce Cloud 3D-QSAR by integrating the functions of molecular structure generation, alignment, molecular interaction field (MIF) computing and results analysis to provide a one-stop solution. We rigidly validated this server, and the activity prediction correlation was R2 = 0.934 in 834 test molecules. The sensitivity, specificity and accuracy were 86.9%, 94.5% and 91.5%, respectively, with AUC = 0.981, AUCPR = 0.971. The Cloud 3D-QSAR server may facilitate the development of good QSAR models in drug discovery. Our server is free and now available at http://chemyang.ccnu.edu.cn/ccb/server/cloud3dQSAR/ and http://agroda.gzu.edu.cn:9999/ccb/server/cloud3dQSAR/.

QSAR analysis of 3-pyrimidin-4-yl-oxazolidin-2-one derivatives isocitrate dehydrogenase inhibitors using Topomer CoMFA and HQSAR methods.

A series of mIDH1 inhibitors derived from 3-pyrimidine-4-oxazolidin-2-ketone derivatives were studied by QSAR model to explore the key factors that inhibit mIDH1 activity. The generated model was cross-verified and non-cross-verified by Topomer CoMFA and HQSAR methods; the independent test set was verified by PLS method; the Topomer search technology was used for virtual screening and molecular design; and the Surflex-Dock method and ADMET technology were used for molecular docking, pharmacology and toxicity prediction of the designed drug molecules. The Topomer CoMFA and HQSAR cross-validation coefficients q2 are 0.783 and 0.784, respectively, and the non-cross-validation coefficients r2 are 0.978 and 0.934, respectively. Ten new drug molecules have been designed using Topomer search technology. The results of molecular docking and ADMET show that the newly designed drug molecules are effective. The docking situation, pharmacology and toxicity prediction results are good. The model can be used to predict the bioactivity of the same type of new compounds and their derivatives. The prediction results of molecular design, molecular docking and ADMET can provide some ideas for the design and development of novel mIDH1 inhibitor anticancer drugs, and provide certain theoretical basis of the experimental verification of new compounds in the future. Newly designed molecules after docking with corresponding proteins in the PDB library, it can explore the targets of drug molecules acting with large proteins and the related force, which is very helpful for the design of new drugs and the mechanism of drug action.

Computer-Aided and AILDE Approaches to Design Novel 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors.

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is a pivotal enzyme in tocopherol and plastoquinone synthesis and a potential target for novel herbicides. Thirty-five pyridine derivatives were selected to establish a Topomer comparative molecular field analysis (Topomer CoMFA) model to obtain correlation information between HPPD inhibitory activity and the molecular structure. A credible and predictive Topomer CoMFA model was established by "split in two R-groups" cutting methods and fragment combinations (q2 = 0.703, r2 = 0.957, ONC = 6). The established model was used to screen out more active compounds and was optimized through the auto in silico ligand directing evolution (AILDE) platform to obtain potential HPPD inhibitors. Twenty-two new compounds with theoretically good HPPD inhibition were obtained by combining the high-activity contribution substituents in the existing molecules with the R-group search via Topomer search. Molecular docking results revealed that most of the 22 fresh compounds could form stable π-π interactions. The absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction and drug-like properties made 9 compounds potential HPPD inhibitors. Molecular dynamics simulation indicated that Compounds Y12 and Y14 showed good root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values and stability. According to the AILDE online verification, 5 new compounds with potential HPPD inhibition were discovered as HPPD inhibitor candidates. This study provides beneficial insights for subsequent HPPD inhibitor design.

Structure-Activity Relationship Studies Based on 3D-QSAR CoMFA/CoMSIA for Thieno-Pyrimidine Derivatives as Triple Negative Breast Cancer Inhibitors.

