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SUMMARY:Prediction and Taking Insight of Molecular Quantum Property by Ran
 dom Forest Regression
DTSTART;VALUE=DATE-TIME:20191108T065500Z
DTEND;VALUE=DATE-TIME:20191108T071000Z
DTSTAMP;VALUE=DATE-TIME:20260724T113241Z
UID:indico-contribution-22@sshep.snu.ac.kr
DESCRIPTION:Speakers: Beomchang Kang (Seoul National University)\nFluoresc
 ent molecules are widely used for bio-imaging. They are attached to specif
 ic cell organelles or proteins\, enabling observation of detailed structur
 e and dynamics in the cell. Efficient fluorescent molecules must have a hi
 gh quantum yield for effective bio-imaging. Diverse effective fluorescent 
 molecules whose color are distinctive need to get more information of cell
  or protein. First step of discover novel molecules using computational ap
 proach is prediction of compound. Here\, we use random forest regression t
 o predict excitation energy and oscillator strength of a molecule. Random 
 forest algorithm is white box. It is easy to extract feature importance. W
 e could get insight from it.\nA statistical machine that predicts excitati
 on energies and associated oscillator strengths\, the probability of absor
 ption or emission of light in transitions between different energy states\
 , of a molecule were trained using the random forest algorithm. The Pubche
 mQC database was used as a training set. It has over 3 million known compo
 unds. We picked up 0.5 million molecules  randomly. 90% of them were in tr
 aining set and the others were test set. The ECFP4(extended connectivity f
 ingerprints 2) of molecules were used as input features. We found some fra
 gments which can decide molecules’ quantum property by feature importanc
 e analysis.\n\nhttps://sshep.snu.ac.kr/event/107/contributions/22/
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/22/
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