Identification of small molecule antagonists of sonic hedgehog/heparin binding with activity in hedgehog functional assays.

Sonic hedgehog (Shh) is a morphogen with important roles in embryonic development and in the development of a number of cancers. Its activity is modulated by interactions with binding partners and co-receptors including heparin and heparin sulfate proteoglycans (HSPG). To identify antagonists of Shh/heparin binding, a diverse collection of 34,560 chemicals was screened in single point 384-well format.

Identification of Novel GANT61 Analogs with Activity in Hedgehog Functional Assays and GLI1-Dependent Cancer Cells.

Aberrant activation of hedgehog (Hh) signaling has been implicated in various cancers. Current FDA-approved inhibitors target the seven-transmembrane receptor Smoothened, but resistance to these drugs has been observed. It has been proposed that a more promising strategy to target this pathway is at the GLI1 transcription factor level.

Innervation of nociceptor neurons in the spleen promotes germinal center responses and humoral immunity.

Peripheral sensory neurons widely innervate various tissues to continuously monitor and respond to environmental stimuli. Whether peripheral sensory neurons innervate the spleen and modulate splenic immune response remains poorly defined. Here, we demonstrate that nociceptive sensory nerve fibers extensively innervate the spleen along blood vessels and reach B cell zones.

Applying a Gene Reversal Rate Computational Methodology to Identify Drugs for a Rare Cancer: Inflammatory Breast Cancer.

The aim of this study was to utilize a computational methodology based on Gene Reversal Rate (GRR) scoring to repurpose existing drugs for a rare and understudied cancer: inflammatory breast cancer (IBC). This method uses IBC-related gene expression signatures (GES) and drug-induced gene expression profiles from the LINCS database to calculate a GRR score for each candidate drug, and is based on the idea that a compound that can counteract gene expression changes of a disease may have potential therapeutic applications for that disease. Genes related to IBC with associated differential expression data (265 up-regulated and 122 down-regulated) were collated from PubMed-indexed publications.

Lead/Drug Discovery from Natural Resources.

Natural products and their derivatives have been shown to be effective drug candidates against various diseases for many years. Over a long period of time, nature has produced an abundant and prosperous source pool for novel therapeutic agents with distinctive structures. Major natural-product-based drugs approved for clinical use include anti-infectives and anticancer agents.

Cheminformatics Modeling of Gene Silencing for Both Natural and Chemically Modified siRNAs.

In designing effective siRNAs for a specific mRNA target, it is critically important to have predictive models for the potency of siRNAs. None of the published methods characterized the chemical structures of individual nucleotides constituting a siRNA molecule; therefore, they cannot predict the potency of gene silencing by chemically modified siRNAs (cm-siRNA). We propose a new approach that can predict the potency of gene silencing by cm-siRNAs, which characterizes each nucleotide (NT) using 12 BCUT cheminformatics descriptors describing its charge distribution, hydrophobic and polar properties.

Data Mining Meets Machine Learning: A Novel ANN-based Multi-body Interaction Docking Scoring Function (MBI-score) Based on Utilizing Frequent Geometric and Chemical Patterns of Interfacial Atoms in Native Protein-ligand Complexes.

Accurate prediction of binding poses is crucial to structure-based drug design. We employ two powerful artificial intelligence (AI) approaches, data-mining and machine-learning, to design artificial neural network (ANN) based pose-scoring function. It is a simple machine-learning-based statistical function that employs frequent geometric and chemical patterns of interacting atoms at protein-ligand interfaces.

Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses.

We aimed to develop and validate a new graph embedding algorithm for embedding drug-disease-target networks to generate novel drug repurposing hypotheses. Our model denotes drugs, diseases and targets as subjects, predicates and objects, respectively. Each entity is represented by a multidimensional vector and the predicate is regarded as a translation vector from a subject to an object vectors.

A semantic similarity based methodology for predicting protein-protein interactions: Evaluation with P53-interacting kinases.

Biomedical literature contains unstructured, rich information regarding proteins, ligands, diseases as well as biological pathways in which they are involved. Systematically analyzing such textual corpus has the potential for biomedical discovery of new protein-protein interactions and hidden drug indications. For this purpose, we have investigated a methodology that is based on a well-established text mining tool, Word2Vec, for the analysis of PubMed full text articles to derive word embeddings, and the use of a simple semantic similarity comparison either by itself or in conjunction with k-Nearest Neighbor (kNN) technique for the prediction of new relationships.

