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SUMMARY:Discovering Novel Fluorescent Molecules by Combining Machine-learn
 ing and Global Optimization
DTSTART;VALUE=DATE-TIME:20191107T045000Z
DTEND;VALUE=DATE-TIME:20191107T051000Z
DTSTAMP;VALUE=DATE-TIME:20260724T053719Z
UID:indico-contribution-31@sshep.snu.ac.kr
DESCRIPTION:Speakers: Juyong Lee (Kangwon National University)\nFluorescen
 t molecules are widely used for bio-imaging. They are attached to specific
  cell organelles and/or proteins\, enabling observation of detailed struct
 ure and dynamics in the cell. Efficient fluorescent molecules must have a 
 high quantum yield for effective bio-imaging. Here\, we present a systemat
 ic approach to discovering novel fluorescent molecules that combines machi
 ne-learning and global optimization algorithms. We recast the problem of d
 iscovering novel fluorescent molecules with high-intensity emission light 
 into a global optimization problem by using the oscillator strength of a m
 olecule as an objective function for optimization.\n\nA statistical machin
 e that predicts excitation energies and associated oscillator strengths\, 
 the probability of absorption or emission of light in transitions between 
 different energy states\, of a molecule were trained using the random fore
 st algorithm. The Pub-chemQC database\, which contains TD-DFT calculation 
 results of 3.8 million known molecules\, was used as a training set. The e
 xtended connectivity fingerprints of molecules were used as input vectors.
  To optimize the oscillator strength of a molecule\, a highly efficient gl
 obal optimization algorithm called CSA was used. For CSA global optimizati
 on\, SMILES representation of a molecule was mapped to a 200-dimensional i
 nteger vector by using Natural Language Toolkit. After CSA global optimiza
 tion calculation converged\, we assessed the validity of our approach by p
 erforming quantum mechanical calculations. TD-DFT calculations were carrie
 d out to verify whether novel molecules obtained by this procedure actuall
 y have high oscillator strength.\n\nhttps://sshep.snu.ac.kr/event/107/cont
 ributions/31/
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/31/
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