Search

Your search keyword '"S. Segler"' showing total 43 results
Start Over Author "S. Segler" Publication Year Range Last 10 years
43 results on '"S. Segler"'

4. Generative Active Learning for the Search of Small-molecule Protein Binders.

Author

Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, and Yoshua Bengio

Published
2024
Full Text
View/download PDF

25. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

Author

Marwin H. S. Segler, Thierry Kogej, Christian Tyrchan, and Mark P. Waller

Subjects
Chemistry, QD1-999
Abstract

In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria), it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.

Published
2017
Full Text
View/download PDF

27. Predicting Electron Paths.

Author

John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, and José Miguel Hernández-Lobato

Published
2018

32. Machine learning the ropes: principles, applications and directions in synthetic chemistry

Author

Felix Strieth-Kalthoff, Frank Glorius, Frederik Sandfort, and Marwin H. S. Segler

Subjects
Workflow, Property (programming), Computer science, business.industry, Perspective (graphical), Context (language use), General Chemistry, Artificial intelligence, Machine learning, computer.software_genre, business, computer, Organic molecules
Abstract

Machine learning (ML) has emerged as a general, problem-solving paradigm with many applications in computer vision, natural language processing, digital safety, or medicine. By recognizing complex patterns in data, ML bears the potential to modernise the way how many chemical challenges are approached. In this review, an introduction to ML is given from the perspective of synthetic chemistry: starting from the fundamentals regarding algorithms and best-practice workflows, the review covers different applications of machine learning in synthesis planning, property prediction, molecular design, and reactivity prediction. In particular, different approaches of representing and utilizing organic molecules will be discussed - providing synthetic chemists both with the understanding and the tools required to apply machine learning in the context of their research, and pointers for further studying.

Published
2020
Full Text
View/download PDF

33. GuacaMol: Benchmarking Models for de Novo Molecular Design

Author

Marco Fiscato, Alain C. Vaucher, Nathan Brown, and Marwin H. S. Segler

Subjects
Models, Molecular, FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, General Chemical Engineering, media_common.quotation_subject, Quantitative Structure-Activity Relationship, FOS: Physical sciences, Fidelity, Library and Information Sciences, Machine learning, computer.software_genre, Quantitative Biology - Quantitative Methods, 01 natural sciences, Machine Learning (cs.LG), Deep Learning, Isomerism, Physics - Chemical Physics, 0103 physical sciences, Quantitative Methods (q-bio.QM), media_common, computer.programming_language, Chemical Physics (physics.chem-ph), Molecular Structure, 010304 chemical physics, Artificial neural network, business.industry, Suite, Deep learning, Biomolecules (q-bio.BM), General Chemistry, Benchmarking, Python (programming language), Chemical space, 0104 chemical sciences, Computer Science Applications, 010404 medicinal & biomolecular chemistry, Pharmaceutical Preparations, Quantitative Biology - Biomolecules, Drug Design, FOS: Biological sciences, Benchmark (computing), Artificial intelligence, business, Monte Carlo Method, computer
Abstract

De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in many application areas, models for molecular design based on neural networks appeared recently and show promising results. However, the new models have not been profiled on consistent tasks, and comparative studies to well-established algorithms have only seldom been performed. To standardize the assessment of both classical and neural models for de novo molecular design, we propose an evaluation framework, GuacaMol, based on a suite of standardized benchmarks. The benchmark tasks encompass measuring the fidelity of the models to reproduce the property distribution of the training sets, the ability to generate novel molecules, the exploration and exploitation of chemical space, and a variety of single and multiobjective optimization tasks. The benchmarking open-source Python code and a leaderboard can be found on https://benevolent.ai/guacamol .

Published
2019
Full Text
View/download PDF

34. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

Author

Thierry Kogej, Mark P. Waller, Christian Tyrchan, and Marwin H. S. Segler

Subjects
0301 basic medicine, Drug discovery, Computer science, General Chemical Engineering, General Chemistry, Computational biology, Bioinformatics, lcsh:Chemistry, 03 medical and health sciences, 030104 developmental biology, Recurrent neural network, lcsh:QD1-999, Biological target, Molecule, Language model, Design cycle, Function (biology), Research Article
Abstract

In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria), it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery., Using artificial neural networks, computers can learn to generate molecules with desired target properties. This can aid in the creative process of drug design.

Published
2017

35. Modelling Chemical Reasoning to Predict and Invent Reactions

Author

Marwin H. S. Segler and Mark P. Waller

Subjects
Theoretical computer science, 010405 organic chemistry, Chemistry, Organic Chemistry, Binary number, General Chemistry, 010402 general chemistry, computer.software_genre, 01 natural sciences, Chemical reaction, Local structure, Catalysis, Expert system, 0104 chemical sciences, Time frame, Knowledge graph, Graph (abstract data type), computer
Abstract

The ability to reason beyond established knowledge allows organic chemists to solve synthetic problems and invent novel transformations. Herein, we propose a model that mimics chemical reasoning, and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180 000 randomly selected binary reactions. The data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-)discovering novel transformations (even including transition metal-catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph and because each single reaction prediction is typically achieved in a sub-second time frame, the model can be used as a high-throughput generator of reaction hypotheses for reaction discovery.

