Knowledge graph-enhanced molecular contrastive learning with functional prompt
暂无分享,去创建一个
Xin Shao | Ningyu Zhang | Huajun Chen | Qiang Zhang | Zhuo Chen | Yin Fang | Xiang Zhuang | Xiaohui Fan | Zhuang Xiang | Zhuo Chen
[1] M. Rosales-Hernández,et al. New compounds from heterocyclic amines scaffold with multitarget inhibitory activity on Aβ aggregation, AChE, and BACE1 in the Alzheimer disease , 2022, PloS one.
[2] G. W. Vuister,et al. Fragment-Based Drug Discovery by NMR. Where Are the Successes and Where can It Be Improved? , 2022, Frontiers in Molecular Biosciences.
[3] Shengchao Liu,et al. Pre-training Molecular Graph Representation with 3D Geometry , 2021, ICLR.
[4] Fei Huang,et al. Learning to Ask for Data-Efficient Event Argument Extraction , 2021, AAAI.
[5] Gorka Labaka,et al. Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction , 2021, EMNLP.
[6] Hiroaki Hayashi,et al. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing , 2021, ACM Comput. Surv..
[7] Hua Wu,et al. Geometry-enhanced molecular representation learning for property prediction , 2021, Nature Machine Intelligence.
[8] Jiayu Zhou,et al. MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph , 2021, KDD.
[9] Amir Barati Farimani,et al. Molecular contrastive learning of representations via graph neural networks , 2021, Nature Machine Intelligence.
[10] F. Jourdan,et al. FORUM: building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases , 2021, bioRxiv.
[11] Ian Horrocks,et al. OWL2Vec*: embedding of OWL ontologies , 2020, Machine Learning.
[12] P. Ertl,et al. The Most Common Functional Groups in Bioactive Molecules and How Their Popularity has Evolved Over Time. , 2020, Journal of medicinal chemistry.
[13] Xiangxiang Zeng,et al. KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction , 2020, IJCAI.
[14] Sarit Kraus,et al. Constrained Policy Improvement for Efficient Reinforcement Learning , 2020, IJCAI.
[15] Phillip Isola,et al. Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere , 2020, ICML.
[16] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[17] J. Leskovec,et al. Strategies for Pre-training Graph Neural Networks , 2019, ICLR.
[18] Jamie Munro,et al. Trends in clinical success rates and therapeutic focus , 2019, Nature Reviews Drug Discovery.
[19] Regina Barzilay,et al. Are Learned Molecular Representations Ready For Prime Time? , 2019, ArXiv.
[20] Evan Bolton,et al. PubChem 2019 update: improved access to chemical data , 2018, Nucleic Acids Res..
[21] Jure Leskovec,et al. How Powerful are Graph Neural Networks? , 2018, ICLR.
[22] Jinfeng Yi,et al. Edge Attention-based Multi-Relational Graph Convolutional Networks , 2018, ArXiv.
[23] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[24] Vijay S. Pande,et al. MoleculeNet: a benchmark for molecular machine learning , 2017, Chemical science.
[25] Vijay S. Pande,et al. Computational Modeling of β-Secretase 1 (BACE-1) Inhibitors Using Ligand Based Approaches , 2016, J. Chem. Inf. Model..
[26] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[27] Geoffrey E. Hinton,et al. Layer Normalization , 2016, ArXiv.
[28] Peer Bork,et al. The SIDER database of drugs and side effects , 2015, Nucleic Acids Res..
[29] John J. Irwin,et al. ZINC 15 – Ligand Discovery for Everyone , 2015, J. Chem. Inf. Model..
[30] Yoshua Bengio,et al. On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.
[31] David L. Mobley,et al. FreeSolv: a database of experimental and calculated hydration free energies, with input files , 2014, Journal of Computer-Aided Molecular Design.
[32] Michael Hay,et al. Clinical development success rates for investigational drugs , 2014, Nature Biotechnology.
[33] John P. Overington,et al. ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..
[34] Dirk P. Kroese,et al. Kernel density estimation via diffusion , 2010, 1011.2602.
[35] T. Hartung. Toxicology for the twenty-first century , 2009, Nature.
[36] Lorenz C. Blum,et al. 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. , 2009, Journal of the American Chemical Society.
[37] Kaspar Riesen,et al. IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning , 2008, SSPR/SPR.
[38] P. Gunasekaran,et al. Toxicity assessment and microbial degradation of azo dyes. , 2006, Indian journal of experimental biology.
[39] G. Bemis,et al. The properties of known drugs. 1. Molecular frameworks. , 1996, Journal of medicinal chemistry.
[40] Laurens van der Maaten,et al. Accelerating t-SNE using tree-based algorithms , 2014, J. Mach. Learn. Res..
[41] R. Fitzpatrick. Haz-Map: information on hazardous chemicals and occupational diseases. , 2004, Medical reference services quarterly.