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DTSTAMP:20190719T085754Z
LOCATION:HG E 1.2
DTSTART;TZID=Europe/Stockholm:20190614T103000
DTEND;TZID=Europe/Stockholm:20190614T123000
UID:submissions.pasc-conference.org_PASC19_sess127@linklings.com
SUMMARY:MS42 - Artificial Intelligence and Knowledge Representation in Che
 mical Sciences
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Chemi
 stry and Materials\n\nExtracting, Analyzing, and Planning Inorganic Materi
 als Synthesis using Literature-Trained Models\n\nStrubell\n\nMany recent a
 dvances in materials design and discovery have been accelerated by data-dr
 iven methods that leverage structured datasets such as organic reaction da
 tabases. In stark contrast to organic synthesis, however, the vast majorit
 y of knowledge regarding inorganic synthesis remains unstructured...\n\n--
 -------------------\nChemical Database Auto-Generation Tools for Large-Sca
 le Data-Mining\n\nCole\n\nLarge-scale data-mining workflows are increasing
 ly able to predict successfully new chemicals that possess a targeted func
 tionality. The success of such materials discovery approaches is nonethele
 ss contingent upon having the right database source to mine. This presenta
 tion shows how to tailor-make ...\n\n---------------------\nIBM RXN for Ch
 emistry: Predicting Chemical Reactions using the Molecular Transformer\n\n
 Schwaller, Laino, Gaudin, Bolgar, Bekas...\n\nOrganic synthesis – ma
 king complex molecules out of simpler building blocks – is a crucial
  part in the production and therefore, also in the discovery of novel mole
 cules and materials. One necessary yet unsolved step in synthesis planning
  is solving the forward problem: given reactants a...\n\n-----------------
 ----\nDe Novo Drug Design with Artificial Intelligence\n\nSchneider\n\nWe 
 have implemented and challenged 'chemistry-savvy' deep learning models in 
 prospective molecular design projects that aimed to obtain synthetically e
 asily accessible new chemical entities. We will present several prospectiv
 e applications and discuss opportunities and current limitations of theses
  ...\n
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