Published
2017
Full Text
View/download PDF

36. Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction

Author

Mark P. Waller and Marwin H. S. Segler

Subjects
010405 organic chemistry, business.industry, Chemistry, Organic Chemistry, Context (language use), General Chemistry, 010402 general chemistry, Machine learning, computer.software_genre, 01 natural sciences, Catalysis, Expert system, 0104 chemical sciences, Transformation (function), Deep neural networks, Artificial intelligence, Chemistry (relationship), business, Set (psychology), Retrosynthetic analysis, computer
Abstract

Reaction prediction and retrosynthesis are the cornerstones of organic chemistry. Rule-based expert systems have been the most widespread approach to computationally solve these two related challenges to date. However, reaction rules often fail because they ignore the molecular context, which leads to reactivity conflicts. Herein, we report that deep neural networks can learn to resolve reactivity conflicts and to prioritize the most suitable transformation rules. We show that by training our model on 3.5 million reactions taken from the collective published knowledge of the entire discipline of chemistry, our model exhibits a top10-accuracy of 95 % in retrosynthesis and 97 % for reaction prediction on a validation set of almost 1 million reactions.

Published
2016

37. PROGRESS IN THE CONSTRUCTION OF THE CDF SILICON VERTEX DETECTOR

Author

S. Dell'Agnello, M. Wong, L. Pescara, G. Busetto, Hans J. Wenzel, Andrea Castro, Mosè Mariotti, J. A. J. Matthews, R. Ely, M. Gold, P. Tipton, F. D. Snider, Slawek Tkaczyk, S. Segler, S. Kleinfelder, S. Vejcik, V. Bolognesi, M. Hrycyk, N. M. Shaw, J. Skarha, B. A. Barnett, Giovanni Punzi, M. Loreti, Dario Bisello, H. Carter, Kathleen R. Turner, Nicola Turini, O. Schneider, Charles A. Nelson, M. W. Bailey, L. Ristori, Timothy M. Shaw, D. Amidei, Nicola Bacchetta, F. Bedeschi, A. Menzione, B. Gonzales, W. C. Wester, C. Boswell, Alan Garfinkel, A. Spies, S. Galeotti, P. F. Derwent, J. Tseng, W. Carithers, S.E. Holland, F. Raffaelli, C. Haber, B. Flaugher, F. Tartarelli, F. Zetti, and T. Merrick

Subjects
Physics, Nuclear and High Energy Physics, Silicon, Physics::Instrumentation and Detectors, business.industry, Detector, Readout electronics, chemistry.chemical_element, STRIPS, Silicon vertex detector, law.invention, Nuclear physics, Optics, chemistry, law, Mechanical design, High Energy Physics::Experiment, Gas cooling, business, Instrumentation
Abstract

The mechanical design and construction progress of the CDF silicon vertex detector is described. Results on the location accuracy of the silicon strip detectors are presented and indicate an initial placement uncertainty of less than 10 μm. The water and gas cooling system, which is constructed of low mass materials and is used to remove heat from the readout electronics, is briefly described. Measurements of the performance of the silicon strip detectors are also given and show > 98.5% functioning strips for the assembled SVX detector.

