Chemical Informatics Functionality in R

The flexibility and scope of the R programming environment has made it a popular choice for statistical modeling and scientific prototyping in a number of fields. In the field of chemistry, R provides several tools for a variety of problems related to statistical modeling of chemical information. However, one aspect common to these tools is that they do not have direct access to the information that is available from chemical structures, such as contained in molecular descriptors. We describe the rcdk package that provides the R user with access to the CDK, a Java framework for cheminformatics. As a result, it is possible to read in a variety of molecular formats, calculate molecular descriptors and evaluate fingerprints. In addition, we describe the rpubchem that will allow access to the data in PubChem, a public repository of molecular structures and associated assay data for approximately 8 million compounds. Currently, the package allows access to structural information as well as some simple molecular properties from PubChem. In addition the package allows access to bio-assay data from the PubChem FTP servers.

Chemical Informatics Functionality in R

The flexibility and scope of the R programming environment has made it a popular choice for statistical modeling and scientific prototyping in a number of fields. In the field of chemistry, R provides several tools for a variety of problems related to statistical modeling of chemical information. However, one aspect common to these tools is that they do not have direct access to the information that is available from chemical structures, such as contained in molecular descriptors. We describe the rcdk package that provides the R user with access to the CDK, a Java framework for cheminformatics. As a result, it is possible to read in a variety of molecular formats, calculate molecular descriptors and evaluate fingerprints. In addition, we describe the rpubchem that will allow access to the data in PubChem, a public repository of molecular structures and associated assay data for approximately 8 million compounds. Currently, the package allows access to structural information as well as some simple molecular properties from PubChem. In addition the package allows access to bio-assay data from the PubChem FTP servers

Cannabinoids suppress the innate immune response to periodontal pathogen Porphyromonas gingivalis in gingival epithelial cells.

Marijuana is widely used in the United States for recreational and medicinal purposes. Despite the proposed beneficial effects of cannabis on certain medical conditions, there is also a concern there is also a concern over negative health consequences of marijuana smoking. Recently, cannabis use has been established as a dose related risk factor for chronic periodontitis. Although the induction / exacerbation of chronic periodontitis in marijuana users has been established epidemiologically, the mechanisms underlying such predisposition are still unknown. It has long been suggested that marijuana has anti-inflammatory properties, while human epithelial cells are known to express cannabinoid receptor type 2 (CB2). Therefore, we hypothesized that marijuana-derived cannabinoids may suppress the innate immune response, altering the ability of gingival epithelial cells to respond appropriately to bacterial stimuli (the classic TLR4 ligand, E. coli LPS; and the major periodontal vii pathogen, Porphyromonas gingivalis). More specifically, we hypothesized that predominant phytocannabinoid subtypes (cannabidiol [CBD], cannabinol [CBN], or tetrahydrocannabinol [THC]) at physiologically relevant doses (0 - 10µg/ml) would suppress proinflammatory cytokine production in LPS- (0-1µg/ml) or P. gingivalis (MOI, 10:1) exposed telomerase immortalized human gingival keratinocytes (TIGK cells) in a CB2-related manner. We also hypothesized that marijuana-derived cannabinoids may influence the viability of both epithelial (TIGK cells, as assessed by Trypan blue exclusion) and representative oral bacteria (P. gingivalis, Treponema denticola and Filifactor alocis, growth curves monitored by optical density). Higher doses (\u3e5 µg/ml) of marijuana derived cannabinoids (CBD, CBN or THC) inhibited the growth of P. gingivalis (p \u3c 0.001) and F. alocis (p \u3c 0.001), relative to unexposed bacteria, whereas T. denticola growth was resistant to all cannabinoid doses tested (1- 10 µg/ml, p \u3e 0.05).. TIGK cells were non-viable when exposed to high cannabinoid concentrations (\u3e 10 µg/ml).Sub-lethal (1 -5 µg/ml) doses of each cannabinoid subtype suppressed the production of IL-8 and IL-6 but enhanced IL-10 release in P. gingivalis or LPS stimulated TIGKs (all p \u3c 0.001). Treatment with the CB2 inhibitor, JTE907, did not rescue cannabinoid-induced immune suppression, suggesting that alternative cannabinoid receptors, such as CB1, GPR55 or A2A receptors, may be associated with the anti YLLL inflammatory function of marijuana-derived cannabinoids. If the phenomena of (i) cannabinoid induced epithelial toxicity; (ii) growth restriction of a sub-population of bacterial species in the oral biofilm; and (iii) innate suppression in gingival epithelial cells, established herein, occur in vivo, they are likely to help explain increased susceptibility to periodontal diseases in cannabis users. To expand, the epithelial barrier may be compromised in marijuana smokers, facilitating bacterial entry and more efficient diffusion of microbial toxins into the gingival tissues; marijuana-derived cannabinoids may promote microbial dysbiosis; and a suppressed pro-inflammatory epithelial response to bacterial stimuli may promote immune evasion, colonization and persistence by pathogenic bacteria. These data provide some of the first mechanistic insights into cannabinoid associated periodontal diseases. Such mechanistic advance are critical if novel therapeutics to prevent chronic periodontitis in marijuana users are to be developed. Further, an improved understanding of the immuneregulatory properties of cannabinoids will be useful in the development of therapeutics for the containment of inflammation, in general

