BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CERN//INDICO//EN BEGIN:VEVENT SUMMARY:Prediction of Protein Structure and Interaction by Physics and Inf ormatics DTSTART;VALUE=DATE-TIME:20191107T043000Z DTEND;VALUE=DATE-TIME:20191107T045000Z DTSTAMP;VALUE=DATE-TIME:20260724T171406Z UID:indico-contribution-10-28@sshep.snu.ac.kr DESCRIPTION:Speakers: Chaok Seok (Seoul National University)\nProtein stru cture prediction problem has challenged theoretical and computational phys ical scientists since the first protein structure was published in 1958. T here have been steady progresses in protein structure prediction since the n\, but major contributions to the progress came from informatics-based ap proaches rather than from physics-based approaches. Recently\, DeepMind’ s AlphaFold made a further contribution by introducing deep learning to ex tract structural information from the large sequence database. Meaningful contribution of physics-based approaches began to be made only in 2012 in the field of structure refinement. However\, structural improvements that can be achieved by refinement with current physics-based approaches are ve ry limited due to both energy and sampling problems. To overcome this limi tation\, we are taking an approach that combines physics and informatics\, including deep learning. We take similar approaches to predict interactio ns of proteins with other proteins or small ligands including short peptid es and oligosaccharides. Our goal is to develop protein structure modeling techniques that can provide useful predictions even in the absence of ava ilable information\, although currently available experimental data would play important roles in developing such techniques. Such modeling methods would be very useful for applications to a wide range of biomedical resear ch and drug discovery.\n\nhttps://sshep.snu.ac.kr/event/107/contributions/ 28/ 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/28/ END:VEVENT BEGIN:VEVENT SUMMARY:Fleming : AI-driven Integrated Drug Discovery Platform DTSTART;VALUE=DATE-TIME:20191107T051000Z DTEND;VALUE=DATE-TIME:20191107T053000Z DTSTAMP;VALUE=DATE-TIME:20260724T171406Z UID:indico-contribution-10-30@sshep.snu.ac.kr DESCRIPTION:Speakers: Junsu Ko (© Arontier)\nNew drug development cost mo re than one billion over the last decade. And the chance of success was qu ite low\, one in five thousands. In order to make the drug development pro cess efficient in terms of time and cost\, Artificial Intelligence has bee n actively introduced to and rigorously adopted in the various fields of d rug development: compound activity prediction\, compound design\, patient selection\, and clinical trial design\, just to name a few.\n\nAs these fi elds become advanced\, more AI-based drug development platforms are develo ped and deployed in the fields to overcome data shortage.\n\nFleming is an integrated and automated platform for time and cost efficient drug develo pment. It uses protein structure prediction and genome-based target select ion for efficiency. In Fleming\, precise target protein structures\, gener ated by protein structure prediction techniques using AI\, are used to pic k candidate compounds and to design active compounds with high chance of s uccess. And genome-based analysis powered by Fleming AI selects (find-an-a djective-for-this) compounds by predicting compound activity and toxicity. \n\nThese features in Fleming accelerate the new drug development process utilizing and combining various technologies: disease genome analysis\, ca ndidate compound selection\, compound generation\, activity prediction\, a nd toxicity prediction.\n\nhttps://sshep.snu.ac.kr/event/107/contributions /30/ 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/30/ END:VEVENT BEGIN:VEVENT 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:20260724T171406Z UID:indico-contribution-10-31@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/ END:VEVENT END:VCALENDAR