Mol-PECO: a deep learning model to predict human olfactory perception from molecular structures

no code implementations • 21 May 2023 • Mengji Zhang, Yusuke Hiki, Akira Funahashi, Tetsuya J. Kobayashi

While visual and auditory information conveyed by wavelength of light and frequency of sound have been decoded, predicting olfactory information encoded by the combination of odorants remains challenging due to the unknown and potentially discontinuous perceptual space of smells and odorants.

molecular representation Retrieval

BioSimulators: a central registry of simulation engines and services for recommending specific tools

no code implementations • 13 Mar 2022 • Bilal Shaikh, Lucian P. Smith, Dan Vasilescu, Gnaneswara Marupilla, Michael Wilson, Eran Agmon, Henry Agnew, Steven S. Andrews, Azraf Anwar, Moritz E. Beber, Frank T. Bergmann, David Brooks, Lutz Brusch, Laurence Calzone, Kiri Choi, Joshua Cooper, John Detloff, Brian Drawert, Michel Dumontier, G. Bard Ermentrout, James R. Faeder, Andrew P. Freiburger, Fabian Fröhlich, Akira Funahashi, Alan Garny, John H. Gennari, Padraig Gleeson, Anne Goelzer, Zachary Haiman, Joseph L. Hellerstein, Stefan Hoops, Jon C. Ison, Diego Jahn, Henry V. Jakubowski, Ryann Jordan, Matúš Kalaš, Matthias König, Wolfram Liebermeister, Synchon Mandal, Robert McDougal, J. Kyle Medley, Pedro Mendes, Robert Müller, Chris J. Myers, Aurelien Naldi, Tung V. N. Nguyen, David P. Nickerson, Brett G. Olivier, Drashti Patoliya, Loïc Paulevé, Linda R. Petzold, Ankita Priya, Anand K. Rampadarath, Johann M. Rohwer, Ali S. Saglam, Dilawar Singh, Ankur Sinha, Jacky Snoep, Hugh Sorby, Ryan Spangler, Jörn Starruß, Payton J. Thomas, David van Niekerk, Daniel Weindl, Fengkai Zhang, Anna Zhukova, Arthur P. Goldberg, Michael L. Blinov, Herbert M. Sauro, Ion I. Moraru, Jonathan R. Karr

To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators. org), a central registry of the capabilities of simulation tools and consistent Python, command-line, and containerized interfaces to each version of each tool.

Symbolic integration by integrating learning models with different strengths and weaknesses

no code implementations • 9 Mar 2021 • Hazumi Kubota, Yuta Tokuoka, Takahiro G. Yamada, Akira Funahashi

In this study, we adjusted the learning model to take mathematical information into account and developed a wide range of learning models that learn the order of numerical operations more robustly.

Translation