The CombiUgi Project and Closing the Open Science Loop

This presentation combines 2 blog posts explaining the CombiUgi project and the predicted anti-tumor activity of a virtual library of 68,000 compounds

Covariate-Dependent Clustering of Undirected Networks with Brain-Imaging Data

This article focuses on model-based clustering of subjects based on the shared relationships of subject-specific networks and covariates in scenarios when there are differences in the relationship between networks and covariates for different groups of subjects. It is also of interest to identify the network nodes significantly associated with each covariate in each cluster of subjects. To address these methodological questions, we propose a novel nonparametric Bayesian mixture modeling framework with an undirected network response and scalar predictors. The symmetric matrix coefficients corresponding to the scalar predictors of interest in each mixture component involve low-rankness and group sparsity within the low-rank structure. While the low-rank structure in the network coefficients adds parsimony and computational efficiency, the group sparsity within the low-rank structure enables drawing inference on network nodes and cells significantly associated with each scalar predictor. Being a principled Bayesian mixture modeling framework, our approach allows model-based identification of the number of clusters, offers clustering uncertainty in terms of the co-clustering matrix and presents precise characterization of uncertainty in identifying network nodes significantly related to a predictor in each cluster. Empirical results in various simulation scenarios illustrate substantial inferential gains of the proposed framework in comparison with competitors. Analysis of a real brain connectome dataset using the proposed method provides interesting insights into the brain regions of interest (ROIs) significantly related to creative achievement in each cluster of subjects.NSF-DMS 2220840, NSF-DMS 221067

A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria.

BACKGROUND: Nearly half of the world's population (3.2 billion people) were at risk of malaria in 2015, and resistance to current therapies is a major concern. While the standard of care includes drug combinations, there is a pressing need to identify new combinations that can bypass current resistance mechanisms. In the work presented here, a combined transcriptional drug repositioning/discovery and machine learning approach is proposed. METHODS: The integrated approach utilizes gene expression data from patient-derived samples, in combination with large-scale anti-malarial combination screening data, to predict synergistic compound combinations for three Plasmodium falciparum strains (3D7, DD2 and HB3). Both single compounds and combinations predicted to be active were prospectively tested in experiment. RESULTS: One of the predicted single agents, apicidin, was active with the AC50 values of 74.9, 84.1 and 74.9 nM in 3D7, DD2 and HB3 P. falciparum strains while its maximal safe plasma concentration in human is 547.6 ± 136.6 nM. Apicidin at the safe dose of 500 nM kills on average 97% of the parasite. The synergy prediction algorithm exhibited overall precision and recall of 83.5 and 65.1% for mild-to-strong, 48.8 and 75.5% for moderate-to-strong and 12.0 and 62.7% for strong synergies. Some of the prospectively predicted combinations, such as tacrolimus-hydroxyzine and raloxifene-thioridazine, exhibited significant synergy across the three P. falciparum strains included in the study. CONCLUSIONS: Systematic approaches can play an important role in accelerating discovering novel combinational therapies for malaria as it enables selecting novel synergistic compound pairs in a more informed and cost-effective manner

Blue Obelisk - Interoperability in chemical informatics

The Blue Obelisk Movement (http://www.blueobelisk.org/) is the name used by a diverse Internet group promoting reusable chemistry via open source software development, consistent and complimentary chemoinformatics research, open data, and open standards. We outline recent examples of cooperation in the Blue Obelisk group:  a shared dictionary of algorithms and implementations in chemoinformatics algorithms drawing from our various software projects; a shared repository of chemoinformatics data including elemental properties, atomic radii, isotopes, atom typing rules, and so forth; and Web services for the platform-independent use of chemoinformatics programs

Open Notebook Science Challenge: Solubilities of Organic Compounds in Organic Solvents

This book contains the results of the Open Notebook Science Solubility Challenge. All experimental measurements are provided with a link to either the laboratory notebook page where the experiment was carried out or to a literature reference. The Challenge was sponsored by Submeta, Nature and Sigma-Aldrich

Open Notebook Science Challenge: Solubilities of Organic Compounds in Organic Solvents

This book contains the results of the Open Notebook Science Solubility Challenge. All experimental measurements are provided with a link to either the laboratory notebook page where the experiment was carried out or to a literature reference. The Challenge was sponsored by Submeta, Nature and Sigma-Aldrich