Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 12 of 25 entries
Sorted by: Best Match Show Resources per page
Bigger data, collaborative tools and the future of predictive drug discovery.

Journal of computer-aided molecular design

Ekins S, Clark AM, Swamidass SJ, Litterman N, Williams AJ.
PMID: 24943138
J Comput Aided Mol Des. 2014 Oct;28(10):997-1008. doi: 10.1007/s10822-014-9762-y. Epub 2014 Jun 19.

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service commercial offerings. These have transformed how we find molecule-related data and use...

Cite
Ekins S, Clark AM, Swamidass SJ, et al. Bigger data, collaborative tools and the future of predictive drug discovery. J Comput Aided Mol Des. 2014;28(10):997-1008doi: 10.1007/s10822-014-9762-y.
Ekins, S., Clark, A. M., Swamidass, S. J., Litterman, N., & Williams, A. J. (2014). Bigger data, collaborative tools and the future of predictive drug discovery. Journal of computer-aided molecular design, 28(10), 997-1008. https://doi.org/10.1007/s10822-014-9762-y
Ekins, Sean, et al. "Bigger data, collaborative tools and the future of predictive drug discovery." Journal of computer-aided molecular design vol. 28,10 (2014): 997-1008. doi: https://doi.org/10.1007/s10822-014-9762-y
Ekins S, Clark AM, Swamidass SJ, Litterman N, Williams AJ. Bigger data, collaborative tools and the future of predictive drug discovery. J Comput Aided Mol Des. 2014 Oct;28(10):997-1008. doi: 10.1007/s10822-014-9762-y. Epub 2014 Jun 19. PMID: 24943138; PMCID: PMC4198464.
Copy Download .nbib Format: NLM
Bioactivation of Isoxazole-Containing Bromodomain and Extra-Terminal Domain (BET) Inhibitors.

Metabolites

Flynn NR, Ward MD, Schleiff MA, Laurin CMC, Farmer R, Conway SJ, Boysen G, Swamidass SJ, Miller GP.
PMID: 34203690
Metabolites. 2021 Jun 15;11(6). doi: 10.3390/metabo11060390.

The 3,5-dimethylisoxazole motif has become a useful and popular acetyl-lysine mimic employed in isoxazole-containing bromodomain and extra-terminal (BET) inhibitors but may introduce the potential for bioactivations into toxic reactive metabolites. As a test, we coupled deep neural models for...

Cite
Flynn NR, Ward MD, Schleiff MA, et al. Bioactivation of Isoxazole-Containing Bromodomain and Extra-Terminal Domain (BET) Inhibitors. Metabolites. 2021;11(6)doi: 10.3390/metabo11060390.
Flynn, N. R., Ward, M. D., Schleiff, M. A., Laurin, C. M. C., Farmer, R., Conway, S. J., Boysen, G., Swamidass, S. J., & Miller, G. P. (2021). Bioactivation of Isoxazole-Containing Bromodomain and Extra-Terminal Domain (BET) Inhibitors. Metabolites, 11(6), . https://doi.org/10.3390/metabo11060390
Flynn, Noah R, et al. "Bioactivation of Isoxazole-Containing Bromodomain and Extra-Terminal Domain (BET) Inhibitors." Metabolites vol. 11,6 (2021). doi: https://doi.org/10.3390/metabo11060390
Flynn NR, Ward MD, Schleiff MA, Laurin CMC, Farmer R, Conway SJ, Boysen G, Swamidass SJ, Miller GP. Bioactivation of Isoxazole-Containing Bromodomain and Extra-Terminal Domain (BET) Inhibitors. Metabolites. 2021 Jun 15;11(6). doi: 10.3390/metabo11060390. PMID: 34203690; PMCID: PMC8232216.
Copy Download .nbib Format: NLM
Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites.