Triple-negative breast cancer (TNBC) is defined as a kind of breast cancer that lacks estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptors (HER2). This cancer accounts for 10-15% of all breast cancers and has the features of high invasiveness and metastatic potential. The treatment regimens are still lacking and need to develop novel inhibitors for therapeutic strategies. Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses, based on a series of forty-seven thieno-pyrimidine derivatives, were performed to identify the key structural features for the inhibitory biological activities. The established comparative molecular field analysis (CoMFA) presented a leave-one-out cross-validated correlation coefficient q2 of 0.818 and a determination coefficient r2 of 0.917. In comparative molecular similarity indices analysis (CoMSIA), a q2 of 0.801 and an r2 of 0.897 were exhibited. The predictive capability of these models was confirmed by using external validation and was further validated by the progressive scrambling stability test. From these results of validation, the models were determined to be statistically reliable and robust. This study could provide valuable information for further optimization and design of novel inhibitors against metastatic breast cancer.

Synthesis, molecular docking and extensive structure activity relationship of substituted DHP derivatives: a new class of herbicides.

In the present study, twenty-two derivatives of dihydropyridine (DHP) have been synthesized using the Boric acid catalyst in solventless conditions. The synthesis was confirmed by FTIR analysis, 1HNMR, and 13CNMR analysis. The quantitative structure-activity relationship for all the synthesized derivatives was performed using an artificial neural network with correlation coefficient (R2) 0.8611, mean standard error 0.19, and Comparative molecular field analysis (CoMFA) with correlation coefficient (R2) 0.713, mean standard error 0.27. The molecular docking activity of synthesized compounds was tested using "AUTODOCK VINA" against "Acetohydroxyacid synthase protein receptors (PDB code 1YHZ)" acquired from the "RCSB Protein Data Bank". Docking experiments demonstrated favorable interaction among synthesized DHP derivatives and protein receptors with significant binding energy values. These synthesized derivatives have been screened for their pre-emergence herbicidal bioassay against weed species Echinochola crus galli, and the IC50 value were calculated and activity was compared with Butachlor, significant activity was exhibited by all the derivatives. All the synthesized compounds were also screened for their post emergence herbicidal activity against Echinochola crus galli, and the activity of DHPs were compared with penoxulum. All the synthesized compounds show good to moderate activity. Thus, it is concluded that substituted DHP derivatives may be developed as potential herbicides.

Synthesis, fungicidal activity, and 3D-QSAR of tetrazole derivatives containing phenyloxadiazole moieties.

In an effort to discover new agents with good fungicidal activities against CDM (cucumber downy mildew), a series of tetrazole derivatives containing phenyloxadiazole moieties were designed and synthesized. The EC50 values for fungicidal activities against CDM were determined. Bioassay results indicated that most synthesized compounds exhibited potential in vivo fungicidal activity against CDM. A CoMFA (comparative molecular field analysis) model based on the bioactivity was developed to identify some primary structural quality for the efficiency. The values of q2 and r2 for the established model were 0.791 and 0.982 respectively, which reliability and predict abilities were verified. Three analogues (q3, q4, q5) were designed and synthesized based on the model. All these compounds exhibited significant fungicidal activity on CDM with the EC50 of 1.43, 1.52, 1.77 mg·L-1. This work could provide a useful instruction for the further structure optimization.

Anti-chronic myeloid leukemia activity and quantitative structure-activity relationship of novel thiazole aminobenzamide derivatives.

The anti-chronic myeloid leukemia activity of thiazole aminobenzamide derivatives in vitro was tested by a methanethiosulfonate (MTS)-based viability assay method, and the result showed that some compounds exhibited good inhibitory activities against human chronic myeloid leukemia cell line K562, imatinib-resistant strain K562/R and T135I mutant cell line BaF3-ABL-BCR-T315I. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods were used to analyze the relationship between the structure of thiazole aminobenzamide derivatives and the inhibition of K562/R cell activity. In CoMFA, Q2 was 0.899 and R2 was 0.963; in CoMSIA, Q2 and R2 were 0.840 and 0.903, respectively. These data indicated that the selected test set showed suitable external predictive ability. Combined with the contour map results, we further analyzed the three-dimensional quantitative structure (3D-QSAR) model. The results demonstrated that in the backbone of the thiazole aminobenzamide derivative, the substitution of a small group at R1 position, or the introduction of a hydrophilic group at R2 position, or the introduction of a large-volume amino acid at R3 position may be beneficial to improve the anti-CML activity of the compound.

