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Zoe L Sessions

Bio: Zoe L Sessions is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Medicine & In silico. The author has an hindex of 2, co-authored 3 publications receiving 14 citations.

Papers
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Journal ArticleDOI
TL;DR: The advances in computational approaches to drug discovery of small molecules with epigenetic modulation profiles are reviewed, the current chemogenomics data available for epigenetics targets are summarized, and a perspective on the greater utility of biomedical knowledge mining as a means to advance the epigenetic drug discovery is provided.

22 citations

Journal ArticleDOI
TL;DR: In this article, a review of the development and application of the simplex approach for the solution of various QSAR/QSPR problems is presented, including the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment topography identification.
Abstract: We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment “topography” identification.

9 citations

Journal ArticleDOI
TL;DR: In this paper , the authors explored conserved binding sites in the key coronavirus proteins for the development of broad-spectrum direct acting anti-coronaviral compounds and validated the significance of this conservation for drug discovery with existing experimental data.

5 citations

Journal ArticleDOI
TL;DR: In this paper, the authors build a dataset of all the secondary metabolites isolated within the Annonaceae family and perform the respective chemotaxonomic analysis using self-organizing maps (SOMs).
Abstract: The Annonaceae family of plants is one of the most anatomically and structurally uniform families. Chemotaxonomy is a common practice to determine the chemical patterns within these families at different phylogenetic levels. The aim of this study was to build a dataset of all the secondary metabolites isolated within the Annonaceae family and to perform the respective chemotaxonomic analysis using self-organizing maps (SOMs). This dataset is composed of 5321botanical occurrences and 1860 unique molecules present in all subfamilies of the Annonaceae. Diterpenes account for 366 unique compounds and 533 botanical occurrences seen in both Annonoideae and Malmeoideae subfamilies. The Annoneae, Xylopieae and Miliuseae tribes had the highest number of botanical occurrences and were therefore selected for the analysis. Molecular descriptors of the diterpenes and their respective botanical occurrences were used to generate the SOMs. These SOMs demonstrated clear and indicative tribe separations, with a match rate higher than 70%. Our results corroborate with the morphological and molecular data. These models can be used to predict the phylogenetic location of certain diterpenes and to accelerate the research of Annonaceae secondary metabolites and their biological potentials.

4 citations

Posted ContentDOI
16 Mar 2022-bioRxiv
TL;DR: Five compounds that bind the human ACE2 protein can interrupt SARS-CoV-2 replication without damaging ACE2’s natural enzymatic function, and serve as a strong starting point for both development of acute treatments for COVID-19 and research into the mechanism of infection.
Abstract: The COVID-19 pandemic has had enormous health, economic, and social consequences. Vaccines have been successful in reducing rates of infection and hospitalization, but there is still a need for an acute treatment for the disease. We investigate whether compounds that bind the human ACE2 protein can interrupt SARS-CoV-2 replication without damaging ACE2’s natural enzymatic function. Initial compounds were screened for binding to ACE2 but little interruption of ACE2 enzymatic activity. This set of compounds was extended by application of quantitative structure-activity analysis, which resulted in 512 virtual hits for further confirmatory screening. A subsequent SARS-CoV-2 replication assay revealed that five of these compounds inhibit SARS-CoV-2 replication in human cells. Further effort is required to completely determine the antiviral mechanism of these compounds, but they serve as a strong starting point for both development of acute treatments for COVID-19 and research into the mechanism of infection. TOC Graphic: Overall study design.

2 citations


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Journal ArticleDOI
TL;DR: The enzymatic activity, drug inhibition, and structure of OMpro are characterized while evaluating the past and future implications of Mpro mutations, and PAXLOVID, a ritonavir-boosted formulation of nirmatrelvir, is issued.