Journal of chemical information and modeling

Hughes TB, Dang NL, Kumar A, Flynn NR, Swamidass SJ.
PMID: 32881497
J Chem Inf Model. 2020 Oct 26;60(10):4702-4716. doi: 10.1021/acs.jcim.0c00360. Epub 2020 Sep 16.

Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug...

Cite
Hughes TB, Dang NL, Kumar A, et al. Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites. J Chem Inf Model. 2020;60(10):4702-4716doi: 10.1021/acs.jcim.0c00360.
Hughes, T. B., Dang, N. L., Kumar, A., Flynn, N. R., & Swamidass, S. J. (2020). Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites. Journal of chemical information and modeling, 60(10), 4702-4716. https://doi.org/10.1021/acs.jcim.0c00360
Hughes, Tyler B, et al. "Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites." Journal of chemical information and modeling vol. 60,10 (2020): 4702-4716. doi: https://doi.org/10.1021/acs.jcim.0c00360
Hughes TB, Dang NL, Kumar A, Flynn NR, Swamidass SJ. Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites. J Chem Inf Model. 2020 Oct 26;60(10):4702-4716. doi: 10.1021/acs.jcim.0c00360. Epub 2020 Sep 16. PMID: 32881497; PMCID: PMC8716321.
Copy Download .nbib Format: NLM
XenoNet: Inference and Likelihood of Intermediate Metabolite Formation.

Journal of chemical information and modeling

Flynn NR, Dang NL, Ward MD, Swamidass SJ.
PMID: 32525671
J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29.

Drug metabolism is a common cause of adverse drug reactions. Drug molecules can be metabolized into reactive metabolites, which can conjugate to biomolecules, like protein and DNA, in a process termed bioactivation. To mitigate adverse reactions caused by bioactivation,...

Cite
Flynn NR, Dang NL, Ward MD, et al. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020;60(7):3431-3449doi: 10.1021/acs.jcim.0c00361.
Flynn, N. R., Dang, N. L., Ward, M. D., & Swamidass, S. J. (2020). XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. Journal of chemical information and modeling, 60(7), 3431-3449. https://doi.org/10.1021/acs.jcim.0c00361
Flynn, Noah R, et al. "XenoNet: Inference and Likelihood of Intermediate Metabolite Formation." Journal of chemical information and modeling vol. 60,7 (2020): 3431-3449. doi: https://doi.org/10.1021/acs.jcim.0c00361
Flynn NR, Dang NL, Ward MD, Swamidass SJ. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29. PMID: 32525671.
Copy Download .nbib Format: NLM
Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network.

ACS central science

Hughes TB, Miller GP, Swamidass SJ.
PMID: 27162970
ACS Cent Sci. 2015 Jul 22;1(4):168-80. doi: 10.1021/acscentsci.5b00131. Epub 2015 Jun 09.

Drug toxicity is frequently caused by electrophilic reactive metabolites that covalently bind to proteins. Epoxides comprise a large class of three-membered cyclic ethers. These molecules are electrophilic and typically highly reactive due to ring tension and polarized carbon-oxygen bonds....

Cite
Hughes TB, Miller GP, Swamidass SJ. Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. ACS Cent Sci. 2015;1(4):168-80doi: 10.1021/acscentsci.5b00131.
Hughes, T. B., Miller, G. P., & Swamidass, S. J. (2015). Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. ACS central science, 1(4), 168-80. https://doi.org/10.1021/acscentsci.5b00131
Hughes, Tyler B, et al. "Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network." ACS central science vol. 1,4 (2015): 168-80. doi: https://doi.org/10.1021/acscentsci.5b00131
Hughes TB, Miller GP, Swamidass SJ. Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. ACS Cent Sci. 2015 Jul 22;1(4):168-80. doi: 10.1021/acscentsci.5b00131. Epub 2015 Jun 09. PMID: 27162970; PMCID: PMC4827534.
Copy Download .nbib Format: NLM
Erratum: Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity.

