| 학회 | 한국화학공학회 |
| 학술대회 | 2022년 봄 (04/20 ~ 04/23, 제주국제컨벤션센터) |
| 권호 | 28권 1호, p.115 |
| 발표분야 | [주제 2] 기계학습 |
| 제목 | Deep learning-based molecular design |
| 초록 | The ultimate goal of chemistry is to make new molecules with desired properties. It is challenging because chemical space is very large and discrete with a wide variety of molecules. For example, there are only 108 molecules synthesized as potential drug candidates, but 1060 molecules are estimated to be existing. High-throughput virtual screening approach has attracted great attention but still requires large costs and time. In this talk, we propose to use deep learning as an alternative approach. It is specialized in controlling multiple molecular properties simultaneously, embedding them in namely the latent space. As a proof of concept, we will show that it can be used to generate molecules as drugs with specific properties. We also apply it to the design of new OLED molecules with controlled electronic properties. keywords: Molecular design, Deep learning, Generative model, drug, OLED |
| 저자 | 김우연 |
| 소속 | KAIST |
| 키워드 | 생물화공(Biochemical Engineering) |
| 원문파일 | 초록 보기 |