Literature-Wide Association Studies (LWAS) for a Rare Disease: Drug Repurposing for Inflammatory Breast Cancer.

Drug repurposing is an effective means for rapid drug discovery. The aim of this study was to develop and validate a computational methodology based on Literature-Wide Association Studies (LWAS) of PubMed to repurpose existing drugs for a rare inflammatory breast cancer (IBC). We have developed a methodology that conducted LWAS based on the text mining technology Word2Vec.

TNFAIP8 regulates autophagy, cell steatosis, and promotes hepatocellular carcinoma cell proliferation.

Tumor necrosis factor-α-induced protein 8 (TNFAIP8) expression has been linked to tumor progression in various cancer types, but the detailed mechanisms of TNFAIP8 are not fully elucidated. Here we define the role of TNFAIP8 in early events associated with development of hepatocellular carcinoma (HCC). Increased TNFAIP8 levels in HCC cells enhanced cell survival by blocking apoptosis, rendering HCC cells more resistant to the anticancer drugs, sorafenib and regorafenib.

Oncogenic Role of Tumor Necrosis Factor α-Induced Protein 8 (TNFAIP8).

Tumor necrosis factor (TNF)-α-induced protein 8 (TNFAIP8) is a founding member of the TIPE family, which also includes TNFAIP8-like 1 (TIPE1), TNFAIP8-like 2 (TIPE2), and TNFAIP8-like 3 (TIPE3) proteins. Expression of TNFAIP8 is strongly associated with the development of various cancers including cancer of the prostate, liver, lung, breast, colon, esophagus, ovary, cervix, pancreas, and others. In human cancers, TNFAIP8 promotes cell proliferation, invasion, metastasis, drug resistance, autophagy, and tumorigenesis by inhibition of cell apoptosis.

Novel HIV-1 Non-nucleoside Reverse Transcriptase Inhibitor Agents: Optimization of Diarylanilines with High Potency against Wild-Type and Rilpivirine-Resistant E138K Mutant Virus.

Three series (6, 13, and 14) of new diarylaniline (DAAN) analogues were designed, synthesized, and evaluated for anti-HIV potency, especially against the E138K viral strain with a major mutation conferring resistance to the new-generation non-nucleoside reverse transcriptase inhibitor drug rilpivirine (1b). Promising new compounds were then assessed for physicochemical and associated pharmaceutical properties, including aqueous solubility, log P value, and metabolic stability, as well as predicted lipophilic parameters of ligand efficiency, ligand lipophilic efficiency, and ligand efficiency-dependent lipophilicity indices, which are associated with ADME property profiles. Compounds 6a, 14c, and 14d showed high potency against the 1b-resistant E138K mutated viral strain as well as good balance between anti-HIV-1 activity and desirable druglike properties.

Assay development and multivariate scoring for high-content discovery of chemoprotectants of endoplasmic-reticulum-stress-mediated amylin-induced cytotoxicity in pancreatic beta cells.

The underlying pathogenesis of type-II diabetes mellitus is in the dysfunction and selective loss of pancreatic islet β-cells, which ultimately leads to underproduction of endogenous insulin. Amylin, a 37-amino-acid human hormone that is cosecreted with insulin, helps regulate gastric emptying and maintain blood glucose homeostasis through improved postprandial satiety. It is hypothesized that amylin protofibrils cause selective loss of pancreatic β-cells in a manner similar to amyloid β aggregation in Alzheimer's disease.

The Development of Novel Chemical Fragment-Based Descriptors Using Frequent Common Subgraph Mining Approach and Their Application in QSAR Modeling.

We present a novel approach to generating fragment-based molecular descriptors. The molecules are represented by labeled undirected chemical graph. Fast Frequent Subgraph Mining (FFSM) is used to find chemical-fragments (subgraphs) that occur in at least a subset of all molecules in a dataset.

RapidMic: Rapid Computation of the Maximal Information Coefficient.