Scientific reports

Pan Y, Daito T, Sasaki Y, Chung YH, Xing X, Pondugula S, Swamidass SJ, Wang T, Kim AH, Yano H.
PMID: 27649847
Sci Rep. 2016 Sep 21;6:33766. doi: 10.1038/srep33766.

No abstract available.

Cite
Pan Y, Daito T, Sasaki Y, et al. Erratum: Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity. Sci Rep. 2016;6:33766doi: 10.1038/srep33766.
Pan, Y., Daito, T., Sasaki, Y., Chung, Y. H., Xing, X., Pondugula, S., Swamidass, S. J., Wang, T., Kim, A. H., & Yano, H. (2016). Erratum: Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity. Scientific reports, 633766. https://doi.org/10.1038/srep33766
Pan, Yanchun, et al. "Erratum: Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity." Scientific reports vol. 6 (2016): 33766. doi: https://doi.org/10.1038/srep33766
Pan Y, Daito T, Sasaki Y, Chung YH, Xing X, Pondugula S, Swamidass SJ, Wang T, Kim AH, Yano H. Erratum: Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity. Sci Rep. 2016 Sep 21;6:33766. doi: 10.1038/srep33766. PMID: 27649847; PMCID: PMC5030520.
Copy Download .nbib Format: NLM
Large scale study of multiple-molecule queries.

Journal of cheminformatics

Nasr RJ, Swamidass SJ, Baldi PF.
PMID: 20298525
J Cheminform. 2009 Jun 04;1(1):7. doi: 10.1186/1758-2946-1-7.

BACKGROUND: In ligand-based screening, as well as in other chemoinformatics applications, one seeks to effectively search large repositories of molecules in order to retrieve molecules that are similar typically to a single molecule lead. However, in some case, multiple...

Cite
Nasr RJ, Swamidass SJ, Baldi PF. Large scale study of multiple-molecule queries. J Cheminform. 2009;1(1):7doi: 10.1186/1758-2946-1-7.
Nasr, R. J., Swamidass, S. J., & Baldi, P. F. (2009). Large scale study of multiple-molecule queries. Journal of cheminformatics, 1(1), 7. https://doi.org/10.1186/1758-2946-1-7
Nasr, Ramzi J, et al. "Large scale study of multiple-molecule queries." Journal of cheminformatics vol. 1,1 (2009): 7. doi: https://doi.org/10.1186/1758-2946-1-7
Nasr RJ, Swamidass SJ, Baldi PF. Large scale study of multiple-molecule queries. J Cheminform. 2009 Jun 04;1(1):7. doi: 10.1186/1758-2946-1-7. PMID: 20298525; PMCID: PMC3225883.
Copy Download .nbib Format: NLM
XenoNet: Inference and Likelihood of Intermediate Metabolite Formation.

Journal of chemical information and modeling

Flynn NR, Dang NL, Ward MD, Swamidass SJ.
PMID: 32525671
J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29.

Drug metabolism is a common cause of adverse drug reactions. Drug molecules can be metabolized into reactive metabolites, which can conjugate to biomolecules, like protein and DNA, in a process termed bioactivation. To mitigate adverse reactions caused by bioactivation,...

Cite
Flynn NR, Dang NL, Ward MD, et al. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020;60(7):3431-3449doi: 10.1021/acs.jcim.0c00361.
Flynn, N. R., Dang, N. L., Ward, M. D., & Swamidass, S. J. (2020). XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. Journal of chemical information and modeling, 60(7), 3431-3449. https://doi.org/10.1021/acs.jcim.0c00361
Flynn, Noah R, et al. "XenoNet: Inference and Likelihood of Intermediate Metabolite Formation." Journal of chemical information and modeling vol. 60,7 (2020): 3431-3449. doi: https://doi.org/10.1021/acs.jcim.0c00361
Flynn NR, Dang NL, Ward MD, Swamidass SJ. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29. PMID: 32525671; PMCID: PMC8716322.
Copy Download .nbib Format: NLM
Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network.

