@article{ash_kuenemann_rotroff_motsinger-reif_fourches_2019, title={Cheminformatics approach to exploring and modeling trait-associated metabolite profiles}, volume={11}, ISSN={["1758-2946"]}, DOI={10.1186/s13321-019-0366-3}, abstractNote={Developing predictive and transparent approaches to the analysis of metabolite profiles across patient cohorts is of critical importance for understanding the events that trigger or modulate traits of interest (e.g., disease progression, drug metabolism, chemical risk assessment). However, metabolites’ chemical structures are still rarely used in the statistical modeling workflows that establish these trait-metabolite relationships. Herein, we present a novel cheminformatics-based approach capable of identifying predictive, interpretable, and reproducible trait-metabolite relationships. As a proof-of-concept, we utilize a previously published case study consisting of metabolite profiles from non-small-cell lung cancer (NSCLC) adenocarcinoma patients and healthy controls. By characterizing each structurally annotated metabolite using both computed molecular descriptors and patient metabolite concentration profiles, we show that these complementary features enhance the identification and understanding of key metabolites associated with cancer. Ultimately, we built multi-metabolite classification models for assessing patients’ cancer status using specific groups of metabolites identified based on high structural similarity through chemical clustering. We subsequently performed a metabolic pathway enrichment analysis to identify potential mechanistic relationships between metabolites and NSCLC adenocarcinoma. This cheminformatics-inspired approach relies on the metabolites’ structural features and chemical properties to provide critical information about metabolite-trait associations. This method could ultimately facilitate biological understanding and advance research based on metabolomics data, especially with respect to the identification of novel biomarkers.}, journal={JOURNAL OF CHEMINFORMATICS}, author={Ash, Jeremy R. and Kuenemann, Melaine A. and Rotroff, Daniel and Motsinger-Reif, Alison and Fourches, Denis}, year={2019}, month={Jun} }
 @article{menden_wang_mason_szalai_bulusu_guan_yu_kang_jeon_wolfinger_et al._2019, title={Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen}, volume={10}, ISSN={["2041-1723"]}, DOI={10.1038/s41467-019-09799-2}, abstractNote={Abstract}, journal={NATURE COMMUNICATIONS}, author={Menden, Michael P. and Wang, Dennis and Mason, Mike J. and Szalai, Bence and Bulusu, Krishna C. and Guan, Yuanfang and Yu, Thomas and Kang, Jaewoo and Jeon, Minji and Wolfinger, Russ and et al.}, year={2019}, month={Jun} }
 @article{west_lu_rotroff_kuenemann_chang_wu_wagner_buse_motsinger-reif_fourches_et al._2019, title={Identifying individual risk rare variants using protein structure guided local tests (POINT)}, volume={15}, ISSN={["1553-7358"]}, DOI={10.1371/journal.pcbi.1006722}, abstractNote={Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a protein structure guided local test (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9, ANGPTL4 and CETP in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data.}, number={2}, journal={PLOS COMPUTATIONAL BIOLOGY}, author={West, Rachel Marceau and Lu, Wenbin and Rotroff, Daniel M. and Kuenemann, Melaine A. and Chang, Sheng-Mao and Wu, Michael C. and Wagner, Michael J. and Buse, John B. and Motsinger-Reif, Alison A. and Fourches, Denis and et al.}, year={2019}, month={Feb} }
 @article{kuenemann_fourches_2018, title={Cheminformatics analysis of dynamic WNK-inhibitor interactions}, volume={37}, number={6-7}, journal={Molecular Informatics}, author={Kuenemann, M. A. and Fourches, D.}, year={2018} }
 @article{mahapatra_franzosa_roell_kuenemann_houck_reif_fourches_kullman_2018, title={Confirmation of high-throughput screening data and novel mechanistic insights into VDR-xenobiotic interactions by orthogonal assays}, volume={8}, journal={Scientific Reports}, author={Mahapatra, D. and Franzosa, J. A. and Roell, K. and Kuenemann, M. A. and Houck, K. A. and Reif, D. M. and Fourches, D. and Kullman, S. W.}, year={2018} }
 @article{kuenemann_spears_orndorff_fourches_2018, title={In silico predicted glucose-1-phosphate Uridylyltransferase (GalU) inhibitors block a key pathway required for Listeria virulence}, volume={37}, number={6-7}, journal={Molecular Informatics}, author={Kuenemann, M. A. and Spears, P. A. and Orndorff, P. E. and Fourches, D.}, year={2018} }
 @article{williams_kuenemann_driessche_williams_fourches_freeman_2018, title={Toward the Rational Design of Sustainable Hair Dyes Using Cheminformatics Approaches: Step 1. Database Development and Analysis}, volume={6}, ISSN={["2168-0485"]}, url={https://doi.org/10.1021/acssuschemeng.7b03795}, DOI={10.1021/acssuschemeng.7b03795}, abstractNote={Herein, we report on the initial step of the design process of new hair dyes with the desired properties. The first step is dedicated to the development of the largest, publicly available database of hair dye substances (containing temporary and semipermanent hair dyes as well as permanent hair dye precursors) used in commercial hair dye formulations. The database was utilized to perform a cheminformatics study assessing the computed physicochemical properties of the different hair dye substances, especially within each cluster of structurally similar dyes. The various substances could be differentiated based on their average molecular weight, hydrophobicity, topological polar surface area, and number of hydrogen bond acceptors, with some overlap also observed. In particular, we found that dyes such as C.I. Basic Orange 1 and 2 were clustered among the precursors, suggesting that their diffusion behavior is similar to that of permanent hair dye precursors. We anticipate taking advantage of this interestin...}, number={2}, journal={ACS SUSTAINABLE CHEMISTRY & ENGINEERING}, publisher={American Chemical Society (ACS)}, author={Williams, Tova N. and Kuenemann, Melaine A. and Driessche, George A. and Williams, Antony J. and Fourches, Denis and Freeman, Harold S.}, year={2018}, month={Feb}, pages={2344–2352} }
 @article{kuenemann_fourches_2017, title={Cheminformatics modeling of amine solutions for assessing their CO2 absorption properties}, volume={36}, number={7}, journal={Molecular Informatics}, author={Kuenemann, M. A. and Fourches, D.}, year={2017} }
 @article{kuenemann_szymczyk_chen_sultana_hinks_freeman_williams_fourches_vinueza_2017, title={Weaver's historic accessible collection of synthetic dyes: a cheminformatics analysis}, volume={8}, ISSN={["2041-6539"]}, DOI={10.1039/c7sc00567a}, abstractNote={The Max Weaver Dye Library is presented to the scientific community with a cheminformatics approach to enhance research opportunities with this unique collection of ∼98 000 vials of custom-made dyes.}, number={6}, journal={CHEMICAL SCIENCE}, author={Kuenemann, Melaine A. and Szymczyk, Malgorzata and Chen, Yufei and Sultana, Nadia and Hinks, David and Freeman, Harold S. and Williams, Antony J. and Fourches, Denis and Vinueza, Nelson R.}, year={2017}, month={Jun}, pages={4334–4339} }