Published
2016
Full Text
View/download PDF

38. Observation of diffractive J/psi production at the Fermilab Tevatron

Author

K. Kondo, Robert M Harris, G. Veramendi, M. Gold, A. Munar, G. Pauletta, Andrea Castro, D. Khazins, Maxwell Chertok, T. Ohsugi, I. Yu, M. J. Shochet, K. Karr, R. J. Miller, A. Connolly, E. Kovacs, T. Ohmoto, W. Orejudos, Frank Chlebana, T. Asakawa, Z. Yu, Marjorie Shapiro, A. J. Slaughter, A. Mukherjee, A. Akopian, C. Pagliarone, G. Punzi, E. Engels, Maurice Garcia-Sciveres, M. P. Schmidt, Andrea Bocci, E. Buckley-Geer, Riccardo Paoletti, P. Schlabach, S. Galeotti, A. Rakitine, P. Sinervo, R. Takashima, Tommaso Dorigo, James Bensinger, M. Barone, M. Kelly, E. McKigney, J. Wolinski, A. Reichold, Leonard Spiegel, R. Vilar, P. K. Teng, C. Green, A. Nomerotski, T. Yoshida, P. Azzi-Bacchetta, C. Smith, M. Guenther, C. Bromberg, P. Murat, G. Velev, N. Lai, Kazuhiko Hara, F. Zetti, Stephan Lammel, A. Robinson, S. Wolinski, J. D. Lewis, Kenichi Hatakeyama, J. G. Loken, C. Holck, E. James, S. Vejcik, M. Mangano, G. W. Foster, D. Glenzinski, M. Lindgren, T. Kikuchi, A. Artikov, S. Segler, H. Sato, M. Kirk, P. Koehn, P. Yeh, Fedor Prokoshin, Pierre Savard, M. Popovic, Yasuo Fukui, T. Takano, M. Franklin, Lawrence Nodulman, M. Shimojima, Stefano Belforte, T. Okusawa, T. Kuwabara, P. Mazzanti, Q. Fan, A. Tollestrup, W. Caskey, C. Ferretti, R. E. Hughes, Maria Spiropulu, T. Devlin, B. Tannenbaum, Thomas LeCompte, A. Sidoti, J. Done, J. Huston, Matthew Herndon, Chunhui Chen, Petar Maksimovic, Hajin Kim, M. Mishina, J. C. Freeman, J. A.J. Matthews, G. Bauer, A. D. Hardman, F. D. Snider, J. Cranshaw, L. Ristori, R. Culbertson, R. J. Tesarek, S. Rolli, Benjamin Kilminster, Y. Gotra, Fumihiko Ukegawa, A. Dominguez, D. Winn, Christoph Paus, J. Carlson, A. B. Wicklund, William Trischuk, D. Lucchesi, Barry Blumenfeld, S. C. van den Brink, M. J. Wang, T. Miao, R. M. Haas, Umesh Joshi, Roger Moore, M. H. Kirby, D. O. Litvintsev, Nicola Bacchetta, E. E. Schmidt, C. I. Ciobanu, A. Roy, J. Steele, F. Bedeschi, Xin Wu, S. Wolbers, D. W. Gerdes, K. D. Hoffman, Gino Bolla, T. L. Watts, L. Holloway, H. Toyoda, S. H. Kim, Christina Mesropian, Yanwen Liu, R. Kephart, R. L. Wagner, Hiroto Kambara, T. Handa, Andrey Korytov, N. S. Lockyer, Roberto Rossin, S. Cabrera, W. Carithers, S. Murgia, H. Wenzel, H. Akimoto, A. Scribano, R. Oishi, R. Madrak, Joe Kroll, C. Nelson, Monica D'Onofrio, J. Patrick, P. Sphicas, Virgil E Barnes, N. Moggi, Frank Hartmann, Alan Sill, K. Goulianos, S. R. Hahn, V. Glagolev, K. S. McFarland, S. Tether, J. Antos, Konstanty Sumorok, M. Bishai, S. Lami, T. Shah, J. Berryhill, A. Semenov, M. Cordelli, G. Busetto, Brian L Winer, M. Brozovic, Minu Kim, S. Waschke, K. Tollefson, R. G. Wagner, Kenneth Bloom, Matthew Jones, K. Anikeev, D. Errede, G. Latino, Th. Müller, K. Sliwa, S. Klimenko, Aw Chan, T. Shibayama, M. Loreti, Duncan Carlsmith, J. Valls, D. Wolinski, K. Kelley, S. Leone, H. S. Budd, T. Kaneko, Joel Goldstein, Y. C. Chen, S. Tkaczyk, David Stuart, Y. Kato, D. Neuberger, G. Guillian, M. Binkley, P. Gris, Alexei Safonov, M. G. Albrow, Douglas Benjamin, J. Guimaraes Da Costa, D. Tonelli, R. G. Feild, K. Terashi, P. S. Chang, J. B. Liu, James D. Olsen, S. Lusin, F. Happacher, P. T. Chang, Dario Bisello, A. Bhatti, C. Haber, Tong Gao, S. E. Kuhlmann, A. Menzione, Y. Seiya, Nicola Turini, A. T. Laasanen, Catherine Newman-Holmes, Jay Hauser, G. P. Yeh, I. V. Gorelov, T. Speer, J. Spalding, G. Chlachidze, G. P. Grim, Masaaki Tanaka, Tony Liss, P. Amaral, G. Piacentino, S. Worm, K. Borras, R. D. Field, D. Amidei, M. L. Chu, A. T. Goshaw, H. Minato, Mosè Mariotti, Paolo Calafiura, Teresa Rodrigo, K. Lee, W. C. Wester, F. Strumia, T. J. Phillips, S. Miscetti, S. Zucchelli, R. E. Blair, Andrea Sansoni, Ivan-Kresimir Furic, Christopher Neu, F. Rimondi, E. Brubaker, J. Strologas, P. de Barbaro, A. Gallas, J. F. de Troconiz, Y. Morita, P. Tamburello, J. Lys, S. Donati, J. Yoh, P. Gatti, M. E. Convery, P. F. Derwent, V. Nagaslaev, M. von der Mey, Hiroshi Ikeda, R. Roser, Ivan Vila, J. N. Bellinger, G. Bellettini, H. Bachacou, S. Baroiant, F. DeJongh, W. Ashmanskas, Robin Erbacher, W-M. Yao, O. Pukhov, Louis Lyons, G. Martignon, R. C. Webb, J.I. Friedman, M. Lancaster, A. Byon-Wagner, Tetsuo Arisawa, C. Hill, S. Errede, W. K. Sakumoto, N. B. Wallace, L. Demortier, Igor Volobouev, C. H. Wang, N. Eddy, R. J. Hollebeek, J. Wyss, Soo-Bong Kim, Andrew Beretvas, Timothy Knight Nelson, C. Grosso-Pilcher, Z. Wan, A. M. Lee, J. S. Miller, Kevin Burkett, Jacobo Konigsberg, Young Do Oh, J. Mayer, Beom Jun Kim, Richard Dante St Denis, A. Bodek, J. Iwai, W. H. Bell, G. Chiarelli, W. Hao, A. Ruiz, D. Toback, M. T. Cheng, R. D. Kennedy, M. M. Deninno, C. Yosef, Alessandro Cerri, B. Ward, Amitabh Lath, A. Brandl, T. Moulik, I.E. Chirikov-Zorin, A. Solodsky, A. G. Clark, B. A. Barnett, Giorgio Apollinari, A. Meyer, J. Siegrist, Kevin Einsweiler, T. Wilkes, John Huth, M. Dell'Orso, Teruki Kamon, A. J. Martin, L. Pondrom, E. Wicklund, Craig Blocker, C. R. Hall, J. W. Chapman, Kaori Maeshima, Mark Kruse, Luca Scodellaro, Guenakh Mitselmakher, J. E. Elias, T. Vaiciulis, Paul Tipton, K. L. Byrum, P. Wilson, P. N. Singh, M. Menguzzato, M. Tecchio, R. Vidal, Anna Zanetti, H. Kasha, H. Niu, N. Bruner, L. Rosenson, P. F. Shepard, S. H. Oh, Jane Nachtman, J. P. Fernandez, L. E. Kirsch, F. Ratnikov, P. Lukens, Jay Dittmann, D. Cauz, V. Papadimitriou, Daijin Kim, Michele Gallinaro, A. S. Thompson, A. Heiss, Itsuo Nakano, D. Dagenhart, J. Tseng, L. Groer, L. Pescara, Alan Garfinkel, J. Boudreau, M. Riveline, H. H. Williams, K. T. Pitts, G. Introzzi, A. Korn, A. Stefanini, Y. Bonushkin, David Saltzberg, D. Reher, P. McIntyre, J. Budagov, R. L. Lander, J. P. Berge, F. Azfar, D. E. Pellett, L. Christofek, T. Suzuki, C. Sanchez, Manfred Paulini, S. Blusk, Jochen Jens Heinrich, H. Nakada, A. Pompos, Sally Seidel, Saverio D'Auria, O. Lobban, Yongsun Kim, L. Santi, Shin-Shan Yu, A. Ribon, M. Mulhearn, W. Riegler, F. Ptohos, W. J. Robertson, T. Watanabe, W. Bokhari, M. Campbell, D. Waters, P. Giannetti, J. Lancaster, R. Handler, Y. S. Chung, S. Dell'Agnello, R. Cropp, S. Bailey, B. Flaugher, Michael Schmitt, J. Incandela, J. Conway, G. Pope, T. Affolder, A. Barbaro-Galtieri, D. Vucinic, F. Semeria, T. A. Keaffaber, F. Spinella, K. Kurino, I. Fiori, A. W. Scott, D. Partos, James Proudfoot, F. Palmonari, L. Malferrari, R. Thurman-Keup, Cyp Ngan, S. Nahn, E. Moore, Y. Iwata, Daniela Bortoletto, K. Takikawa, Clark, Allan Geoffrey, Kambara, Hisanori, Speer, Thomas, Strumia Michelini, Federica, and Wu, Xin