ACS central science

Hughes TB, Dang NL, Miller GP, Swamidass SJ.
PMID: 27610414
ACS Cent Sci. 2016 Aug 24;2(8):529-37. doi: 10.1021/acscentsci.6b00162. Epub 2016 Jul 29.

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict...

Cite
Hughes TB, Dang NL, Miller GP, et al. Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network. ACS Cent Sci. 2016;2(8):529-37doi: 10.1021/acscentsci.6b00162.
Hughes, T. B., Dang, N. L., Miller, G. P., & Swamidass, S. J. (2016). Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network. ACS central science, 2(8), 529-37. https://doi.org/10.1021/acscentsci.6b00162
Hughes, Tyler B, et al. "Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network." ACS central science vol. 2,8 (2016): 529-37. doi: https://doi.org/10.1021/acscentsci.6b00162
Hughes TB, Dang NL, Miller GP, Swamidass SJ. Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network. ACS Cent Sci. 2016 Aug 24;2(8):529-37. doi: 10.1021/acscentsci.6b00162. Epub 2016 Jul 29. PMID: 27610414; PMCID: PMC4999971.
Copy Download .nbib Format: NLM
Deep Learning Coordinate-Free Quantum Chemistry.

The journal of physical chemistry. A

Matlock MK, Hoffman M, Dang NL, Folmsbee DL, Langkamp LA, Hutchison GR, Kumar N, Sarullo K, Swamidass SJ.
PMID: 34609871
J Phys Chem A. 2021 Oct 14;125(40):8978-8986. doi: 10.1021/acs.jpca.1c04462. Epub 2021 Oct 05.

Computing quantum chemical properties of small molecules and polymers can provide insights valuable into physicists, chemists, and biologists when designing new materials, catalysts, biological probes, and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of...

Cite
Matlock MK, Hoffman M, Dang NL, et al. Deep Learning Coordinate-Free Quantum Chemistry. J Phys Chem A. 2021;125(40):8978-8986doi: 10.1021/acs.jpca.1c04462.
Matlock, M. K., Hoffman, M., Dang, N. L., Folmsbee, D. L., Langkamp, L. A., Hutchison, G. R., Kumar, N., Sarullo, K., & Swamidass, S. J. (2021). Deep Learning Coordinate-Free Quantum Chemistry. The journal of physical chemistry. A, 125(40), 8978-8986. https://doi.org/10.1021/acs.jpca.1c04462
Matlock, Matthew K, et al. "Deep Learning Coordinate-Free Quantum Chemistry." The journal of physical chemistry. A vol. 125,40 (2021): 8978-8986. doi: https://doi.org/10.1021/acs.jpca.1c04462
Matlock MK, Hoffman M, Dang NL, Folmsbee DL, Langkamp LA, Hutchison GR, Kumar N, Sarullo K, Swamidass SJ. Deep Learning Coordinate-Free Quantum Chemistry. J Phys Chem A. 2021 Oct 14;125(40):8978-8986. doi: 10.1021/acs.jpca.1c04462. Epub 2021 Oct 05. PMID: 34609871.
Copy Download .nbib Format: NLM
Probabilistic Substructure Mining From Small-Molecule Screens.

Molecular informatics

Ranu S, Calhoun BT, Singh AK, Swamidass SJ.
PMID: 27467413
Mol Inform. 2011 Sep;30(9):809-15. doi: 10.1002/minf.201100058. Epub 2011 Aug 04.

Identifying the overrepresented substructures from a set of molecules with similar activity is a common task in chemical informatics. Existing substructure miners are deterministic, requiring the activity of all mined molecules to be known with high confidence. In contrast,...

