Jaechang Lim
- Molecular generative model based on conditional variational autoencoder\n for de novo molecular design
2018/06/15 by Jaechang Lim, Seongok Ryu, Lim, Jaechang +5 · 7 citations
Computer Science · #Computational Drug Discovery Methods #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions
2020/08/22 by Seokhyun Moon, Moon, Seokhyun, Wonho Zhung +7 · 6 citations
Computer Science · Materials Science · Biochemistry, Genetics and Molecular Biology · #Computational Drug Discovery Methods #Machine Learning in Materials Science #Protein Structure and Dynamics
- PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening
2023/07/03 by Seokhyun Moon, Moon, Seokhyun, Sang-Yeon Hwang +5 · 2 citations
Computer Science · Materials Science · Biochemistry, Genetics and Molecular Biology · #Computational Drug Discovery Methods #Machine Learning in Materials Science #Protein Structure and Dynamics
- Fragment-based molecular generative model with high generalization ability and synthetic accessibility
2021/11/25 by Seonghwan Seo, Seo, Seonghwan, Jaechang Lim +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Computational Drug Discovery Methods #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Protein Structure and Dynamics
- Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network
2018/05/28 by Seongok Ryu, Jaechang Lim, Ryu, Seongok +5 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Computational Drug Discovery Methods #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Protein Structure and Dynamics