Drug Discovery - Key Publications
This is a sampling of the key publications in the area of Drug Discovery:
1. Automated Hypothesis Generation Based on Mining Scientific Literature, Proceeding: 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, New York, 2014
2. Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature. 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Sydney, NSW, Australia. August 10-13, 2015.
3. IBM WATSON USES ARTIFICIAL INTELLIGENCE TO SUGGEST ADDITIONAL PRION DOMAIN CONTAINING PROTEINS LINKED TO ALS, (2016) Sessions 1 - 11, Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 17:sup1, 1-80, DOI: 10.1080/21678421.2016.1231971
4. Applying IBM Watson cognitive computing to identify drugs with potential for treating L-DOPA-induced dyskinesia, (2017) 13th International Conference AD/PD™
5. S. Kang, P. Das, S.J. McGrane, A.J. Martin, T. Huynh, A.K. Royyuru, A.J. Taylor, P.G. Jones, R. Zhou, Molecular Recognition of Metabotropic Glutamate Receptor Type 1 (mGluR1): Synergistic Understanding with Free Energy Perturbation and Linear Response Modeling.; J. Phys. Chem. B. 118, 6393-6404 (2014).
6. R. Zhou, Ed., Molecular Modeling at the Atomic Scale: Methods and Applications in Quantitative Biology (CRC Press, Boca Raton, 2014).
7. R.P. Sheridan, V.N. Maiorov, M.K. Holloway, W.D. Cornell, Y.D. Gao; Drug-like density: a method of quantifying the “bindability” of a protein target based on a very large set of pockets and drug-like ligands from the Protein Data Bank; J. Chem. Inf. Model. 50, 2029-2040 (2010).
8. Comparison of topological, shape, and docking methods in virtual screening; G.B. McGaughey, R.P. Sheridan, C.I. Bayly, J.C. Culberson, C. Kreatsoulas, S. Lindsley, V. Maiorov, J.-F. Truchon, W.D. Cornell, ; J. Chem. Inf. Model. 47, 1504-1519 (2007).
9. Dey, S., Luo, H., Fokoue, A., Hu, J., Zhang, P. A methodology and system to identify chemical substructures that have significant association with adverse drug reactions. US Patent Application (YOR8-2016-2333).
10. Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P. Predicting drug-drug interactions through large-scale similarity-based link prediction. Extended Semantic Web Conference (ESWC) 2016:774-789.
11. Li, Y., Ryan, P., Wei, Y., Friedman, C. A method to combine signals from spontaneous reporting systems and observational healthcare data to detect adverse drug reactions. Drug Safety 2015, 38:895-908.
12. Determination of Cerebral Spinal Fluid A1-42 from plasma analytes: blood signature of Alzheimer’s risk. Poster presentation AD/PD 2017 Vienna, Australia March 29th – April 2nd
13. Faux, B. Fung, C. Schieber, B. GoudeyAyton S., Faux NG., Bush AI., CSF ferritin predicts preclinical cognitive decline in APOE-ε4 carriers JAMA Neurol. 2017 Jan 1;74(1):122-125.
15. S Ayton, NG Faux, AI Bush, Alzheimer’s Disease Neuroimaging Initiative; Ferritin levels in the cerebrospinal fluid predict Alzheimer’s disease outcomes and are regulated by APOE; Nature communications 6; 6760 (2015) doi:10.1038/ncomms7760
16. “Developments in the Dynamic Covalent Chemistries from the Reaction of Thiols with Hexahydrotriazines,” J. Am. Chem. Soc., 2015, 137 (45), 14248-14251.
17. “Fast and selective ring-opening polymerizations by alkoxides and thioureas,” Nature Chemistry, 2016, 8 (11), 1047.
18. “Highly potent antimicrobial polyionenes with rapid killing kinetics, skin biocompatibility and in vivo bactericidal activity,” Biomaterials, 2017, published on the web.
19. “Self-assembled, biodegradable magnetic resonance imaging agents: organic radical-functionalized diblock copolymers,” ACS Macro letters, 2017, 6,176.
20. “Organocatalytic Anticancer Drug Loading of Degradable Mixed Micelles via a Biomimetic Mechanism,” Macromolecules, 2016, 49(6), 2013.
21. “Developments in the Dynamic Covalent Chemistries from the Reaction of Thiols with Hexahydrotriazines,” J. Am. Chem. Soc., 2015, 137 (45), 14248-14251.
22. “Broad-Spectrum Anitimicrobial Star Polycarbonates Functionalized with Mannose for Targeting Bacteria Residing Inside Immune Cells,” Adv. Healthcare Mat., 2016, 5, 1272.
23. “Cooperative orthogonal macromolecular assemblies with broad spectrum antiviral activity, high selectivity and resistance mitigation,” Macromolecules., 2016, 49(7), 2618.
24. J. F. Cors, A. Stucki, and G. V. Kaigala, “Hydrodynamic thermal confinement: creating thermo-chemical microenvironments on surfaces,” Chemical Communications 52, 13035–13038, 2016.