Docking-Based 3D-QSAR Studies for 1,3,4-oxadiazol-2-one Derivatives as FAAH Inhibitors.

This work aimed to construct 3D-QSAR CoMFA and CoMSIA models for a series of 31 FAAH inhibitors, containing the 1,3,4-oxadiazol-2-one moiety. The obtained models were characterized by good statistical parameters: CoMFA Q2 = 0.61, R2 = 0.98; CoMSIA Q2 = 0.64, R2 = 0.93. The CoMFA model field contributions were 54.1% and 45.9% for steric and electrostatic fields, respectively. In the CoMSIA model, electrostatic, steric, hydrogen bond donor, and hydrogen acceptor properties were equal to 34.6%, 23.9%, 23.4%, and 18.0%, respectively. These models were validated by applying the leave-one-out technique, the seven-element test set (CoMFA r2test-set = 0.91; CoMSIA r2test-set = 0.91), a progressive scrambling test, and external validation criteria developed by Golbraikh and Tropsha (CoMFA r20 = 0.98, k = 0.95; CoMSIA r20 = 0.98, k = 0.89). As the statistical significance of the obtained model was confirmed, the results of the CoMFA and CoMSIA field calculation were mapped onto the enzyme binding site. It gave us the opportunity to discuss the structure-activity relationship based on the ligand-enzyme interactions. In particular, examination of the electrostatic properties of the established CoMFA model revealed fields that correspond to the regions where electropositive substituents are not desired, e.g., in the neighborhood of the 1,3,4-oxadiazol-2-one moiety. This highlights the importance of heterocycle, a highly electronegative moiety in this area of each ligand. Examination of hydrogen bond donor and acceptor properties contour maps revealed several spots where the implementation of another hydrogen-bond-donating moiety will positively impact molecules' binding affinity, e.g., in the neighborhood of the 1,3,4-oxadiazol-2-one ring. On the other hand, there is a large isopleth that refers to the favorable H-bond properties close to the terminal phenoxy group of a ligand, which means that, generally speaking, H-bond acceptors are desired in this area.

Generation of Non-Nucleotide CD73 Inhibitors Using a Molecular Docking and 3D-QSAR Approach.

Radiotherapy and chemotherapy are conventional cancer treatments. Around 60% of all patients who are diagnosed with cancer receive radio- or chemotherapy in combination with surgery during their disease. Only a few patients respond to the blockage of immune checkpoints alone, or in combination therapy, because their tumours might not be immunogenic. Under these circumstances, an increasing level of extracellular adenosine via the activation of ecto-5'-nucleotidase (CD73) and consequent adenosine receptor signalling is a typical mechanism that tumours use to evade immune surveillance. CD73 is responsible for the conversion of adenosine monophosphate to adenosine. CD73 is overexpressed in various tumour types. Hence, targetting CD73's signalling is important for the reversal of adenosine-facilitated immune suppression. In this study, we selected a potent series of the non-nucleotide small molecule inhibitors of CD73. Molecular docking studies were performed in order to examine the binding mode of the inhibitors inside the active site of CD73 and 3D-QSAR was used to study the structure-activity relationship. The obtained CoMFA (q2 = 0.844, ONC = 5, r2 = 0.947) and CoMSIA (q2 = 0.804, ONC = 4, r2 = 0.954) models showed reasonable statistical values. The 3D-QSAR contour map analysis revealed useful structural characteristics that were needed to modify non-nucleotide small molecule inhibitors. We used the structural information from the overall docking and 3D-QSAR results to design new, potent CD73 non-nucleotide inhibitors. The newly designed CD73 inhibitors exhibited higher activity (predicted pIC50) than the most active compound of all of the derivatives that were selected for this study. Further experimental studies are needed in order to validate the new CD73 inhibitors.