To discover relationships and associations rapidly in large-scale datasets, we propose a cross-platform tool for the rapid computation of the maximal information coefficient based on parallel computing methods. Through parallel processing, the provided tool can effectively analyze large-scale biological datasets with a markedly reduced computing time. The experimental results show that the proposed tool is notably fast, and is able to perform an all-pairs analysis of a large biological dataset using a normal computer.

Scoring protein interaction decoys using exposed residues (SPIDER): a novel multibody interaction scoring function based on frequent geometric patterns of interfacial residues.

Accurate prediction of the structure of protein-protein complexes in computational docking experiments remains a formidable challenge. It has been recognized that identifying native or native-like poses among multiple decoys is the major bottleneck of the current scoring functions used in docking. We have developed a novel multibody pose-scoring function that has no theoretical limit on the number of residues contributing to the individual interaction terms.

Receptor-based pharmacophore and pharmacophore key descriptors for virtual screening and QSAR modeling.

The intuitive nature of the pharmacophore concept has made it widely accepted by the medicinal chemistry community, evidenced by the past 3 decades of development and application of computerized pharmacophore modeling tools. On the other hand, shape complementarity has been recognized as a critical factor in molecular recognition between drugs and their receptors. Recent development of fast and accurate shape comparison tools has facilitated the wide spread use of shape matching technologies in drug discovery.

Structure-based quantitative structure--activity relationship modeling of estrogen receptor β-ligands.

Background: A variety of chemotypes have been studied as estrogen receptor (ER) β-selective ligands for potential drugs against various indications, including neurodegenerative diseases. Their structure--activity relationship data and the x-ray structures of the ERβ ligand-binding domain bound with different ligands have become available. Thus, it is vitally important for future development of ERβ-selective ligands that robust quantitative structure-activity relationship (QSAR) models be built.

Cheminformatics Approach to Gene Silencing: Z Descriptors of Nucleotides and SVM Regression Afford Predictive Models for siRNA Potency.

Short interfering RNA mediated gene silencing technology has been through tremendous development over the past decade, and has found broad applications in both basic biomedical research and pharmaceutical development. Critical to the effective use of this technology is the development of reliable algorithms to predict the potency and selectivity of siRNAs under study. Existing algorithms are mostly built upon sequence information of siRNAs and then employ statistical pattern recognition or machine learning techniques to derive rules or models.

Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design.

Optimization of chemical library composition affords more efficient identification of hits from biological screening experiments. The optimization could be achieved through rational selection of reagents used in combinatorial library synthesis. However, with a rapid advent of parallel synthesis methods and availability of millions of compounds synthesized by many vendors, it may be more efficient to design targeted libraries by means of virtual screening of commercial compound collections.

Informatics approach to the rational design of siRNA libraries.

This chapter surveys the literature for state-of-the-art methods for the rational design of siRNA libraries. It identifies and presents major milestones in the field of computational modeling of siRNA's gene silencing efficacy. Commonly used features of siRNAs are summarized along with major machine learning techniques employed to build the predictive models.

Molecular shape technologies in drug discovery: methods and applications.

Shape complementarity is a critically important factor in molecular recognition among drugs and their biological receptors. The notion that molecules with similar 3D shapes tend to have similar biological activity has been recognized and implemented in computational drug discovery tools for decades. But the low computational efficiency and the lack of widely accessible software tools limited the use of early shape-matching algorithms.

Development of improved models for phosphodiesterase-4 inhibitors with a multi-conformational structure-based QSAR method.

Phosphodiesterase-4 (PDE-4) is an important drug target for several diseases, including COPD (chronic obstructive pulmonary disorder) and neurodegenerative diseases. In this paper, we describe the development of improved QSAR (quantitative structure-activity relationship) models using a novel multi-conformational structure-based pharmacophore key (MC-SBPPK) method. Similar to our previous work, this method calculates molecular descriptors based on the matching of a molecule's pharmacophore features with those of the target binding pocket.

A novel structure-based multimode QSAR method affords predictive models for phosphodiesterase inhibitors.

Quantitative structure-activity relationship (QSAR) methods aim to build quantitatively predictive models for the discovery of new molecules. It has been widely used in medicinal chemistry for drug discovery. Many QSAR techniques have been developed since Hansch's seminal work, and more are still being developed.