Design, Synthesis, Biological Evaluation, and Molecular Modeling of 2-Difluoromethylbenzimidazole Derivatives as Potential PI3Kα Inhibitors

PI3Kα is one of the potential targets for novel anticancer drugs. In this study, a series of 2-difluoromethylbenzimidazole derivatives were studied based on the combination of molecular modeling techniques 3D-QSAR, molecular docking, and molecular dynamics. The results showed that the best comparative molecular field analysis (CoMFA) model had q2 = 0.797 and r2 = 0.996 and the best comparative molecular similarity indices analysis (CoMSIA) model had q2 = 0.567 and r2 = 0.960. It was indicated that these 3D-QSAR models have good verification and excellent prediction capabilities. The binding mode of the compound 29 and 4YKN was explored using molecular docking and a molecular dynamics simulation. Ultimately, five new PI3Kα inhibitors were designed and screened by these models. Then, two of them (86, 87) were selected to be synthesized and biologically evaluated, with a satisfying result (22.8 nM for 86 and 33.6 nM for 87).

PreSS/MD: Predictor of Skin Sensitization Caused by Chemicals Leaching from Medical Devices

Safety evaluation for medical devices includes the toxicity assessment of chemicals used in device manufacturing, cleansing and/or sterilization that may leach into a patient. According to international standards on biocompatibility assessments (ISO 10993), chemicals that could be released from medical devices should be evaluated for their potential to induce skin sensitization/allergenicity, and one of the commonly used approaches is the guinea pig maximization test (GPMT). However, there is growing trend in regulatory science to move away from costly animal assays to employing New Approach Methodologies including computational methods. Herein, we developed a new computational tool for rapid and accurate prediction of the GPMT outcome that we named PreSS/MD (Predictor of Skin Sensitization for Medical Devices). To enable model development, we (i) collected, curated, and integrated the largest publicly available dataset for GPMT; (ii) succeeded in developing externally predictive (balanced accuracy of 70-74% as evaluated by both 5-fold external cross-validation and testing of novel compounds) Quantitative Structure-Activity Relationships (QSAR) models for GPMT using machine learning algorithms, including Deep Learning; and (iii) developed a publicly accessible web portal integrating PreSS/MD models that enables the prediction of GPMT outcomes for any molecules using. We expect that PreSS/MD will be used by both researchers and regulatory agencies to support safety assessment for medical devices and help replace, reduce or refine the use of animals in toxicity testing. PreSS/MD is freely available at https://pressmd.mml.unc.edu/. Keywords: sensitization, GPMT, QSAR, deep learning,

Atom based 3D QSAR and Fingerprint based 2D QSAR of Novel Molecules as MmpL3 receptor inhibitors for Mycobacterium tuberculosis

Tuberculosis is one of the leading cause of increase in mortality rate in today’s health care scenario. Due to increase frequency of drug resistant TB it is prudent to find new targets and promising targets for anti-tubercular activity. MmpL3 (Mycobacterial Membrane Protein Large 3) is one of the most effective and promiscuous targets for development of new drug for anti-tubercular therapy due to its cross resistance inhibition property. In this study we have presented atom based 3D QSAR and finger print based 2D QSAR models to study different structural and functional groups of Adamantyl urea derivatives and their action in MmpL3 inhibitory activity which will provide us the insight for designing better and far more effective anti TB drugs.

Quantitative Structure Activity Relationship (QSAR) study predicts small molecule binding to RNA structure

The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSAR). Herein, we developed QSAR models that quantitatively predict both thermodynamic and kinetic-based binding parameters of small molecules and the HIV-1 TAR model RNA system. A set of small molecules bearing diverse scaffolds was screened against the HIV-1-TAR construct using surface plasmon resonance, which provided the binding kinetics and affinities. The data was then analyzed using multiple linear regression (MLR) combined with feature selection to afford robust models for binding of diverse RNA-targeted scaffolds. The predictivity of the model was validated on untested small molecules. The QSAR models presented herein represent the first application of validated and predictive 2D-QSAR using multiple scaffolds against an RNA target. We expect the workflow to be generally applicable to other RNA structures, ultimately providing essential insight into the small molecule descriptors that drive selective binding interactions and, consequently, providing a platform that can exponentially increase the efficiency of ligand design and optimization without the need for high-resolution RNA structures.

A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening

Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set (e.g.,QCV2) or the test set (e.g., specificity, selectivity or QF1/F2/F32). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions.

Selection of Promising Novel Fragment Sized S. aureus SrtA Noncovalent Inhibitors Based on QSAR and Docking Modeling Studies

Sortase A (SrtA) of Staphylococcus aureus has been identified as a promising target to a new type of antivirulent drugs, and therefore, the design of lead molecules with a low nanomolar range of activity and suitable drug-like properties is important. In this work, we aimed at identifying new fragment-sized starting points to design new noncovalent S. aureus SrtA inhibitors by making use of the dedicated molecular motif, 5-arylpyrrolidine-2-carboxylate, which has been previously shown to be significant for covalent binding SrtA inhibitors. To this end, an in silico approach combining QSAR and molecular docking studies was used. The known SrtA inhibitors from the ChEMBL database with diverse scaffolds were first employed to derive descriptors and interpret their significance and correlation to activity. Then, the classification and regression QSAR models were built, which were used for rough ranking of the virtual library of the synthetically feasible compounds containing the dedicated motif. Additionally, the virtual library compounds were docked into the “activated” model of SrtA (PDB:2KID). The consensus ranking of the virtual library resulted in the most promising structures, which will be subject to further synthesis and experimental testing in order to establish new fragment-like molecules for further development into antivirulent drugs.

QSAR Studies and Structure Property/Activity Relationships Applied in Pyrazine Derivatives as Antiproliferative Agents Against the BGC823

Electronic structures, the effect of the substitution, structure physicochemical property/activity relationships and drug-likeness applied in pyrazine derivatives, have been studied at ab initio (HF, MP2) and B3LYP/DFT (density functional theory) levels. In the paper, the calculated values, i.e., NBO (natural bond orbitals) charges, bond lengths, dipole moments, electron affinities, heats of formation and quantitative structure-activity relationships (QSAR) properties are presented. For the QSAR studies, we used multiple linear regression (MLR) and artificial neural network (ANN) tatistical modeling. The results show a high correlation between experimental and predicted activity values, indicating the validation and the good quality of the derived QSAR models. In addition, statistical analysis reveals that the ANN technique with (9-4-1) architecture is more significant than the MLR model. The virtual screening based on the molecular similarity method and applicability domain of QSAR allowed the discovery of novel anti-proliferative activity candidates with improved activity.

STRUCTURAL FEATURE STUDY OF QUINOLINES DERIVATIVES WITH THERMODYNAMIC AND OTHER DESCRIPTORS: A QSAR APPROACH

Quantitative structure activity relationship analysis was performed on a series of thirty-three quinoline derivatives to establish the structural features required for angiotensin II receptor activity. QSAR models were derived by stepwise multiple regression analysis employing the method of least squares, using quantum chemical, thermodynamic, electronic and steric descriptors. Model showed best predictability of activity with cross validated value (q2 ) =0.7485, coeffi cient of determination (r2 ) =0.8734 and standard error of estimate (s) = 0.2690. These guidelines may be used to develop new antihypertensive agents based on the quinoline analogues scaffold.