The integration of pharmacophore-based 3D QSAR modeling and virtual screening in safety profiling: A case study to identify antagonistic activities against adenosine receptor, A2A, using 1,897 known drugs

bbSelect - An Open-Source Tool for Performing a 3D Pharmacophore-Driven Diverse Selection of R-Groups

The design of compounds during hit-to-lead often seeks to explore a vector from a core scaffold to form additional interactions with the target protein. A rational approach to this is to probe the region of a protein accessed by a vector with a systematic placement of pharmacophore features in 3D, particularly when bound structures are not available. Herein, we present bbSelect, an open-source tool built to map the placements of pharmacophore features in 3D Euclidean space from a library of R-groups, employing partitioning to drive a diverse and systematic selection to a user-defined size. An evaluation of bbSelect against established methods exemplified the superiority of bbSelect in its ability to perform diverse selections, achieving high levels of pharmacophore feature placement coverage with selection sizes of a fraction of the total set and without the introduction of excess complexity. bbSelect also reports visualizations and rationale to enable users to understand and interrogate results. This provides a tool for the drug discovery community to guide their hit-to-lead activities.

Inhibition of SARS-CoV-2 NSP-15 by Uridine-5'-Monophosphate Analogues Using QSAR Modelling, Molecular Dynamics Simulations, and Free Energy Landscape

SARS-CoV-2 is accountable for severe social and economic disruption around the world causing COVID-19. Non-structural protein-15 (NSP15) possesses a domain that is vital to the viral life cycle and is known as uridylate-specific endoribonuclease (EndoU). This domain binds to the uridine 5'-monophosphate (U5P) so that the protein may carry out its native activity. It is considered a vital drug target to inhibit the growth of the virus. Thus, in this current study, ML-based QSAR and virtual screening of U5P analogues targeting Nsp15 were performed to identify potential molecules against SARS-CoV-2. Screening of 816 unique U5P analogues using ML-based QSAR identified 397 compounds ranked on their predicted bioactivity (pIC50). Further, molecular docking and hydrogen bond interaction analysis resulted in the selection of the top three compounds (53309102, 57398422, and 76314921). Molecular dynamics simulation of the most promising compounds showed that two molecules 53309102 and 57398422 acted as potential binders of Nsp15. The compound was able to inhibit nsp15 activity as it was successfully bound to the active site of the nsp15 protein. This was achieved by the formation of relevant contacts with enzymatically critical amino acid residues (His235, His250, and Lys290). Principal component analysis and free energy landscape studies showed stable complex formation while MM/GBSA calculation showed lower binding energies for 53309102 (ΔGTOTAL = -29.4 kcal/mol) and 57398422 (ΔGTOTAL = -39.4 kcal/mol) compared to the control U5P (ΔGTOTAL = -18.8 kcal/mol). This study aimed to identify analogues of U5P inhibiting the NSP15 function that potentially could be used for treating COVID-19.

Investigating the bispecific lead compounds against methicillin-resistant Staphylococcus aureus SarA and CrtM using machine learning and molecular dynamics approach

Methicillin-resistant Staphylococcus aureus (MRSA) is a notorious pathogen that has emerged as a serious global health concern over the past few decades. Staphylococcal accessory regulator A (SarA) and 4,4'-diapophytoene synthase (CrtM) play a crucial role in biofilm formation and staphyloxanthin biosynthesis. Thus, the present study used a machine learning-based QSAR model to screen 1261 plant-derived natural organic compounds in order to identify a medication candidate with both biofilm and virulence inhibitory potential. Additionally, the in-silico molecular docking analysis has demonstrated significant binding efficacy of the identified hit compound, that is 85137543, with SarA and CrtM when compared to the control compound, hesperidin. Post-MD simulation analysis of the complexes depicted strong binding of 85137543 to both SarA and CrtM. Moreover, 85137543 showed hydrogen bonding with the key residues of both proteins during docking (ALA138 of SarA and ALA134 of CrtM) and post-MD simulation (LYS273 of CrtM and ASN212 of SarA). The RMSD of 85137543 was stable and consistent when bound to both CrtM and SarA with RMSDs of 1.3 and 1 nm, respectively. In addition, principal component analysis and the free energy landscape showed stable complex formation with both proteins. Low binding free energy (ΔGTotal) was observed by 85137543 for SarA (-47.92 kcal/mol) and CrtM (-36.43 kcal/mol), which showed strong binding. Overall, this study identified 85137543 as a potential inhibitor of both SarA and CrtM in MRSA.Communicated by Ramaswamy H. Sarma.