25. D. P. Taylor, I. Zeaf, R. D. Lovchik, and G. V. Kaigala, “Centimeter-scale surface interactions using hydrodynamic flow confinements,” Langmuir 32(41), 10537–10544, 2016.
26. A.Oskooei, and G. V. Kaigala, “Deep-reaching hydrodynamic flow confinements (DR-HFC): µm-scale liquid localization for open surfaces with topographical variations, IEEE Trans. Biomedical Eng. 99, 2016.
27. A.Kashyap, J. Autebert, E. Delamarche and G. V. Kaigala,” Selective local lysis and sampling of live cells for nucleic acid analysis using a microfluidic probe,” Scientific Reports 6, 29579, 2016.
28. N. Ostromohov, M. Bercovici, and G. V. Kaigala, “Delivery of minimally dispersed liquid interfaces for sequential surface chemistry,” Lab Chip 16, 3015–3023, 2016.
29. J. Autebert, J. Cors, D. Taylor, and G. V. Kaigala, Convection-enhanced biopatterning with hydrodynamically confined nanoliter volumes of reagents, Anal. Chem. 88(6), 3235–3242, 2016.
30. G. V. Kaigala, R. D. Lovchik and E. Delamarche, “Microfluidics in the open space for local chemistries on biological interfaces,” Angewandte Chemie, 2012.
31. D. Huber, J. Autebert, and G. V. Kaigala, “Micro fluorescence in situ hybridization (µFISH) for spatially multiplexed analysis of a cell monolayer,” Biomedical Microdevices 18(40), 2016.
32. Kozloski, J. and Cecchi, G. (2010). A theory of loop formation and elimination by spike timing-dependent plasticity. Front Neural Circuits 4. doi:10.3389/fncir.2010.00007
33. Kozloski J. and Wagner J. (2011) An Ultrascalable Solution to Large-scale Neural Tissue Simulation. Front Neuroinform 5:1-21. doi:10.3389/fninf.2011.00015
34. Kozloski, J. (2016). Closed-loop brain model of neocortical information-based exchange. Front Neuroanat 10, 3. doi:10.3389/fnana.2016.00003
35. Memelli H., Torben-Nielsen B and Kozloski J. (2013) Self-referential forces are sufficient to explain different dendritic morphologies. Front Neuroinform. doi:10.3389/fninf.2013.00001
36. Ponzi, A. and Wickens, J. (2010). Sequentially switching cell assemblies in random inhibitory networks of spiking neurons in the striatum. J Neurosci 30. doi:10.1523/JNEUROSCI.5540-09.2010
37. Ponzi, A. and Wickens, J. (2012). Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network. Front Syst Neurosci 6, 6. doi:10.3389/fnsys.2012.00006
38. Ponzi, A. and Wickens, J. (2013). Optimal balance of the striatal medium spiny neuron network. PLoS Comp Biol 9. doi:10.1371/journal.pcbi.1002954
39. Rumbell T. H., Draguljić D., Yadav A., Hof P. R., Luebke J. I. and Weaver C. M. (2016) Automated evolutionary optimization of ion channel conductances and kinetics in models of young and aged rhesus monkey pyramidal neurons. Journal of Computational Neuroscience 41(1)65蟆. doi:10.1007/s10827-016-0605-9
40. Gurev, V*, Pathmanathan, P*, Fattebert, J-L, Wen, HF, Magerlein, J, Gray, RA, Richards, DF and Rice, JJ. A high-resolution computational model of the deforming human heart. Biomechanics & Modeling in Mechanobiology (online Jan. 1, 2015).
41. Richards, DF, Glosli, JN, Draeger, EW, Mirin, AA, Chan, B, Fattebert, J-L, Krauss, W, Oppelstrup, T, Bulter, CJ, Gunnels, JJ, Gurev, V, Kim, C, Magerlein, J, Reumann, M, Wen, HF, and Rice, JJ. Toward Real-Time Simulation of Cardiac Electrophysiology in a Human Heart at High Resolution. Computer Methods in Biomechanics and Biomedical Engineering 16(7):802-805 (2013).
42. Hoefen, R, Reumann, M, Goldenberg, I, Moss AJ, O-Jin, J, Gu, Y, Zareba, W, McNitt,, S Zareba, W, Jons, C, Kanters, JK, Platonov, PG, Shimizu, W, Wilde, AAM, Rice, JJ and Lopes, CM. In Silico Cardiac Risk Assessment of Long QT type 1 patients: clinical predictability of cardiac models. J Am Coll Cardiol. 60(21):2182-2191 (2012).
43. Jons C, O-Uchi J, Moss AJ, Reumann M, Rice JJ, Goldenberg I, Zareba W, Wilde AA, Shimizu W, Kanters JK, McNitt S, Hofman N, Robinson JL and Lopes CM. Use of Mutant-Specific Ion Channel Characteristics for Risk Stratification of Long QT Syndrome Patients, Sci Transl Med. 3(76):76ra28 (2011).