Molecular-team-study
Homepage : https://molecular-ai-team.netlify.app
Lab homepage : http://mailab.korea.ac.kr/
Introduction (Our molecular AI study)
- molecular AI study 세미나 및 공부자료 저장소입니다.
- Deep learning for molecular design—a review of the state of the art (2019) [Paper]
- Artificial Intelligence for Autonomous Molecular Design: A Perspective (Molecules, 2021) [paper]
- Artificial Intelligence in Drug Discovery: Applications and Techniques (arXiv, 2021) [paper]
- Drug discovery with explainable artificial intelligence (nature machine intelligence, 2021) [paper]
- Machine learning for chemical discovery (nature communications, 2021) [paper]
- Machine Learning Methods in Drug Discovery (Molecules, 2020) [paper]
- Comprehensive Survey of Recent Drug Discovery Using Deep Learning (International Journal of Molecular Sciences, 2021) [paper]
| Day | Title | Conference or Journal | Member | Youtube | Paper | |
|---|---|---|---|---|---|---|
| 2022.03.10 | MT_study 1차, MT_study 2차, MT_study 3차 | 한지웅 | 1차 2차 3차 | |||
| 2022.03. | (MPNN) Neural Message Passing for Quantum Chemistry | ICML 2017 | 이덕중 | [PDF] | [paper] | |
| 2022.04.12 | Multi-Objective Molecule Generation using Interpretable Substructures | ICML 2020 | 신동희 | |||
| 2022.04.19 | Hierarchical Generation of Molecular Graphs using Structural Motifs | ICML 2020 | 손영한 | |||
| 2022.04.26 | Improving Generalization in Meta-learning via Task Augmentation | ICML 2021 | 한지웅 | |||
| 2022.05.03 | GRAND: Graph Neural Diffusion | ICML 2021 | 신동희 | |||
| 2022.05.17 | Equivariant Subgraph Aggregation Networks | ICLR 2022 | 손영한 | |||
| 2022.05.24 | improving molecular design by stochastic iterative target augmentation | ICML 2020 | 한지웅 | |||
| 2022.05.31 | 1.Motif-based Graph Self-Supervised Learning for Molecular Property Prediction. 2.Do Transformers Really Perform Badly for Graph Representation? |
NIPS 2021 | 이덕중 | |||
| 2022.06.14,21 | ITERATIVE REFINEMENT GRAPH NEURAL NETWORK FOR ANTIBODY SEQUENCE-STRUCTURE CO-DESIGN | ICLR 2022 | 한지웅 | |||
| 2022.06.28 | Denoising Diffusion Probabilistic Models | NIPS 2022 | 신동희 | [PDF] | paper | |
| 2022.07.20 | Independent SE(3)-Equivariant Models for End-To-End Rigid Protein Docking | ICLR 2022 | 손영한 | [PDF] | [paper] | |
| 2022.08.12 | TOPOLOGICAL GRAPH NEURAL NETWORKS | ICLR 2022 | 이덕중 | [PDF] | [paper] | |
| 2022.09.02 | Model agnostic generation of counterfactual explanations for molecules | Chemical Science 2022 | 한지웅 | [PDF] | [paper] | |
| 2022.09.07 | Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning | ICML 2020 | 신동희 | [PDF] | [paper] | |
| 2022.09.19 | 3DLinker, An E(3) Equivariant Variational Autoencoder for Molecular Linker Design | ICML 2022 | 손영한 | [PDF] | [paper] | |
| 2022.09.26 | Generative Coarse-Graining of Molecular Conformations | ICML 2022 | 이덕중 | [PDF] | [paper] | |
| 2022.10.04 | DeepGraphGo, Graph Neural Network for Large-Scale, Multispecies Protein Function Prediction | ISMB 2022 | 한지웅 | [PDF] | [paper] | |
| 2022.10.11 | GEOMOL- Torsional Geometric Generation of Molecular 3D Conformer Ensembles | NIPS 2021 | 신동희 | [PDF] | [paper] | |
| 2022.10.17 | 3D Infomax improves GNNs for Molecular Property Prediction | ICML 2021 | 손영한 | [PDF] | [paper] | |
| 2022.10.24 | 이덕중 | [PDF] | [paper] |
- Graph Property Prediction (benchmark dataset) [URL]
- RDKit (package) [URL]
- MoleculeNet (Benchmark dataset) [URL]
- ZINC15 (Benchmark dataset) [URL]
- MIT Lecture [Youtube] [Lec]
- AAAI 2021 Drug discovery (Mila, Weill Cornell Medicine, Cleveland Clinic) [Youtube] [Homepage]
- Mila (Company) [Homepage]
- MIT Tommi S. Jaakkola (Lab) [Homepage]
- valence drug discovery (Company) [Homepage]
- DeepMind (Company) [Homepage]
- Isomorphic Laboratories (Company) [Homepage]
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