A5074498235- Computational Drug Discovery Methods
- Bioinformatics and Genomic Networks
- Protein Degradation and Inhibitors
- Machine Learning in Materials Science
- Biomedical Text Mining and Ontologies
- Animal testing and alternatives
- Advanced Biosensing Techniques and Applications
- Metabolomics and Mass Spectrometry Studies
- Genetics, Bioinformatics, and Biomedical Research
- Contact Dermatitis and Allergies
- Dermatology and Skin Diseases
- Genomics and Rare Diseases
- Biosimilars and Bioanalytical Methods
- Photoreceptor and optogenetics research
- Click Chemistry and Applications
- Medical Imaging Techniques and Applications
- Cell Image Analysis Techniques
- Gastrointestinal Tumor Research and Treatment
- Synthesis and Reactivity of Heterocycles
- Chemistry and Chemical Engineering
- Cholinesterase and Neurodegenerative Diseases
- Semantic Web and Ontologies
- Synthesis and biological activity
- 3D Printing in Biomedical Research
- Quinazolinone synthesis and applications
University of North Carolina at Chapel Hill
2015-2024
University of North Carolina Health Care
2023
Communities In Schools of Orange County
2016
Institute of Medicinal Plant Development
2016
Fox Chase Cancer Center
2015
Saint Joseph's University
2012
Abstract The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number possible combinations vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large combination dataset, consisting 11,576 experiments from 910 across 85 molecularly characterized cell lines, and results a DREAM Challenge evaluate computational strategies for...
The use of substructural alerts to identify Pan-Assay INterference compoundS (PAINS) has become a common component the triage process in biological screening campaigns. These alerts, however, were originally derived from proprietary library tested just six assays measuring protein–protein interaction (PPI) inhibition using AlphaScreen detection technology only; moreover, 68% (328 out 480 alerts) four or fewer compounds. In an effort assess reliability these as indicators pan-assay...
Protein kinases are highly tractable targets for drug discovery. However, the biological function and therapeutic potential of majority 500+ human protein remains unknown. We have developed physical virtual collections small molecule inhibitors, which we call chemogenomic sets, that designed to inhibit catalytic almost half kinases. In this manuscript share our progress towards generation a comprehensive kinase set (KCGS), release kinome profiling data large inhibitor (Published Kinase...
Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple transparent means to flag potential hazards or group compounds into categories for read-across. However, there has been growing concern that disproportionally too many chemicals toxic, which questions their reliability toxicity markers. Conversely, the rigorously developed properly validated statistical QSAR models can accurately reliably predict of chemical; however, use hampered by lack...
la diffusion de documents scientifiques niveau recherche, publiés ou non, émanant des établissements d'enseignement et recherche français étrangers, laboratoires publics privés.
Abstract Despite decades of intensive search for compounds that modulate the activity particular protein targets, a large proportion human kinome remains as yet undrugged. Effective approaches are therefore required to map massive space unexplored compound–kinase interactions novel and potent activities. Here, we carry out crowdsourced benchmarking predictive algorithms kinase inhibitor potencies across multiple families tested on unpublished bioactivity data. We find top-performing...
Deep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties. Many deep learning approaches employ reinforcement optimizing the target properties generated molecules. However, success this approach is often hampered by problem sparse rewards as majority are expectedly predicted inactives. We propose several technical innovations to address and improve balance between exploration exploitation modes learning. In...
The ability to determine which environmental chemicals pose the greatest potential threats human health remains one of major concerns in regulatory toxicology. Computation methods that can accurately predict chemicals' toxic silico are increasingly sought-after replace vitro high-throughput screening (HTS) as well controversial and costly vivo animal studies. To this end, we have built Quantitative Structure-Activity Relationship (QSAR) models twelve (12) stress response nuclear receptor...