New phenothiazine conjugates as apoptosis inducing agents: Design, synthesis, In-vitro anti-cancer screening and 131I-radiolabeling for in-vivo evaluation

Phenothiazines (PTZs) are a group of compounds characterized by the presence of the 10H-dibenzo-[b,e]-1,4-thiazine system. PTZs used in clinics as antipsychotic drugs with other diverse biological activities. The current aim of the study is to investigate and understand the effect of potent PTZs compounds using a group of In-vitro and In-vivo assays. A total of seventeen novel phenothiazine derivatives have been designed, synthesized, and evaluated primarily in-vitro for their ability to inhibit proliferation activity against NCI-60 cancer cell lines, including several multi-drug resistant (MDR) tumor cell lines. Almost all compounds were active and displayed promising cellular activities with GI50 values in the sub-micromolar range. Four of the most promising derivatives (4b, 4h, 4g and 6e) have been further tested against two selected sensitive cancer cell lines (colon cancer; HCT-116 and breast cancer; MDA-MB231). The apoptosis assay showed that all the selected compounds were able to induce early apoptosis and compound 6e was able to induce additional cellular necrosis. Cell cycle assay showed all selected compounds were able to induce cell cycle arrest at sub-molecular phase of G0-G1 with compound 6e induced cell cycle arrest at G2M in HCT-116 cells. Accordingly, the apoptotic effect of the selected compounds was extensively investigated on genetic level and Casp-3, Casp-9 and Bax gene were up-regulated with down-regulation of Bcl-2 gene suggesting the activation of both intrinsic and extrinsic pathways. In-vivo evaluation of the antitumor activity of compound 4b in solid tumor bearing mice showed promising therapeutic effect with manifestation of dose and time dependent toxic effects at higher doses. For better evaluation of the degree of localization of 4b, its 131I-congener (131I-4b) was injected intravenously in Ehrlich solid tumor bearing mice that showed good localization at tumor site with rapid distribution and clearance from the blood. In-silico study suggested NADPH oxidases (NOXs) as potential molecular target. The compounds introduced in the current study work provided a cutting-edge phenothiazine hybrid scaffold with promising anti-proliferation action that may suggest their anti-cancer activity.

S1PR1 modulators in multiple sclerosis: Efficacy, safety, comparison, and chemical structure insights

Multiple sclerosis (MS) is a neurological disease that leads to severe physical and cognitive disabilities. Drugs used in the treatment of MS vary from small synthetic molecules to large macromolecules such as antibodies. Sphingosine 1-phosphate receptor modulators are frequently used for the treatment of MS. These medicines prevent the egress of lymphocytes from secondary lymphoid organs leading to immune system suppression. Currently, four S1PR modulators are on the market and several potential drug candidates are in clinical trials for the treatment of MS. These compounds differ in chemical structure, adverse effects, and efficacy points of view. The current article reviews the latest studies on S1PR1 modulators and compares them with other MS drugs in terms of efficacy, tolerability, and safety. A special focus was dedicated to discussing the structure-activity relationships of these compounds and performing a three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis to gain better insight into the ligand-receptor interaction mode.

Synthesis, QSAR modeling, and molecular docking of novel fused 7-deazaxanthine derivatives as adenosine A2A receptor antagonists

Predictive QSAR models for the search of new adenosine A_2A receptor antagonists were developed by using OCHEM platform. The predictive ability of the regression models has coefficient of determination q²  = 0.65-0.71 with cross-validation and independent test set. The inhibition activities of novel fused 7-deazaxanthine compounds were predicted by the developed QSAR models. A preparative method for the synthesis of pyrimido[5',4':4,5]pyrrolo[1,2-a][1,4]diazepine derivatives was developed, and 11 new adenosine A_2A receptor antagonists were obtained. Preliminary investigations into the toxicology of fused 7-deazaxanthine compounds toward commonly used model organism to assess toxicity invertebrate cladoceran D. magna were also described.