Molecular Modeling Studies of N-phenylpyrimidine-4-amine Derivatives for Inhibiting FMS-like Tyrosine Kinase-3.

Overexpression and frequent mutations in FMS-like tyrosine kinase-3 (FLT3) are considered risk factors for severe acute myeloid leukemia (AML). Hyperactive FLT3 induces premature activation of multiple intracellular signaling pathways, resulting in cell proliferation and anti-apoptosis. We conducted the computational modeling studies of 40 pyrimidine-4,6-diamine-based compounds by integrating docking, molecular dynamics, and three-dimensional structure-activity relationship (3D-QSAR). Molecular docking showed that K644, C694, F691, E692, N701, D829, and F830 are critical residues for the binding of ligands at the hydrophobic active site. Molecular dynamics (MD), together with Molecular Mechanics Poison-Boltzmann/Generalized Born Surface Area, i.e., MM-PB(GB)SA, and linear interaction energy (LIE) estimation, provided critical information on the stability and binding affinity of the selected docked compounds. The MD study suggested that the mutation in the gatekeeper residue F691 exhibited a lower binding affinity to the ligand. Although, the mutation in D835 in the activation loop did not exhibit any significant change in the binding energy to the most active compound. We developed the ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models. CoMFA (q2 = 0.802, r2 = 0.983, and QF32 = 0.698) and CoMSIA (q2 = 0.725, r2 = 0.965 and QF32 = 0.668) established the structure-activity relationship (SAR) and showed a reasonable external predictive power. The contour maps from the CoMFA and CoMSIA models could explain valuable information about the favorable and unfavorable positions for chemical group substitution, which can increase or decrease the inhibitory activity of the compounds. In addition, we designed 30 novel compounds, and their predicted pIC50 values were assessed with the CoMSIA model, followed by the assessment of their physicochemical properties, bioavailability, and free energy calculation. The overall outcome could provide valuable information for designing and synthesizing more potent FLT3 inhibitors.

Integrated molecular docking, 3D QSAR and molecular dynamics simulation studies on indole derivatives for designing new Pim-1 inhibitors.

Pim-1 is one of the isoforms of pim proteins comprising pim-1, pim-2 and pim-3. It was basically recognized as proviral integration moloney murine leukemia virus which is associated with T-cell lymphomogenesis. Pim-1 is known to play a crucial role in cell cycle progression and acts as downstream target for the JAK/STAT signaling pathway. Recently it has emerged as a hopeful therapeutic target for cancer treatment as deregulation or over expression of pim causes hematologic cancers. In present article molecular docking based three dimensional quantitative structure and activity relationship and molecular dynamics simulation studies have been carried out on indole derivatives reported as pim-1 inhibitors. Initially docking was carried out to obtain the receptor specific orientation of the molecules and later to understand the structural requirements of pim-1 inhibitors robust 3 D QSAR models were built using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) methods. The reliability of the models was established from conventional (r2) and cross validated (q2) values of 0.982, 0.524 for CoMFA and 0.974, 0.586 for CoMSIA respectively. Further the predictive ability of the model was evaluated using a test set of 17 molecules. The docking studies revealed that interaction with Glu 121 is vital for binding of inhibitors to pim-1. Based on the outcome of the results new molecules with improved activity were designed. Furthermore, MD simulations were also performed to examine the stability of interactions and investigate the pivotal role of Glu 121.