Subjects
Physics, Particle physics, Muon, Proton, Tevatron, General Physics and Astronomy, Particle accelerator, ddc:500.2, SILICON VERTEX DETECTOR, DIJET PRODUCTION, CDF, Gluon, law.invention, High Energy Physics - Experiment, Nuclear physics, law, Pseudorapidity, SILICON VERTEX DETECTOR, DIJET PRODUCTION, CDF, Rapidity, High Energy Physics::Experiment, Fermilab, Nuclear Experiment, QC
Abstract

We report the first observation of diffractive $J/\psi(\to \mu^+\mu^-)$ production in $\bar pp$ collisions at $\sqrt{s}$=1.8 TeV. Diffractive events are identified by their rapidity gap signature. In a sample of events with two muons of transverse momentum $p_T^{\mu}>2$ GeV/$c$ within the pseudorapidity region $|\eta, Comment: 9 pages, 4 figures, using RevTeX. Submitted to Physical Review Letters

Published
2016
Full Text
View/download PDF

39. Measurement of the strong coupling constant from inclusive jet production at the Tevatron pp collider

Author

T. Affolder, H. Akimoto, A. Akopian, M. G. Albrow, P. Amaral, D. Amidei, K. Anikeev, J. Antos, G. Apollinari, T. Arisawa, A. Artikov, T. Asakawa, W. Ashmanskas, F. Azfar, P. Azzi-Bacchetta, N. Bacchetta, H. Bachacou, S. Bailey, P. de Barbaro, A. Barbaro-Galtieri, V. E. Barnes, B. A. Barnett, S. Baroiant, M. Barone, G. Bauer, F. Bedeschi, S. Belforte, W. H. Bell, G. Bellettini, J. Bellinger, D. Benjamin, J. Bensinger, A. Beretvas, J. P. Berge, J. Berryhill, A. Bhatti, M. Binkley, D. Bisello, M. Bishai, R. E. Blair, C. Blocker, K. Bloom, B. Blumenfeld, S. R. Blusk, A. Bocci, A. Bodek, W. Bokhari, G. Bolla, Y. Bonushkin, D. Bortoletto, J. Boudreau, A. Brandl, S. van den Brink, C. Bromberg, M. Brozovic, E. Brubaker, N. Bruner, E. Buckley-Geer, J. Budagov, H. S. Budd, K. Burkett, G. Busetto, A. Byon-Wagner, K. L. Byrum, S. Cabrera, P. Calafiura, M. Campbell, W. Carithers, J. Carlson, D. Carlsmith, W. Caskey, A. Castro, D. Cauz, A. Cerri, A. W. Chan, P. S. Chang, P. T. Chang, J. Chapman, C. Chen, Y. C. Chen, M.-T. Cheng, M. Chertok, G. Chiarelli, I. Chirikov-Zorin, G. Chlachidze, F. Chlebana, L. Christofek, M. L. Chu, Y. S. Chung, C. I. Ciobanu, A. G. Clark, A. P. Colijn, A. Connolly, M. Convery, J. Conway, M. Cordelli, J. Cranshaw, R. Cropp, R. Culbertson, D. Dagenhart, S. D'Auria, F. DeJongh, S. Dell'Agnello, M. Dell'Orso, S. Demers, L. Demortier, M. Deninno, P. F. Derwent, T. Devlin, J. R. Dittmann, A. Dominguez, S. Donati, J. Done, M. D'Onofrio, T. Dorigo, N. Eddy, K. Einsweiler, J. E. Elias, E. Engels, R. Erbacher, D. Errede, S. Errede, Q. Fan, H.-C. Fang, R. G. Feild, J. P. Fernandez, C. Ferretti, R. D. Field, I. Fiori, B. Flaugher, G. W. Foster, M. Franklin, J. Freeman, J. Friedman, Y. Fukui, I. Furic, S. Galeotti, A. Gallas, M. Gallinaro, T. Gao, M. Garcia-Sciveres, A. F. Garfinkel, P. Gatti, C. Gay, D. W. Gerdes, P. Giannetti, P. Giromini, V. Glagolev, D. Glenzinski, M. Gold, J. Goldstein, I. Gorelov, A. T. Goshaw, Y. Gotra, K. Goulianos, C. Green, G. Grim, P. Gris, L. Groer, C. Grosso-Pilcher, M. Guenther, G. Guillian, J. Guimaraes da