3D-QSAR-Based Pharmacophore Modeling, Virtual Screening, and Molecular Docking Studies for Identification of Tubulin Inhibitors with Potential Anticancer Activity

In this study, we aimed to develop a pharmacophore-based three-dimensional quantitative structure activity relationship (3D-QSAR) for a set including sixty-two cytotoxic quinolines (1-62) as anticancer agents with tubulin inhibitory activity. A total of 279 pharmacophore hypotheses were generated based on the survival score to build QSAR models. A six-point pharmacophore model (AAARRR.1061) was identified as the best model which consisted of three hydrogen bond acceptors (A) and three aromatic ring (R) features. The model showed a high correlation coefficient (R 2 = 0.865), cross-validation coefficient (Q 2 = 0.718), and F value (72.3). The best pharmacophore model was then validated by the Y-Randomization test and ROC-AUC analysis. The generated 3D contour maps were used to reveal the structure activity relationship of the compounds. The IBScreen database was screened against AAARRR.1061, and after calculating ADMET properties, 10 compounds were selected for further docking study. Molecular docking analysis showed that compound STOCK2S-23597 with the highest docking score (-10.948 kcal/mol) had hydrophobic interactions and can form four hydrogen bonds with active site residues.

Using computer tools for the evaluation of biodegradability, toxicity, and activity on the AT1 receptor of degradation products identified in the removal of valsartan by using photo-electro-Fenton process

This work deals with the theoretical approach of biodegradability, lipophilicity, and physiological activity of VAL and four degradation products (DPs) detected after 20 min of the photo-electro-Fenton (PEF) process. The biodegradability calculation, taking into account the change in the theoretical oxygen demand, showed that the four DPs had a more negative value than VAL, indicating that they are more susceptible to oxidation. However, these results do not imply more accessible biotransformation pathways than VAL, as observed using the EAWAG-BBD program, through which neutral biotransformation pathway prediction for VAL and DPs was made. Subsequently, by calculating the theoretical lipophilicity of the molecules (log P), the theoretical toxicity of the DPs was proposed, where the DPs had log P values between 1 and 3, lower values than those of VAL (log P = 4), indicating that DPs could be less toxic than the original compound (VAL). Both results suggest that VAL degradation (by photo-electro-Fenton process proposed) yields a positive effect on the environment. Finally, when molecular dynamic simulations were carried out, it was observed that DP1, DP2, and DP3 maintained similar interactions to those of VAL with the binding site of the AT1R. DP4 did not show any interaction. These results indicated that, despite the presence of DPs, generated after 20 min of the treatment, they could not exert a physiological activity in any organism the same way that does VAL.

Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases

Neurodegenerative diseases (NDs) including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.

Generative chemistry: drug discovery with deep learning generative models

The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures the creativity of deep learning generative models exhibits the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.

G protein-coupled receptors: structure- and function-based drug discovery

As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure-function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.

Identification of novel CDK 9 inhibitors based on virtual screening, molecular dynamics simulation, and biological evaluation

AIMS

Cyclin-dependent kinase 9 (CDK9) is a member of the CDK subfamily and plays a major role in the regulation of transcriptional elongation. It has attracted widespread attention as a therapeutic target for cancer. Here, we aimed to explore novel CDK 9 inhibitors by using a hybrid virtual screening strategy.

MAIN METHODS

A hybrid virtual screening strategy was constructed with computer-aided drug design (CADD). First, compounds were filtered in accordance with Lipinski's rule of five and adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Second, a 3D-QSAR pharmacophore model was built and used as a 3D query to screen the obtained hit compounds. Third, the hit compounds were subjected to molecular docking studies. Fourth, molecular dynamics (MD) simulations were performed on CDK9 in complex with the final hits to examine the structural stability. Finally, CDK9 kinase biochemical assay was performed to identify the biological activity of the hit compounds.