Two- and three-dimensional QSAR studies on hURAT1 inhibitors with flexible linkers: topomer CoMFA and HQSAR.

hURAT1 (human urate transporter 1) is a successful target for hyperuricemia. Recently, the modification work on hURAT1 inhibitors showed that the flexible linkers would benefit biological activity. The study aimed to investigate the contribution of the linkers and give modification strategies on this kind of structures based on QSAR models (HQSAR and topomer CoMFA). The most effective HQSAR and topomer CoMFA models were generated by applying the training set containing 63 compounds, with the cross-validated q2 values of 0.869/0.818 and the non-cross-validated correlation coefficients r2 of 0.951/0.978, respectively. The Y-randomization test was applied to ensure the robustness of the models. The external predictive correlation coefficient (rpred2) grounded on the external test set (21 compounds) of two models was 0.910 and 0.907, respectively. In addition, the models were validated by Golbraikh-Tropsha and Roy methods, as well as other statistical metrics. The results showed that both models were reliable. Topomer CoMFA steric/electrostatic contours and HQSAR atomic contribution maps illustrated the structural features which governed their inhibitory potency. The dependable results could provide important insights to guide the designing of more potential hURAT1 inhibitors.

Insight into structural requirements of ACE inhibitory dipeptides: QSAR and molecular docking studies.

The angiotensin I-converting enzyme (ACE) has been found to exhibit inhibitory capability against blood pressure. Recently, several ACE inhibitors with different structures have been reported. In the present work, molecular modeling studies using quantitative structure-activity relationship (QSAR) and molecular docking simulations were carried out. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were firstly used to generate 3D-QSAR models. The results indicate that the best CoMFA model has [Formula: see text] = 0.504, [Formula: see text] = 0.5896, and the best CoMSIA model has [Formula: see text] = 0.525, [Formula: see text] = 0.5666. Furthermore, 2D-QSAR models developed by multiple linear regression/MLR, partial least squares regression/PLSR, and support vector machine regression/SVR methods provide highly significant squared correlation coefficient Rtr2 values of 0.8380, 0.8650, and 0.8230, external validated correlation coefficient Qte2 of 0.8279, 0.8223, and 0.7255, respectively. The statistical results show satisfactory goodness-of-fit, robustness, and perfect external predictive performance. Moreover, molecular docking studies were employed to predict the binding mode between dipeptides and ACE receptor. The combination of QSAR studies and molecular docking indicates the requirement of certain physicochemical parameters for better ACE inhibitors.

The effect of trees on human energy fluxes in a humid subtropical climate region.

Trees are considered to be an effective tool for improving human thermal comfort in hot climates and have been widely used in landscape architecture. However, it is not always clear how trees affect human-environment energy fluxes. In this study, an in-depth analysis of four common tree species was undertaken based on comprehensive field measurements, in terms of how each tree and its characteristics affected the energy fluxes of a person in a humid subtropical climate region. Results showed that the largest effect of trees was on radiation fluxes, with a much smaller effect on the convective and evaporative fluxes. For a person standing in shade, a tree can reduce approximately 25% of the absorbed radiation compared with an open reference point. Moreover, the cooling effect on radiation components was found to be greater in the solar radiation domain than in the terrestrial radiation domain. Solar radiation and ground surface temperature had the largest effect on a human energy budget, which was affected by characteristics of the trees and the thermophysical properties of ground surfaces. The effect from relative humidity and wind speed was quite minimal. For the four common tree species in this study, Ficus microcarpa had the best thermal performance by reducing the most absorbed solar radiation flux. This study shows a detailed empirical research about the thermal effects of trees on a person, providing recommendations for tree species selection in urban design.

Molecular Modeling Study of c-KIT/PDGFRα Dual Inhibitors for the Treatment of Gastrointestinal Stromal Tumors.