Costa, R. M. Haas, C. Haber, S. R. Hahn, C. Hall, T. Handa, R. Handler, W. Hao, F. Happacher, K. Hara, A. D. Hardman, R. M. Harris, F. Hartmann, K. Hatakeyama, J. Hauser, J. Heinrich, A. Heiss, M. Herndon, C. Hill, K. D. Hoffman, C. Holck, R. Hollebeek, L. Holloway, B. T. Huffman, R. Hughes, J. Huston, J. Huth, H. Ikeda, J. Incandela, G. Introzzi, A. Ivanov, J. Iwai, Y. Iwata, E. James, M. Jones, U. Joshi, H. Kambara, T. Kamon, T. Kaneko, K. Karr, S. Kartal, H. Kasha, Y. Kato, T. A. Keaffaber, K. Kelley, M. Kelly, D. Khazins, T. Kikuchi, B. Kilminster, B. J. Kim, D. H. Kim, H. S. Kim, M. J. Kim, S. B. Kim, S. H. Kim, Y. K. Kim, M. Kirby, M. Kirk, L. Kirsch, S. Klimenko, P. Koehn, K. Kondo, J. Konigsberg, A. Korn, A. Korytov, E. Kovacs, J. Kroll, M. Kruse, S. E. Kuhlmann, K. Kurino, T. Kuwabara, A. T. Laasanen, N. Lai, S. Lami, S. Lammel, J. Lancaster, M. Lancaster, R. Lander, A. Lath, G. Latino, T. LeCompte, A. M. Lee, K. Lee, S. Leone, J. D. Lewis, M. Lindgren, T. M. Liss, J. B. Liu, Y. C. Liu, D. O. Litvintsev, O. Lobban, N. Lockyer, J. Loken, M. Loreti, D. Lucchesi, P. Lukens, S. Lusin, L. Lyons, J. Lys, R. Madrak, K. Maeshima, P. Maksimovic, L. Malferrari, M. Mangano, M. Mariotti, G. Martignon, A. Martin, J. A. J. Matthews, J. Mayer, P. Mazzanti, K. S. McFarland, P. McIntyre, E. McKigney, M. Menguzzato, A. Menzione, P. Merkel, C. Mesropian, A. Meyer, T. Miao, R. Miller, J. S. Miller, H. Minato, S. Miscetti, M. Mishina, G. Mitselmakher, Y. Miyazaki, N. Moggi, E. Moore, R. Moore, Y. Morita, T. Moulik, M. Mulhearn, A. Mukherjee, T. Muller, A. Munar, P. Murat, S. Murgia, J. Nachtman, V. Nagaslaev, S. Nahn, H. Nakada, I. Nakano, C. Nelson, T. Nelson, C. Neu, D. Neuberger, C. Newman-Holmes, C.-Y. P. Ngan, H. Niu, L. Nodulman, A. Nomerotski, S. H. Oh, Y. D. Oh, T. Ohmoto, T. Ohsugi, R. Oishi, T. Okusawa, J. Olsen, W. Orejudos, C. Pagliarone, F. Palmonari, R. Paoletti, V. Papadimitriou, D. Partos, J. Patrick, G. Pauletta, M. Paulini, C. Paus, D. Pellett, L. Pescara, T. J. Phillips, G. Piacentino, K. T. Pitts, A. Pompos, L. Pondrom, G. Pope, M. Popovic, F. Prokoshin, J. Proudfoot, F. Ptohos, O. Pukhov, G. Punzi, A. Rakitine, F. Ratnikov, D. Reher, A. Reichold, P. Renton, A. Ribon, W. Riegler, F. Rimondi, L. Ristori, M. Riveline, W. J. Robertson, A. Robinson, T. Rodrigo, S. Rolli, L. Rosenson, R. Roser, R. Rossin, C. Rott, A. Roy, A. Ruiz, A. Safonov, R. St. Denis, W. K. Sakumoto, D. Saltzberg, C. Sanchez, A. Sansoni, L. Santi, H. Sato, P. Savard, P. Schlabach, E. E. Schmidt, M. P. Schmidt, M. Schmitt, L. Scodellaro, A. Scott, A. Scribano, S. Segler, S. Seidel, Y. Seiya, A. Semenov, F. Semeria, T. Shah, M. D. Shapiro, P. F. Shepard, T. Shibayama, M. Shimojima, M. Shochet, A. Sidoti, J. Siegrist, A. Sill, P. Sinervo, P. Singh, A. J. Slaughter, K. Sliwa, C. Smith, F. D. Snider, A. Solodsky, J. Spalding, T. Speer, P. Sphicas, F. Spinella, M. Spiropulu, L. Spiegel, J. Steele, A. Stefanini, J. Strologas, F. Strumia, D. Stuart, K. Sumorok, T. Suzuki, T. Takano, R. Takashima, K. Takikawa, P. Tamburello, M. Tanaka, B. Tannenbaum, M. Tecchio, R. Tesarek, P. K. Teng, K. Terashi, S. Tether, A. S. Thompson, R. Thurman-Keup, P. Tipton, S. Tkaczyk, D. Toback, K. Tollefson, A. Tollestrup, D. Tonelli, H. Toyoda, W. Trischuk, J. F. de Troconiz, J. Tseng, N. Turini, F. Ukegawa, T. Vaiciulis, J. Valls, S. Vejcik, G. Velev, G. Veramendi, R. Vidal, I. Vila, R. Vilar, I. Volobouev, M. von der Mey, D. Vucinic, R. G. Wagner, R. L. Wagner, N. B. Wallace, Z. Wan, C. Wang, M. J. Wang, B. Ward, S. Waschke, T. Watanabe, D. Waters, T. Watts, R. Webb, H. Wenzel, W. C. Wester, A. B. Wicklund, E. Wicklund, T. Wilkes, H. H. Williams, P. Wilson, B. L. Winer, D. Winn, S. Wolbers, D. Wolinski, J. Wolinski, S. Wolinski, S. Worm, X. Wu, J. Wyss, W. Yao, G. P. Yeh, P. Yeh, J. Yoh, C. Yosef, T. Yoshida, I. Yu, S. Yu, Z. Yu, A. Zanetti, F. Zetti, S. Zucchelli, Clark, Allan Geoffrey, Kambara, Hisanori, Speer, Thomas, Strumia Michelini, Federica, and Wu, Xin

