| 1. |
- Mansouri, Kamel, et al.
(författare)
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CoMPARA : Collaborative Modeling Project for Androgen Receptor Activity
- 2020
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Ingår i: Journal of Environmental Health Perspectives. - 0091-6765 .- 1552-9924. ; 128:2, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast (TM) metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast (TM)/Tox21 HTS in vitro assays.RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of similar to 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment.
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| 2. |
- Muratov, E. N., et al.
(författare)
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QSAR without borders
- 2020
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Ingår i: Chemical Society Reviews. - : Royal Society of Chemistry (RSC). - 0306-0012 .- 1460-4744. ; 49:11, s. 3525-3564
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Tidskriftsartikel (refereegranskat)abstract
- Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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| 3. |
- Varfolomeyev, S, et al.
(författare)
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Postgenomic chemistry (IUPAC Technical Report)
- 2005
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Ingår i: Pure and Applied Chemistry. - : Walter de Gruyter GmbH. - 0033-4545 .- 1365-3075. ; 77:9, s. 1641-1654
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Tidskriftsartikel (refereegranskat)abstract
- Numerous areas of chemistry can benefit from the ongoing genomic revolution. Here, we discuss and highlight trends in chemistry in the postgenomic era. The areas of interest include combinatorial approaches in organic chemistry; design and analysis of proteins containing unnatural amino acids; trace element-containing proteins; design and characterization of new enzyme types; applications of postgenomic chemistry in drug design; identification of lipid networks and global characterization of lipid molecular species; development of recombinant and self-proliferating polymers; and applications in food chemistry and bioanalytical chemistry based on new nanoanalytical systems and novel recognition elements.
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