Gastrointestinal stromal tumors (GISTs) are the most common Mesenchymal Neoplasm of the gastrointestinal tract. The tumorigenesis of GISTs has been associated with the gain-of-function mutation and abnormal activation of the stem cell factor receptor (c-KIT) and platelet-derived growth factor receptor alpha (PDGFRα) kinases. Hence, inhibitors that target c-KIT and PDGFRα could be a therapeutic option for the treatment of GISTs. The available approved c-KIT/PDGFRα inhibitors possessed low efficacy with off-target effects, which necessitated the development of potent inhibitors. We performed computational studies of 48 pyrazolopyridine derivatives that showed inhibitory activity against c-KIT and PDGFRα to study the structural properties important for inhibition of both the kinases. The derivative of phenylurea, which has high activities for both c-KIT (pIC50 = 8.6) and PDGFRα (pIC50 = 8.1), was used as the representative compound for the dataset. Molecular docking and molecular dynamics simulation (100 ns) of compound 14 was performed. Compound 14 showed the formation of hydrogen bonding with Cys673, Glu640, and Asp810 in c-KIT, and Cys677, Glu644, and Asp836 in PDGFRα. The results also suggested that Thr670/T674 substitution in c-KIT/PDGFRα induced conformational changes at the binding site of the receptors. Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed based on the inhibitors. Contour map analysis showed that electropositive and bulky substituents at the para-position and the meta-position of the benzyl ring of compound 14 was favorable and may increase the inhibitory activity against both c-KIT and PDGFRα. Analysis of the results suggested that having bulky and hydrophobic substituents that extend into the hydrophobic pocket of the binding site increases the activity for both c-KIT and PDGFRα. Based on the contour map analysis, 50 compounds were designed, and the activities were predicted. An evaluation of binding free energy showed that eight of the designed compounds have potential binding affinity with c-KIT/PDGFRα. Absorption, distribution, metabolism, excretion and toxicity (ADMET) and synthetic feasibility tests showed that the designed compounds have reasonable pharmaceutical properties and synthetic feasibility. Further experimental study of the designed compounds is recommended. The structural information from this study could provide useful insight into the future development of c-KIT and PDGFRα inhibitors.

Application of quantitative structure-activity relationship analysis on an antibody and alternariol-like compounds interaction study.

The antigen-antibody interaction determines the sensitivity and specificity of competitive immunoassay for hapten detection. In this paper, the specificity of a monoclonal antibody against alternariol-like compounds was evaluated through indirect competitive ELISA. The results showed that the antibody had cross-reactivity with 33 compounds with the binding affinity (expressed by IC50 ) ranging from 9.4 ng/mL to 12.0 µg/mL. All the 33 compounds contained a common moiety and similar substituents. To understand how this common moiety and substituents affected the recognition ability of the antibody, a three-dimensional quantitative structure-activity relationship (3D-QSAR) between the antibody and the 33 alternariol-like compounds was constructed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The q2 values of the CoMFA and CoMSIA models were 0.785 and 0.782, respectively, and the r2 values were 0.911 and 0.988, respectively, indicating that the models had good predictive ability. The results of 3D-QSAR showed that the most important factor affecting antibody recognition was the hydrogen bond mainly formed by the hydroxyl group of alternariol, followed by the hydrophobic force mainly formed by the methyl group. This study provides a reference for the design of new hapten and the mechanisms for antibody recognition.

www.3d-qsar.com: a web portal that brings 3-D QSAR to all electronic devices-the Py-CoMFA web application as tool to build models from pre-aligned datasets.

Comparative molecular field analysis (CoMFA), introduced in 1988, was the first 3-D QSAR method ever published and sold. Since then thousands of application, articles and citation have proved 3-D QSAR as a valuable method to be used in drug design. Several other 3-D QSAR methods have appeared, but still CoMFA remains the most used and cited. Nevertheless from a survey on the Certara® web site it seems that CoMFA is no more available. Herein is presented a python implementation of the CoMFA (Py-CoMFA). Py-CoMFA is usable through the www.3d-qsar.com web applications suites portal by mean of any electronic device that can run a web browser. As benchmark, 30 different publicly available datasets were used to assess the Py-CoMFA usability and robustness. Comparisons with published results proved Py-COMFA to be in very good agreement with those obtained with the original CoMFA. Although the used datasets were pre-aligned, by means of the other web application available through the portal, 3-D QSAR models can be easily build from scratch. In conclusion, although CoMFA is a well known methodology and given the availability of several publicly available Hansch type QSAR web portals, Py-CoMFA represents a valuable tools for any chemoinformatics and informatics non-skilled user that can also be used as support to teach 3-D QSAR. Importantly, Py-CoMFA is the first and unique tool publicly available to build 3-D QSAR models.