Cite
Ranu S, Calhoun BT, Singh AK, et al. Probabilistic Substructure Mining From Small-Molecule Screens. Mol Inform. 2011;30(9):809-15doi: 10.1002/minf.201100058.
Ranu, S., Calhoun, B. T., Singh, A. K., & Swamidass, S. J. (2011). Probabilistic Substructure Mining From Small-Molecule Screens. Molecular informatics, 30(9), 809-15. https://doi.org/10.1002/minf.201100058
Ranu, Sayan, et al. "Probabilistic Substructure Mining From Small-Molecule Screens." Molecular informatics vol. 30,9 (2011): 809-15. doi: https://doi.org/10.1002/minf.201100058
Ranu S, Calhoun BT, Singh AK, Swamidass SJ. Probabilistic Substructure Mining From Small-Molecule Screens. Mol Inform. 2011 Sep;30(9):809-15. doi: 10.1002/minf.201100058. Epub 2011 Aug 04. PMID: 27467413.
Copy Download .nbib Format: NLM
Late Toxicity After Adjuvant Conventional Radiation Versus Image-Guided Intensity-Modulated Radiotherapy for Cervical Cancer (PARCER): A Randomized Controlled Trial.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology

Chopra S, Gupta S, Kannan S, Dora T, Engineer R, Mangaj A, Maheshwari A, Shylasree TS, Ghosh J, Paul SN, Phurailatpam R, Charnalia M, Alone M, Swamidas J, Mahantshetty U, Deodhar K, Kerkar R, Shrivastava SK.
PMID: 34506246
J Clin Oncol. 2021 Nov 20;39(33):3682-3692. doi: 10.1200/JCO.20.02530. Epub 2021 Sep 10.

PURPOSE: Postoperative Adjuvant Radiation in Cervical Cancer (PARCER), a phase III randomized trial, compared late toxicity after image-guided intensity-modulated radiotherapy (IG-IMRT) with three-dimensional conformal radiation therapy (3D-CRT) in women with cervical cancer undergoing postoperative radiation.METHODS: Patients were randomly assigned...

Cite
Chopra S, Gupta S, Kannan S, et al. Late Toxicity After Adjuvant Conventional Radiation Versus Image-Guided Intensity-Modulated Radiotherapy for Cervical Cancer (PARCER): A Randomized Controlled Trial. J Clin Oncol. 2021;39(33):3682-3692doi: 10.1200/JCO.20.02530.
Chopra, S., Gupta, S., Kannan, S., Dora, T., Engineer, R., Mangaj, A., Maheshwari, A., Shylasree, T. S., Ghosh, J., Paul, S. N., Phurailatpam, R., Charnalia, M., Alone, M., Swamidas, J., Mahantshetty, U., Deodhar, K., Kerkar, R., & Shrivastava, S. K. (2021). Late Toxicity After Adjuvant Conventional Radiation Versus Image-Guided Intensity-Modulated Radiotherapy for Cervical Cancer (PARCER): A Randomized Controlled Trial. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 39(33), 3682-3692. https://doi.org/10.1200/JCO.20.02530
Chopra, Supriya, et al. "Late Toxicity After Adjuvant Conventional Radiation Versus Image-Guided Intensity-Modulated Radiotherapy for Cervical Cancer (PARCER): A Randomized Controlled Trial." Journal of clinical oncology : official journal of the American Society of Clinical Oncology vol. 39,33 (2021): 3682-3692. doi: https://doi.org/10.1200/JCO.20.02530
Chopra S, Gupta S, Kannan S, Dora T, Engineer R, Mangaj A, Maheshwari A, Shylasree TS, Ghosh J, Paul SN, Phurailatpam R, Charnalia M, Alone M, Swamidas J, Mahantshetty U, Deodhar K, Kerkar R, Shrivastava SK. Late Toxicity After Adjuvant Conventional Radiation Versus Image-Guided Intensity-Modulated Radiotherapy for Cervical Cancer (PARCER): A Randomized Controlled Trial. J Clin Oncol. 2021 Nov 20;39(33):3682-3692. doi: 10.1200/JCO.20.02530. Epub 2021 Sep 10. PMID: 34506246.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 25 entries