Subjects
Physics, Quantum chromodynamics, Coupling constant, Particle physics, General Physics and Astronomy, ddc:500.2, Gluon, Renormalization, Distribution function, Production (computer science), High Energy Physics::Experiment, Nuclear Experiment, Energy (signal processing), Boson
Abstract

We report a measurement of the strong coupling constant, ${\ensuremath{\alpha}}_{s}({M}_{Z})$, extracted from inclusive jet production in $p\overline{p}$ collisions at $\sqrt{s}\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}1800\mathrm{GeV}$. The QCD prediction for the evolution of ${\ensuremath{\alpha}}_{s}$ with jet transverse energy ${E}_{T}$ is tested over the range $40l{E}_{T}l450\mathrm{GeV}$ using ${E}_{T}$ for the renormalization scale. The data show good agreement with QCD in the region below 250 GeV. The value of ${\ensuremath{\alpha}}_{s}$ at the mass of the ${Z}^{0}$ boson averaged over the range $40l{E}_{T}l250\mathrm{GeV}$ is found to be ${\ensuremath{\alpha}}_{s}({M}_{Z})\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}0.1178\ifmmode\pm\else\textpm\fi{}0.0001(\mathrm{stat}{)}_{\ensuremath{-}0.0095}^{+0.0081}(\mathrm{expt}.\mathrm{syst})$. The associated theoretical uncertainties are mainly due to the choice of renormalization scale $({+6%}{\ensuremath{-}4%})$ and input parton distribution functions $(5%)$.

Published
2016
Full Text
View/download PDF

40. 11th German Conference on Chemoinformatics (GCC 2015) : Fulda, Germany. 8-10 November 2015

