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Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks

Journal of chemical information and modeling, 2020-01, Vol.60 (1), p.47-55

2020

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Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map

Journal of cheminformatics, 2021-02, Vol.13 (1), p.7-7, Article 7

2021

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Deep scaffold hopping with multimodal transformer neural networks

Journal of cheminformatics, 2021-11, Vol.13 (1), p.87-87, Article 87

2021

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Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP

Nature communications, 2022-06, Vol.13 (1), p.3342-3342, Article 3342

2022

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QBMG: quasi-biogenic molecule generator with deep recurrent neural network

Journal of cheminformatics, 2019-01, Vol.11 (1), p.5-12, Article 5

2019

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A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions

Nature communications, 2024-05, Vol.15 (1), p.4476-4476, Article 4476

2024

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DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model

Nature communications, 2024-02, Vol.15 (1), p.1071-1071, Article 1071

2024

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Amelioration of Alzheimer's disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow

Nature biomedical engineering, 2022-01, Vol.6 (1), p.76

2022

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Developing BioNavi for Hybrid Retrosynthesis Planning

JACS Au, 2024-07, Vol.4 (7), p.2492-2502

2024

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Identifying Structure–Property Relationships through SMILES Syntax Analysis with Self-Attention Mechanism

Journal of chemical information and modeling, 2019-02, Vol.59 (2), p.914-923

2019

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SyntaLinker: automatic fragment linking with deep conditional transformer neural networks

Chemical science (Cambridge), 2020-08, Vol.11 (31), p.8312-8322

2020

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Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis

IEEE transactions on affective computing, 2023-07, Vol.14 (3), p.2276-2289

2023

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Subgraph-Aware Few-Shot Inductive Link Prediction Via Meta-Learning

IEEE transactions on knowledge and data engineering, 2023-06, Vol.35 (6), p.6512-6517

2023

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Self-Supervised Contrastive Molecular Representation Learning with a Chemical Synthesis Knowledge Graph

Journal of chemical information and modeling, 2024-03, Vol.64 (6), p.1945-1954

2024

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Meta Learning for Low-Resource Molecular Optimization

Journal of chemical information and modeling, 2021-04, Vol.61 (4), p.1627-1636

2021

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AlphaFold2-aware protein–DNA binding site prediction using graph transformer

Briefings in bioinformatics, 2022-03, Vol.23 (2)

2022

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Precise estimation of residue relative solvent accessible area from Cα atom distance matrix using a deep learning method

Bioinformatics, 2021-12, Vol.38 (1), p.94-98

2021

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DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design

Journal of chemical information and modeling, 2022-12, Vol.62 (23), p.5907-5917

2022

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PharmKG: a dedicated knowledge graph benchmark for bomedical data mining

Briefings in bioinformatics, 2021-07, Vol.22 (4)

2021

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Structure-Aware Multimodal Deep Learning for Drug–Protein Interaction Prediction

Journal of chemical information and modeling, 2022-03, Vol.62 (5), p.1308-1317

2022

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Quantitative evaluation of explainable graph neural networks for molecular property prediction

Patterns (New York, N.Y.), 2022-12, Vol.3 (12), p.100628-100628, Article 100628

2022

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Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches

Journal of chemical information and modeling, 2021-10, Vol.61 (10), p.4900-4912

2021

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Integrating supercomputing and artificial intelligence for life science

Patterns (New York, N.Y.), 2022-12, Vol.3 (12), p.100653-100653, Article 100653

2022

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Deep learning enables discovery of highly potent anti-osteoporosis natural products

European journal of medicinal chemistry, 2021-01, Vol.210, p.112982, Article 112982

2021

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To Improve Prediction of Binding Residues With DNA, RNA, Carbohydrate, and Peptide Via Multi-Task Deep Neural Networks

IEEE/ACM transactions on computational biology and bioinformatics, 2022-11, Vol.19 (6), p.3735-3743

2022

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Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks

Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map

Deep scaffold hopping with multimodal transformer neural networks

Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP

QBMG: quasi-biogenic molecule generator with deep recurrent neural network

A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions

DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model

Amelioration of Alzheimer's disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow

Developing BioNavi for Hybrid Retrosynthesis Planning

Identifying Structure–Property Relationships through SMILES Syntax Analysis with Self-Attention Mechanism

SyntaLinker: automatic fragment linking with deep conditional transformer neural networks

Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis

Subgraph-Aware Few-Shot Inductive Link Prediction Via Meta-Learning

Self-Supervised Contrastive Molecular Representation Learning with a Chemical Synthesis Knowledge Graph

Meta Learning for Low-Resource Molecular Optimization

AlphaFold2-aware protein–DNA binding site prediction using graph transformer

Precise estimation of residue relative solvent accessible area from Cα atom distance matrix using a deep learning method

DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design

PharmKG: a dedicated knowledge graph benchmark for bomedical data mining

Structure-Aware Multimodal Deep Learning for Drug–Protein Interaction Prediction

Quantitative evaluation of explainable graph neural networks for molecular property prediction

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches

Integrating supercomputing and artificial intelligence for life science

Deep learning enables discovery of highly potent anti-osteoporosis natural products

To Improve Prediction of Binding Residues With DNA, RNA, Carbohydrate, and Peptide Via Multi-Task Deep Neural Networks

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