This event is invitation-based.
The First Joint Mini Workshop on Chemical Graph Learning
Learning is one of the fundamental activities of lives. Recent development of advanced tools such as Artificial Intelligence (AI) is promising to help human society reveal "good" (i.e., low-entropy) information from huge data (of high entropy) that may be difficult or even impossible for a human to find. Focusing on chemical graph learning, the purpose of this mini workshop is to exchange research results and ideas from each of the labs and researchers and motivate further collaboration toward better understanding on learning chemical graphs and its applications in drug discovery and others.
- Time/Date: Monday, 2pm-5:30pm, January 29th, 2024
- Place: Room 025, Higashiichijo-Kan, Kyoto University (see access)
- Language: English and Japanese (both are OK)
- Organization: Du lab @ KIT; Haraguchi Lab @ KU; Prof. Han @ KU; Prof. Naveed @ QAU, Pak; Zhao Lab @ KU
- Coordinator: Li, Yanghepu @ Zhao Lab
- Flyer
Program
| Time (Q&A included) | Speaker | Title |
|---|---|---|
| 14:00 - 14:05 | Liang Zhao | Opening speech |
| 14:05 - 14:35 | 成田 光伸 (Narita, Koshin) | フラグメント分割と化学構造記法を用いた化合物の共通部分構造抽出 |
| 14:35 - 15:05 | 碓井 響子 (Usui, Kyoko) | フラグメント分割による化合物の近似共通部分構造の探索 |
| 15:05 - 15:35 | TAKEKIDA, Mao | Inference of Chemical Compounds with Desired Multiple Chemical Properties by Integer Programming and Machine Learning |
| 15:35 - 15:50 | Break | |
| 15:50 - 16:20 | Yanghepu Li | De novo Drug Design against SARS-CoV-2 Protein Targets using SMILES-based Deep Reinforcement Learning |
| 16:20 - 16:50 | AZAM, Naveed Ahmed | Inferring Compounds With Given Aqueous Solubility |
| 16:50 - 17:00 | Group photo | |
| 17:00 - 17:30 | Meeting |