Author

Uli Fechner, Chris de Graaf, Andrew E. Torda, Stefan Güssregen, Andreas Evers, Hans Matter, Gerhard Hessler, Nicola J. Richmond, Peter Schmidtke, Marwin H. S. Segler, Mark P. Waller, Stefanie Pleik, Joan-Emma Shea, Zachary Levine, Ryan Mullen, Karina van den Broek, Matthias Epple, Hubert Kuhn, Andreas Truszkowski, Achim Zielesny, Johannes Fraaije, Ruben Serral Gracia, Stefan M. Kast, Krishna C. Bulusu, Andreas Bender, Abraham Yosipof, Oren Nahum, Hanoch Senderowitz, Timo Krotzky, Robert Schulz, Gerhard Wolber, Stefan Bietz, Matthias Rarey, Markus O. Zimmermann, Andreas Lange, Manuel Ruff, Johannes Heidrich, Ionut Onlia, Thomas E. Exner, Frank M. Boeckler, Marcel Bermudez, Dzmitry S. Firaha, Oldamur Hollóczki, Barbara Kirchner, Christofer S. Tautermann, Andrea Volkamer, Sameh Eid, Samo Turk, Friedrich Rippmann, Simone Fulle, Noureldin Saleh, Giorgio Saladino, Francesco L. Gervasio, Elke Haensele, Lee Banting, David C. Whitley, Jana Sopkova-de Oliveira Santos, Ronan Bureau, Timothy Clark, Achim Sandmann, Harald Lanig, Patrick Kibies, Jochen Heil, Franziska Hoffgaard, Roland Frach, Julian Engel, Steven Smith, Debjit Basu, Daniel Rauh, Oliver Kohlbacher, Jonathan W. Essex, Michael S. Bodnarchuk, Gregory A. Ross, Arndt R. Finkelmann, Andreas H. Göller, Gisbert Schneider, Tamara Husch, Christoph Schütter, Andrea Balducci, Martin Korth, Fidele Ntie-Kang, Stefan Günther, Wolfgang Sippl, Luc Meva’a Mbaze, Conrad V. Simoben, Lydia L. Lifongo, Philip Judson, Jiří Barilla, Miloš V. Lokajíček, Hana Pisaková, Pavel Simr, Natalia Kireeva, Alexandre Petrov, Denis Ostroumov, Vitaly P. Solovev, Vladislav S. Pervov, Nils-Ole Friedrich, Kai Sommer, Johannes Kirchmair, Eugen Proschak, Julia Weber, Daniel Moser, Lena Kalinowski, Janosch Achenbach, Mark Mackey, Tim Cheeseright, Gerrit Renner, Torsten C. Schmidt, Jürgen Schram, Marion Egelkraut-Holtus, Albert van Oeyen, Tuomo Kalliokoski, Denis Fourches, Akachukwu Ibezim, Chika J. Mbah, Umale M. Adikwu, Ngozi J. Nwodo, Alexander Steudle, Brian B. Masek, Stephan Nagy, David Baker, Fred Soltanshahi, Roman Dorfman, Karen Dubrucq, Hitesh Patel, Oliver Koch, Florian Mrugalla, Qurrat U. Ain, Julian E. Fuchs, Robert M. Owen, Kiyoyuki Omoto, Rubben Torella, David C. Pryde, Robert Glen, Petr Hošek, Vojtěch Spiwok, Lewis H. Mervin, Ian Barrett, Mike Firth, David C. Murray, Lisa McWilliams, Qing Cao, Ola Engkvist, Dawid Warszycki, Marek Śmieja, Andrzej J. Bojarski, Natalia Aniceto, Alex Freitas, Taravat Ghafourian, Guido Herrmann, Valentina Eigner-Pitto, Alexandra Naß, Rafał Kurczab, Marcel B. Günther, Susanne Hennig, Felix M. Büttner, Christoph Schall, Adrian Sievers-Engler, Francesco Ansideri, Pierre Koch, Thilo Stehle, Stefan Laufer, Frank M. Böckler, Barbara Zdrazil, Floriane Montanari, Gerhard F. Ecker, Christoph Grebner, Anders Hogner, Johan Ulander, Karl Edman, Victor Guallar, Christian Tyrchan, Wolfgang Klute, Fredrik Bergström, Christian Kramer, Quoc Dat Nguyen, Steven Strohfeldt, Saraphina Böttcher, Tim Pongratz, Dominik Horinek, Bernd Rupp, Raed Al-Yamori, Michael Lisurek, Ronald Kühne, Filipe Furtado, Ludger Wessjohann, Miriam Mathea, Knut Baumann, Siti Zuraidah Mohamad-Zobir, Xianjun Fu, Tai-Ping Fan, Maximilian A. Kuhn, Christoph A. Sotriffer, Azedine Zoufir, Xitong Li, Lewis Mervin, Ellen Berg, Mark Polokoff, Wolf D. Ihlenfeldt, Jette Pretzel, Zayan Alhalabi, Robert Fraczkiewicz, Marvin Waldman, Robert D. Clark, Neem Shaikh, Prabha Garg, Alexander Kos, Hans-Jürgen Himmler, Christophe Jardin, Heinrich Sticht, Thomas B. Steinbrecher, Markus Dahlgren, Daniel Cappel, Teng Lin, Lingle Wang, Goran Krilov, Robert Abel, Richard Friesner, Woody Sherman, Ina A. Pöhner, Joanna Panecka, Rebecca C. Wade, Karen T. Schomburg, Matthias Hilbig, Christian Jäger, Vivien Wieczorek, Lance M. Westerhoff, Oleg Y. Borbulevych, Hans-Ulrich Demuth, Mirko Buchholz, Denis Schmidt, Thomas Rickmeyer, Peter Kolb, Sumit Mittal, Elsa Sánchez-García, Mauro S. Nogueira, Tiago B. Oliveira, Fernando B. da Costa, and Thomas J. Schmidt

Subjects
0303 health sciences, Philosophy, Library and Information Sciences, 16. Peace & justice, Bioinformatics, 01 natural sciences, Computer Graphics and Computer-Aided Design, Meeting Abstracts, language.human_language, 0104 chemical sciences, Computer Science Applications, German, 010404 medicinal & biomolecular chemistry, 03 medical and health sciences, language, Physical and Theoretical Chemistry, Humanities, 030304 developmental biology
Abstract

