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SUMMARY:Delfos: deep learning model for prediction of solvation free energ
 ies in generic organic solvents
DTSTART;VALUE=DATE-TIME:20191108T040000Z
DTEND;VALUE=DATE-TIME:20191108T042000Z
DTSTAMP;VALUE=DATE-TIME:20260725T092906Z
UID:indico-contribution-13-16@sshep.snu.ac.kr
DESCRIPTION:Speakers: YounJoun Jung (Seoul National University)\nPredictio
 n of aqueous solubilities or hydration free energies is an extensively stu
 died area in machine learning applications in chemistry since water is the
  sole solvent in the living system. However\, for non-aqueous solutions\, 
 few machine learning studies have been undertaken so far despite the fact 
 that the solvation mechanism plays an important role in various chemical r
 eactions. Here\, we introduce Delfos (deep learning model for solvation fr
 ee energies in generic organic solvents)\, which is a novel\, machine-lear
 ning-based QSPR method which predicts solvation free energies for various 
 organic solute and solvent systems. A novelty of Delfos involves two separ
 ate solvent and solute encoder networks that can quantify structural featu
 res of given compounds via word embedding and recurrent layers\, augmented
  with the attention mechanism which extracts important substructures from 
 outputs of recurrent neural networks. As a result\, the predictor network 
 calculates the solvation free energy of a given solvent–solute pair usin
 g features from encoders. With the results obtained from extensive calcula
 tions using 2495 solute–solvent pairs\, we demonstrate that Delfos not o
 nly has great potential in showing accuracy comparable to that of the stat
 e-of-the-art computational chemistry methods\, but also offers information
  about which substructures play a dominant role in the solvation process.\
 n\nReferences:\n\n1. [Delfos: deep learning model for prediction of solvat
 ion free energies in generic organic solvents\, Hyuntae Lim and YounJoon J
 ung\, Chemical Science 2019 DOI: 10.1039/C9SC02452B (2019)] [1]\n\n2. [Med
 ia] [2]\n\n\n  [1]: https://pubs.rsc.org/en/content/articlelanding/2019/sc
 /c9sc02452b#!divAbstract\n  [2]: http://now.snu.ac.kr/47/3/1450\n\nhttps:/
 /sshep.snu.ac.kr/event/107/contributions/16/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/16/
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BEGIN:VEVENT
SUMMARY:AI-based Smart Molecular Design
DTSTART;VALUE=DATE-TIME:20191108T042000Z
DTEND;VALUE=DATE-TIME:20191108T044000Z
DTSTAMP;VALUE=DATE-TIME:20260725T092906Z
UID:indico-contribution-13-37@sshep.snu.ac.kr
DESCRIPTION:Speakers: Woo Youn Kim (Chemistry\, KAIST)\nThe ultimate goal 
 of chemistry is to make new molecules with desired properties. It is chall
 enging because chemical space is very large and discrete with a wide varie
 ty of molecules. For example\, there are only 108 molecules synthesized as
  potential drug candidates\, but 1060 molecules are estimated to be existi
 ng. High-throughput virtual screening approach has attracted great attenti
 on but still requires large costs and time. In this talk\, we propose to u
 se a molecular generative model based on deep learning algorithm as an alt
 ernative. It is specialized in controlling multiple molecular properties s
 imultaneously\, embedding them in namely the latent space. As a proof of c
 oncept\, we will show that it can be used to generate a number of molecule
 s as drugs with specific properties. We also apply it to design of new mol
 ecules with promising binding energy for a specific target protein and use
  them as potential drug candidates that are not in the database.\n\nhttps:
 //sshep.snu.ac.kr/event/107/contributions/37/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/37/
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