65 citations

Journal ArticleDOI
TL;DR: The Deep Docking (DD) platform as discussed by the authors enables up to 100-fold acceleration of structure-based virtual screening by docking only a subset of a chemical library, iteratively synchronized with a ligand-based prediction of the remaining docking scores.
Abstract: With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking only a subset of a chemical library, iteratively synchronized with a ligand-based prediction of the remaining docking scores. This method results in hundreds- to thousands-fold virtual hit enrichment (without significant loss of potential drug candidates) and hence enables the screening of billion molecule-sized chemical libraries without using extraordinary computational resources. Herein, we present and discuss the generalized DD protocol that has been proven successful in various computer-aided drug discovery (CADD) campaigns and can be applied in conjunction with any conventional docking program. The protocol encompasses eight consecutive stages: molecular library preparation, receptor preparation, random sampling of a library, ligand preparation, molecular docking, model training, model inference and the residual docking. The standard DD workflow enables iterative application of stages 3-7 with continuous augmentation of the training set, and the number of such iterations can be adjusted by the user. A predefined recall value allows for control of the percentage of top-scoring molecules that are retained by DD and can be adjusted to control the library size reduction. The procedure takes 1-2 weeks (depending on the available resources) and can be completely automated on computing clusters managed by job schedulers. This open-source protocol, at https://github.com/jamesgleave/DD_protocol , can be readily deployed by CADD researchers and can significantly accelerate the effective exploration of ultra-large portions of a chemical space.

50 citations

Journal ArticleDOI
TL;DR: In this paper , the authors discuss the current findings in the development of small molecules for COVID-19 treatment, including their detailed mechanism of action, chemical structures, and preclinical and clinical efficacies.
Abstract: Abstract The outbreak of COVID-19 has become a global crisis, and brought severe disruptions to societies and economies. Until now, effective therapeutics against COVID-19 are in high demand. Along with our improved understanding of the structure, function, and pathogenic process of SARS-CoV-2, many small molecules with potential anti-COVID-19 effects have been developed. So far, several antiviral strategies were explored. Besides directly inhibition of viral proteins such as RdRp and M pro , interference of host enzymes including ACE2 and proteases, and blocking relevant immunoregulatory pathways represented by JAK/STAT, BTK, NF-κB, and NLRP3 pathways, are regarded feasible in drug development. The development of small molecules to treat COVID-19 has been achieved by several strategies, including computer-aided lead compound design and screening, natural product discovery, drug repurposing, and combination therapy. Several small molecules representative by remdesivir and paxlovid have been proved or authorized emergency use in many countries. And many candidates have entered clinical-trial stage. Nevertheless, due to the epidemiological features and variability issues of SARS-CoV-2, it is necessary to continue exploring novel strategies against COVID-19. This review discusses the current findings in the development of small molecules for COVID-19 treatment. Moreover, their detailed mechanism of action, chemical structures, and preclinical and clinical efficacies are discussed.

16 citations

Journal ArticleDOI
TL;DR: In this article, a large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity was conducted. And the results indicated that the models reported herein have considerable potential to identify small molecules with epigenetics activity.
Abstract: Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents many structure-activity relationships that have not been exploited thus far to develop predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. We built predictive models with high accuracy for small molecules' epigenetic target profiling through a systematic comparison of the machine learning models trained on different molecular fingerprints. The models were thoroughly validated, showing mean precisions of up to 0.952 for the epigenetic target prediction task. Our results indicate that the models reported herein have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as a freely accessible web application.

16 citations

Journal ArticleDOI
TL;DR: The Epigenetic Target Profiler (ETP) as discussed by the authors uses a consensus model based on two binary classification models for each target, relying on support vector machines and built on molecular fingerprints of different design.
Abstract: The identification of protein targets of small molecules is essential for drug discovery. With the increasing amount of chemogenomic data in the public domain, multiple ligand-based models for target prediction have emerged. However, these models are generally biased by the number of known ligands for different targets, which involves an under-representation of epigenetic targets, and despite the increasing importance of epigenetic targets in drug discovery, there are no open tools for epigenetic target prediction. In this work, we introduce Epigenetic Target Profiler (ETP), a freely accessible and easy-to-use web application for the prediction of epigenetic targets of small molecules. For a query compound, ETP predicts its bioactivity profile over a panel of 55 different epigenetic targets. To that aim, ETP uses a consensus model based on two binary classification models for each target, relying on support vector machines and built on molecular fingerprints of different design. A distance-to-model parameter related to the reliability of the predictions is included to facilitate their interpretability and assist in the identification of small molecules with potential epigenetic activity. Epigenetic Target Profiler is freely available at http://www.epigenetictargetprofiler.com.

12 citations