Author(s): Fechner, Uli; de Graaf, Chris; Torda, Andrew E; Gussregen, Stefan; Evers, Andreas; Matter, Hans; Hessler, Gerhard; Richmond, Nicola J; Schmidtke, Peter; Segler, Marwin HS; Waller, Mark P; Pleik, Stefanie; Shea, Joan-Emma; Levine, Zachary; Mullen, Ryan; van den Broek, Karina; Epple, Matthias; Kuhn, Hubert; Truszkowski, Andreas; Zielesny, Achim; Fraaije, Johannes Hans; Gracia, Ruben Serral; Kast, Stefan M; Bulusu, Krishna C; Bender, Andreas; Yosipof, Abraham; Nahum, Oren; Senderowitz, Hanoch; Krotzky, Timo; Schulz, Robert; Wolber, Gerhard; Bietz, Stefan; Rarey, Matthias; Zimmermann, Markus O; Lange, Andreas; Ruff, Manuel; Heidrich, Johannes; Onlia, Ionut; Exner, Thomas E; Boeckler, Frank M; Bermudez, Marcel; Firaha, Dzmitry S; Holloczki, Oldamur; Kirchner, Barbara; Tautermann, Christofer S; Volkamer, Andrea; Eid, Sameh; Turk, Samo; Rippmann, Friedrich; Fulle, Simone; Saleh, Noureldin; Saladino, Giorgio; Gervasio, Francesco L; Haensele, Elke; Banting, Lee; Whitley, David C; Oliveira Santos, Jana Sopkova-de; Bureau, Ronan; Clark, Timothy; Sandmann, Achim; Lanig, Harald; Kibies, Patrick; Heil, Jochen; Hoffgaard, Franziska; Frach, Roland; Engel, Julian; Smith, Steven; Basu, Debjit; Rauh, Daniel; Kohlbacher, Oliver; Boeckler, Frank M; Essex, Jonathan W; Bodnarchuk, Michael S; Ross, Gregory A; Finkelmann, Arndt R; Goller, Andreas H; Schneider, Gisbert; Husch, Tamara; Schutter, Christoph; Balducci, Andrea; Korth, Martin; Ntie-Kang, Fidele; Gunther, Stefan; Sippl, Wolfgang; Mbaze, Luc Meva'a

Published
2016

41. ChemInform Abstract: Dehydrogenative TEMPO-Mediated Formation of Unstable Nitrones: Easy Access to N-Carbamoyl Isoxazolines

Author

Andrea Gini, Dominik Kellner, Marwin H. S. Segler, and Olga García Mancheño

Subjects
chemistry.chemical_classification, chemistry, Reagent, Dehydrogenation, General Medicine, Combinatorial chemistry, Nitrone
Abstract

N-carbamoyl nitrones represent an important class of reagents for the synthesis of a variety of natural and biologically active compounds. These compounds are generally converted into valuable 4-isoxazolines upon cyclization reaction with dipolarophiles. However, these types of N-protected nitrones are highly unstable, which limits their synthesis, storage and practical use, enforcing alternative lengthy or elaborated synthetic routes. In this work, a 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO)-mediated formal "dehydrogenation" of N-protected benzyl-, allyl- and alkyl-substituted hydroxylamines followed by in situ trapping of the generated unstable nitrones into N-carbamoyl 4-isoxazolines is presented. A plausible mechanism is also proposed, in which the dipolarophile shows an important assistant role in the generation of the active nitrone intermediate. This simple protocol avoids the problematic isolation of N-carbamoyl protected nitrones, providing new synthetic possibilities in isoxazoline chemistry.

Published
2016
Full Text
View/download PDF

42. Opportunities and obstacles for deep learning in biology and medicine

Author

Travers Ching, S. Joshua Swamidass, Anne E. Carpenter, Michael M. Hoffman, Gregory P. Way, Dave DeCaprio, Benjamin J. Lengerich, Avanti Shrikumar, Johnny Israeli, Zhiyong Lu, Austin Huang, Amr Alexandari, Christopher A. Lavender, Anthony Gitter, Enrico Ferrero, Jack Lanchantin, Evan M. Cofer, Brett K. Beaulieu-Jones, Wei Xie, Michael Zietz, Laura K. Wiley, Anshul Kundaje, Paul-Michael Agapow, Yifan Peng, Stephen Woloszynek, Jinbo Xu, Srinivas C. Turaga, Yanjun Qi, Brian T. Do, Marwin H. S. Segler, Alexandr A. Kalinin, Gail L. Rosen, Simina M. Boca, David J. Harris, Daniel Himmelstein, and Casey S. Greene

Subjects
0301 basic medicine, Biomedical Research, Computer science, Big data, Health records, Biochemistry, Field (computer science), 0302 clinical medicine, Electronic Health Records, Disease, Review Articles, Interpretability, 0303 health sciences, Class (computer programming), Artificial neural network, Management science, 3. Good health, Variety (cybernetics), machine learning, Algorithms, Biotechnology, precision medicine, Decision Making, Biomedical Technology, Biomedical Engineering, Biophysics, Bioengineering, Biomaterials, 03 medical and health sciences, Terminology as Topic, genomics, Humans, Baseline (configuration management), Biomedicine, 030304 developmental biology, business.industry, Deep learning, deep learning, Precision medicine, Data science, 030104 developmental biology, Drug Design, Labeled data, Artificial intelligence, business, Delivery of Health Care, 030217 neurology & neurosurgery, Headline Review
Abstract

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Published
2018
Full Text
View/download PDF

43. Shigella Bacteremia, Georgia, USA, 2002-2012 1 .

Author

Tobin-D'Angelo M, Oosmanally N, Wilson SN, Anderson EJ, Segler S, and Poventud L

Subjects
Adult, Age Factors, Bacteremia microbiology, Dysentery, Bacillary microbiology, Female, Georgia epidemiology, HIV Infections complications, HIV Infections microbiology, Humans, Male, Racial Groups statistics & numerical data, Risk Factors, Sex Factors, Shigella flexneri, Bacteremia epidemiology, Dysentery, Bacillary epidemiology, Shigella
Abstract

Shigella commonly causes gastroenteritis but rarely spreads to the blood. During 2002-2012, we identified 11,262 Shigella infections through population-based active surveillance in Georgia; 72 (0.64%) were isolated from blood. Bacteremia was associated with age >18 years, black race, and S. flexneri. More than half of patients with bacteremia were HIV-infected.

Published
2020
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results