Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors.

Activity cliffs (ACs) are pairs of structurally similar molecules with significantly different affinities for a biotarget, posing a challenge in computer-assisted drug discovery. This study focuses on protein kinases, significant therapeutic targets, with some exhibiting ACs while others do not despite numerous inhibitors. The hypothesis that the presence of ACs is dependent on the target protein and its complete structural context is explored.

Preparation of novel shell-ionotropically crosslinked micelles based on hexadecylamine and tripolyphosphate for cancer drug delivery.

Aims: Micellar systems have the advantage of being easily prepared, cheap, and readily loadable with bioactive molecular cargo. However, their fundamental pitfall is poor stability, particularly under dilution conditions. We propose to use simple quaternary ammonium surfactants, namely, hexadecylamine (HDA) and hexadecylpyridinium (HDAP), together with tripolyphosphate (TPP) anion, to generate ionotropically stabilized micelles capable of drug delivery into cancer cells.

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes.

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers.

Synthesis and antitumor activity of model cyclopentene-[]annelated isoindigos.

A set of cyclopenten-[]annelated isoindigos () has been prepared and tested for their antiproliferative activities against MCF-7 and HL60 cells. Among, the -1-methyl-5'-nitro derivative () displayed the highest activity against HL60 cells (IC = 67 nM) and acted as the most potent Flt3 inhibitor. Compounds exhibited moderate activity against MCF-7 (IC = 50-80 μM).

Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study.

STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer.

Augmenting bioactivity by docking-generated multiple ligand poses to enhance machine learning and pharmacophore modelling: discovery of new TTK inhibitors as case study.

Dual specificity protein kinase threonine/Tyrosine kinase (TTK) is one of the mitotic kinases. High levels of TTK are detected in several types of cancer. Hence, TTK inhibition is considered a promising therapeutic anti-cancer strategy.

Molecular docking, molecular dynamics simulations and screening reveal cefixime and ceftriaxone as GSK3β covalent inhibitors.

GSK3β is a serine/threonine kinase that has been suggested as a putative drug target for several diseases. Recent studies have reported the beneficial effects of cephalosporin antibiotics in cancer and Alzheimer's disease, implying potential inhibition of GSK3β. To investigate this mechanism, four cephalosporins, namely, cefixime, ceftriaxone, cephalexin and cefadroxil were docked into the GSK3β binding pocket.

Discovery of new STAT3 inhibitors as anticancer agents using ligand-receptor contact fingerprints and docking-augmented machine learning.

STAT3 belongs to a family of seven vital transcription factors. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy.

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning.

Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning.

Discovery of new PKN2 inhibitory chemotypes via QSAR-guided selection of docking-based pharmacophores.

Serine/threonine-protein kinase N2 (PKN2) plays an important role in cell cycle progression, cell migration, cell adhesion and transcription activation signaling processes. In cancer, however, it plays important roles in tumor cell migration, invasion and apoptosis. PKN2 inhibitors have been shown to be promising in treating cancer.

Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors pharmacophore exploration combined with flexible docking-based ligand/receptor contact fingerprints and machine learning.

Cdc2-like kinase 4 (CLK4) inhibitors are of potential therapeutic value in many diseases particularly cancer. In this study, we combined extensive ligand-based pharmacophore exploration, ligand-receptor contact fingerprints generated by flexible docking, physicochemical descriptors and machine learning-quantitative structure-activity relationship (ML-QSAR) analysis to investigate the pharmacophoric/binding requirements for potent CLK4 antagonists. Several ML methods were attempted to tie these properties with anti-CLK4 bioactivities including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGBoost), probabilistic neural network (PNN), and support vector regression (SVR).

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors.

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors.

Vitamin B12 binding to mutated human transcobalamin, in-silico study of TCN2 alanine scanning and ClinVar missense mutations/SNPs.

Many missense mutations/SNPs of the TCN2 gene (which yield Transcobalamin (TC)) were reported in the literature but no study is available about their effect on binding to vitamin B12(B12) at the structural level experimentally nor computationally. Predict the effect of TC missense mutations/SNPs on binding affinity to B12 and characterize their contacts to B12 at the structural level. TC-B12 binding energy difference from the wildtype (ΔΔGmut) was calculated for 378 alanine scanning mutations and 76 ClinVar missense mutations, repeated on two distinct X-ray structures of holoTC namely 2BB5 and 4ZRP.

Exploiting activity cliffs for building pharmacophore models and comparison with other pharmacophore generation methods: sphingosine kinase 1 as case study.

Activity cliffs (ACs) are defined as closely analogous compounds of significant affinity discrepancies against certain biotarget. In this paper we propose to use AC pair(s) for extracting valid binding pharmacophores through exposing corresponding protein complexes to stochastic deformation/relaxation followed by applying genetic algorithm/machine learning (GA-ML) for selecting optimal pharmacophore(s) that best classify a long list of inhibitors. We compared the performances of ligand-based and structure-based pharmacophores with counterparts generated by this newly introduced technique.

