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. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.

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. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.

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. In this work, we used multiple docked poses of TTK inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contacts Fingerprints and docking scoring values were used as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to determine critical descriptors for predicting anti-TTK bioactivity and for pharmacophore generation. Three successful pharmacophores were deduced and subsequently used for in silico screening against the NCI database. A total of 14 hits were evaluated in vitro for their anti-TTK bioactivities. One hit of novel chemotype showed reasonable dose-response curve with experimental IC50 of 1.0 µM. The presented work indicates the validity of data augmentation using multiple docked poses for building successful machine learning models and pharmacophore hypotheses.

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. In this work, we used multiple docked poses of STAT3 inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contact Fingerprints and scoring values were implemented as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to identify critical descriptors that determine anti-STAT3 bioactivity to be translated into pharmacophore model(s). Two successful pharmacophores were deduced and subsequently used for in silico screening against the National Cancer Institute (NCI) database. A total of 26 hits were evaluated in vitro for their anti-STAT3 bioactivities. Out of which, three hits of novel chemotypes, showed cytotoxic IC50 values in the nanomolar range (35 nM to 6.7 µM). However, two are potent dihydrofolate reductase (DHFR) inhibitors and therefore should have significant indirect STAT3 inhibitory effects. The third hit (cytotoxic IC50 = 0.44 µM) is purely direct STAT3 inhibitor (devoid of DHFR activity) and caused, at its cytotoxic IC50, more than two-fold reduction in the expression of STAT3 downstream genes (c-Myc and Bcl-xL). The presented work indicates that the concept of data augmentation using multiple docked poses is a promising strategy for generating valid machine learning models capable of discriminating active from inactive compounds.

Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors via 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). A genetic function algorithm (GFA) was combined with each method for feature selection. Eventually, GFA-SVR was found to produce the best self-consistent and predictive model. The model selected three pharmacophores, three ligand-receptor contacts and two physicochemical descriptors. The GFA-SVR model and associated pharmacophore models were used to screen the National Cancer Institute (NCI) structural database for novel CLK4 antagonists. Three potent hits were identified with the best one showing an anti-CLK4 IC50 value of 57 nM.

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. In this study, we exploited the advantages of reduced cytotoxicity and enhanced cellular penetration of chitosan-phthalate (CSP) together with the merits of lecithin (LC)-based nanoparticles (NPs) to create novel, ellipsoid, non-cytotoxic, tripolyphosphate (TPP)-crosslinked NPs capable of delivering siRNA efficiently. The resulting NPs were characterized by dynamic light scattering (DLS) and transmission electron microscopy (TEM), and were found to be ellipsoid in the shape of ca. 180 nm in size, exhibiting novel double-layer shells, with excellent stability at physiological pH and in serum solutions. MTT assay and confocal fluorescence microscopy showed that CSP-LC-TPP NPs are non-cytotoxic and efficiently penetrate cancer cells in vitro. They achieved 44% silencing against SLUG protein in MDA-MB-453 cancer cells and were significantly superior to a commercial liposome-based transfection agent that achieved only 30% silencing under comparable conditions. Moreover, the NPs protected their siRNA cargos in 50% serum and from being displaced by variable concentrations of heparin. In fact, CSP-LC-TPP NPs achieved 26% transfection efficiency in serum containing cell culture media. Real-time wide-field fluorescence microscopy showed siRNA-loaded CSP-LC-TPP NPs to successfully release their cargo intracellularly. We found that the amphoteric nature of chitosan-phthalate polymer promotes the endosomal escape of siRNA and improves the silencing efficiency.

Combining docking-based comparative intermolecular contacts analysis and k-nearest neighbor correlation for the discovery of new check point kinase 1 inhibitors.

Check point kinase 1 (Chk1) is an important protein in G2 phase checkpoint arrest required by cancer cells to maintain cell cycle and to prevent cell death. Therefore, Chk1 inhibitors should have potential as anti-cancer therapeutics. Docking-based comparative intermolecular contacts analysis (dbCICA) is a new three-dimensional quantitative structure activity relationship method that depends on the quality and number of contact points between docked ligands and binding pocket amino acid residues. In this presented work we implemented a novel combination of k-nearest neighbor/genetic function algorithm modeling coupled with dbCICA to select critical ligand-Chk1 contacts capable of explaining anti-Chk1 bioactivity among a long list of inhibitors. The finest set of contacts were translated into two valid pharmacophore hypotheses that were used as 3D search queries to screen the National Cancer Institute's structural database for new Chk1 inhibitors. Three potent Chk1 inhibitors were discovered with IC50 values ranging from 2.4 to 69.7 µM.

Haloperidol inhibits Memapsin 2: innovation by docking simulation and in vitro assay.

A number of drugs exhibit unexpected pharmacological effects related to their ability to bind more than one receptor in humans. Haloperidol a typical antipsychotic drug appeared in several reports to be used in schizophrenia patients in which the significant of Alzheimer's disease has been reduced. The etiology of the disease is characterized by aggregates of amyloid plaques, largely composed of amyloid-ß peptide formed from the amyloid precursor protein cleaved by Memapsin 2. To investigate if haloperidol can bind to Memapsin 2 active site, an initial molecular docking was performed as a preliminary in-silico screening test followed by in vitro enzyme inhibition assay. Haloperidol was found to fit readily in Memapsin binding site with IC(50)value 250mM. Haloperidol can be considered as important lead or important target can be modified for more inhibitory activity, with the intention of protection or treatment for Alzheimer's disease.

In silico screening for non-nucleoside HIV-1 reverse transcriptase inhibitors using physicochemical filters and high-throughput docking followed by in vitro evaluation.

Reverse transcriptase, being the pivot in human immunodeficiency virus replication, is one of the most attractive targets for the development of new antiretroviral agents. We applied a virtual screening workflow based on a combination of physicochemical filters with high-throughput rigid molecular docking to discover novel efficient lead scaffolds for human immunodeficiency virus type 1 reverse transcriptase inhibition. In our protocol, different filters were employed to enrich the lead-likeness and improve the ligands efficiency of the filtered compounds. Out of the 238,819 compounds included in the National Cancer Institute database, 500 virtual screening hits were retrieved employing FILTER and FRED (molecular docking engine) softwares. Four compounds from the 20 highest ranking scored hits tested positive in human immunodeficiency virus type 1 reverse transcriptase using non-radioactive colorimetric assay method. These results demonstrate that our virtual screening protocol is able to enrich novel scaffolds for human immunodeficiency virus type 1 reverse transcriptase inhibition that could be useful for drug development in the area of acquired immune-deficiency syndrome treatment.

Docking simulations and in vitro assay unveil potent inhibitory action of papaverine against protein tyrosine phosphatase 1B.

The structural similarity between papaverine and berberine, a known inhibitor of human protein tyrosine phosphatase 1B (h-PTP 1B), prompted us to investigate the potential of papaverine as h-PTP 1B inhibitor. The investigation included simulated docking experiments to fit papaverine into the binding pocket of h-PTP 1B. Papaverine was found to readily dock within the binding pocket of h-PTP 1B in a low energy orientation via an optimal set of attractive interactions. Experimentally, papaverine illustrated potent in vitro inhibitory effect against recombinant h-PTP 1B (IC(50)=1.20 microM). In vivo, papaverine significantly decreased fasting blood glucose level of Balb/c mice. Our findings should encourage screening of other natural alkaloids for possible anti-h-PTP 1B activities.