KEY FINDINGS

Seven hit compounds were screened out. These hit compounds showed drug-like properties in accordance with Lipinski's rule of five and ADMET. Complexes involving the six hit compounds bound to CDK9 exhibited good structural stability in the MD simulation. Furthermore, these six hit compounds had strong inhibitory activity against CDK9 kinase. In particular, hit 3 showed the most promising activity with the percentage of 71%.

SIGNIFICANCE

The six hit compounds may be promising novel CDK9 inhibitors, and the hybrid virtual screening strategy designed in this study provides an important reference for the design and synthesis of novel CDK9 inhibitors.

A Taxicab geometry quantification system to evaluate the performance of in silico methods: a case study on adenosine receptors ligands

Among still comparatively few G protein-coupled receptors, the adenosine A_2A receptor has been co-crystallized with several ligands, agonists as well as antagonists. It can thus serve as a template with a well-described orthosteric ligand binding region for adenosine receptors. As not all subtypes have been crystallized yet, and in order to investigate the usability of homology models in this context, multiple adenosine A₁ receptor (A₁AR) homology models had been previously obtained and a library of lead-like compounds had been docked. As a result, a number of potent and one selective ligand toward the intended target have been identified. However, in in vitro experimental verification studies, many ligands also bound to the A_2AAR and the A₃AR subtypes. In this work we asked the question whether a classification of the ligands according to their selectivity was possible based on docking scores. Therefore, we built an A₃AR homology model and docked all previously found ligands to all three receptor subtypes. As a metric, we employed an in vitro/in silico selectivity ranking system based on taxicab geometry and obtained a classification model with reasonable separation. In the next step, the method was validated with an external library of, selective ligands with similarly good performance. This classification system might also be useful in further screens.

Comparative Study of Dietary Flavonoids with Different Structures as α-Glucosidase Inhibitors and Insulin Sensitizers

This work was designed to comparatively investigate 27 dietary flavonoids that act as α-glucosidase inhibitors and insulin sensitizers. On the basis of the results of an in vitro experiment of α-glucosidase inhibition, myricetin (IC₅₀ = 11.63 ± 0.36 μM) possessed the strongest inhibitory effect, followed by apigenin-7-O-glucoside (IC₅₀ = 22.80 ± 0.24 μM) and fisetin (IC₅₀ = 46.39 ± 0.34 μM). A three-dimensional quantitative structure-activity relationship model of α-glucosidase inhibitors with good predictive capability [comparative molecular field analysis, q² = 0.529, optimum number of components (ONC) = 10, R² = 0.996, F = 250.843, standard error of estimation (SEE) = 0.064, and two descriptors; comparative similarity index analysis, q² = 0.515, ONC = 10, R² = 0.997, F = 348.301, SEE = 0.054, and four descriptors] was established and indicated that meta positions of ring B favored bulky and minor, electron-withdrawing, and hydrogen bond donor groups. The presence of electron-donating and hydrogen bond acceptor groups at position 4' of ring B could improve α-glucosidase activity. Position 3 of ring C favored minor, electron-donating, and hydrogen bond donor groups, whereas position 7 of ring A favored bulky and hydrogen bond acceptor groups. Molecular docking screened five flavonoids (baicalein, isorhamnetin-3-O-rutinoside, apigenin-7-O-glucoside, kaempferol-7-O-β-glucoside, and cyanidin-3-O-glucoside) that can act as insulin sensitizers and form strong combinations with four key protein targets involved in the insulin signaling pathway. Apigenin-7-O-glucoside (60 μM) can effectively improve insulin resistance, and glucose uptake increased by approximately 73.06% relative to the model group of insulin-resistant HepG2 cells. Therefore, apigenin-7-O-glucoside might serve as the most effective α-glucosidase inhibitor and insulin sensitizer. This work may guide diabetes patients to improve their condition through dietary therapy.