Ligand-based Modeling of CXC Chemokine Receptor 4 and Identification of Inhibitors of Novel Chemotypes as Potential Leads towards New Anti- COVID-19 Treatments.

Background: Chemokines are involved in several human diseases and different stages of COVID-19 infection. They play a critical role in the pathophysiology of the associated acute respiratory disease syndrome, a major complication leading to death among COVID-19 patients. In particular, CXC chemokine receptor 4 (CXCR4) was found to be highly expressed in COVID-19 patients.

Docking-generated multiple ligand poses for bootstrapping bioactivity classifying Machine Learning: Repurposing covalent inhibitors for COVID-19-related TMPRSS2 as case study.

In the present work we introduce the use of multiple docked poses for bootstrapping machine learning-based QSAR modelling. Ligand-receptor contact fingerprints are implemented as descriptor variables. We implemented this method for the discovery of potential inhibitors of the serine protease enzyme TMPRSS2 involved the infectivity of coronaviruses.

Novel Ellipsoid Chitosan-Phthalate Lecithin Nanoparticles for siRNA Delivery.

Small interfering RNA (siRNA) has received increased interest as a gene therapeutic agent. However, instability and lack of safe, affordable, and effective carrier systems limit siRNA's widespread clinical use. To tackle this issue, synthetic vectors such as liposomes and polymeric nanoparticles have recently been extensively investigated.

Inhibition of aggregation of amyloid-β through covalent modification with benzylpenicillin; potential relevance to Alzheimer's disease.

The pathogenesis of Alzheimer's disease (AD) is correlated with the misfolding and aggregation of amyloid- protein (Aβ). Here we report that the antibiotic benzylpenicillin (BP) can specifically bind to Aβ, modulate the process of aggregation and supress its cytotoxic effect, initially a reversible binding interaction, followed by covalent bonding between specific functional groups (nucleophiles) within the Aβ peptide and the -lactam ring. Mass spectrometry and computational docking supported covalent modification of Aβ by BP.

Design and Synthesis of New JAK1 Inhibitors based on Sulfonamide- Triazine Conjugates.

Aims: Design of sulfonamide-triazine derivatives as JAK1 inhibitors.

Background: JAK1 is a kinase involved in different autoimmune diseases. JAK1 inhibitors have shown promising results in treating autoimmune diseases.

Structure-Based Discovery and Bioactivity Evaluation of Novel Aurora-A Kinase Inhibitors as Anticancer Agents via Docking-Based Comparative Intermolecular Contacts Analysis (dbCICA).

Aurora-A kinase plays a central role in mitosis, where aberrant activation contributes to cancer by promoting cell cycle progression, genomic instability, epithelial-mesenchymal transition, and cancer stemness. Aurora-A kinase inhibitors have shown encouraging results in clinical trials but have not gained Food and Drug Administration (FDA) approval. An innovative computational workflow named Docking-based Comparative Intermolecular Contacts Analysis (dbCICA) was applied-aiming to identify novel Aurora-A kinase inhibitors-using seventy-nine reported Aurora-A kinase inhibitors to specify the best possible docking settings needed to fit into the active-site binding pocket of Aurora-A kinase crystal structure, in a process that only potent ligands contact critical binding-site spots, distinct from those occupied by less-active ligands.

Comprehensive Structural and Molecular Comparison of Spike Proteins of SARS-CoV-2, SARS-CoV and MERS-CoV, and Their Interactions with ACE2.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has recently emerged in China and caused a disease called coronavirus disease 2019 (COVID-19). The virus quickly spread around the world, causing a sustained global outbreak. Although SARS-CoV-2, and other coronaviruses, SARS-CoV and Middle East respiratory syndrome CoV (MERS-CoV) are highly similar genetically and at the protein production level, there are significant differences between them.

Elaboration of Novel TTK1 Inhibitory Leads via QSAR-Guided Selection of Crystallographic Pharmacophores Followed By In Vitro Assay.

Introduction: Tyrosine threonine kinase (TTK1) is a key regulator of chromosome segregation. Recently, TTK targeting came into focus for the enhancement of possible anticancer therapies.

Objective: In this regard, we employed our well-known method of QSAR-guided selection of the best crystallographic pharmacophore(s) to discover considerable binding interactions that transfer inhibitors into TTK1 binding site.

Pharmacophore modeling of JAK1: A target infested with activity-cliffs.

Janus kinase 1 (JAK1) is protein kinase involved in autoimmune diseases (AIDs). JAK1 inhibitors have shown promising results in treating AIDs. JAK1 inhibitors are known to exhibit regions of SAR discontinuity or activity cliffs (ACs).