We describe SGC-GAK-1 (11), a potent, selective, and cell-active inhibitor of cyclin G-associated kinase (GAK), together with structurally related negative control SGC-GAK-1N (14). 11 was highly selective in an vitro kinome-wide screen, but cellular engagement assays defined RIPK2 as collateral target. identified 18 potent lacking GAK activity. Together, this chemical probe set can be used to interrogate biology.
An example of structural transformation human skin sensitizers into various non-sensitizers based on interpretation QSAR models.
The enormous increase in the amount of publicly available chemical genomics data and growing emphasis on sharing open science mandates that cheminformaticians also make their models for broad use by scientific community. Chembench is one first accessible, integrated cheminformatics Web portals. It has been extensively used researchers from different fields curation, visualization, analysis, modeling chemogenomics data. Since its launch 2008, accessed more than 1 million times 5000 users a...
Elucidation of the mechanistic relationships between drugs, their targets, and diseases is at core modern drug discovery research. Thousands studies relevant to drug–target–disease (DTD) triangle have been published annotated in Medline/PubMed database. Mining this database affords rapid identification all that confirm connections vertices or enable new inferences such connections. To end, we describe development Chemotext, a publicly available Web server mines entire compendium literature...
Multiple approaches to quantitative structure–activity relationship (QSAR) modeling using various statistical or machine learning techniques and different types of chemical descriptors have been developed over the years. Oftentimes models are used in consensus make more accurate predictions at expense model interpretation. We propose a simple, fast, reliable method termed Multi-Descriptor Read Across (MuDRA) for developing both interpretable models. The is conceptually related well-known kNN...
Traditionally, the skin sensitization potential of chemicals has been assessed using animal models. Due to growing ethical, political, and financial concerns, sustainable alternatives testing need be developed. As publicly available data continues grow, computational approaches, such as alert-based systems, read-across, QSAR models, are expected reduce or replace for prediction human potential. Herein, we discuss current approaches predicting provide future perspectives field. a...
Abstract Chordoma is a devastating rare cancer that affects one in million people. With mean-survival of just 6 years and no approved medicines, the primary treatments are surgery radiation. In order to speed new medicines chordoma patients, drug repurposing strategy represents an attractive approach. Drugs have already advanced through human clinical safety trials potential be more quickly than de novo discovered on targets. We taken two strategies enable this: (1) generated validated...
The Ebola virus (EBOV) causes severe human infection that lacks effective treatment. A recent screen identified a series of compounds block EBOV-like particle entry into cells. Using data from this screen, quantitative structure–activity relationship models were built and employed for virtual screening ∼17 million compound library. Experimental testing 102 hits yielded 14 with IC50 values under 10 μM, including several sub-micromolar inhibitors, more than 10-fold selectivity against host...
Approximately 10–15 % of gastrointestinal stromal tumors (GISTs) lack gain function mutations in the KIT and platelet-derived growth factor receptor alpha (PDGFRA) genes. An alternate mechanism oncogenesis through loss succinate-dehydrogenase (SDH) enzyme complex has been identified for a subset these "wild type" GISTs. Paired tumor normal DNA from an SDH-intact wild-type GIST case was subjected to whole exome sequencing identify pathogenic mechanism(s) this tumor. Selected findings were...
Small, colloidally aggregating molecules (SCAMs) are the most common source of false positives in high-throughput screening (HTS) campaigns. Although SCAMs can be experimentally detected and suppressed by addition detergent assay buffer, sensitivity is not routinely monitored HTS. Computational methods thus needed to flag potential during HTS triage. In this study, we have developed rigorously validated quantitative structure-interference relationship (QSIR) models detergent-sensitive...
Abstract Protein kinases are highly tractable targets for drug discovery. However, the biological function and therapeutic potential of majority 500+ human protein remains unknown. We have developed physical virtual collections small molecule inhibitors, which we call chemogenomic sets, that designed to inhibit catalytic almost half kinases. In this manuscript share our progress towards generation a comprehensive kinase set (KCGS), release kinome profiling data large inhibitor (Published...