4.7 Article

Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

DMGAN: Adversarial Learning-Based Decision Making for Human-Level Plant-Wide Operation of Process Industries Under Uncertainties

Nianzu Zheng et al.

Summary: A decision-making framework based on generative adversarial networks was proposed to achieve plant-wide operational optimization by learning directly from operational data. Experimental results demonstrated the promising performance of the new method in plant operation.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

Article Biology

Improving influenza surveillance based on multi-granularity deep spatiotemporal neural network

Ruxin Wang et al.

Summary: Influenza is a common respiratory disease that poses a significant threat to public health. Accurate prediction of disease risk is crucial, and incorporating spatiotemporal information for risk prediction is a challenging task. The proposed end-to-end spatiotemporal deep neural network structure in this paper demonstrates outstanding performance for accurate influenza risk prediction.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Article Computer Science, Artificial Intelligence

Graph networks for molecular design

Rocio Mercado et al.

Summary: Deep learning methods applied to chemistry have accelerated the discovery of new molecules, with GraphINVENT as a platform using graph neural networks to design new molecules. The models in GraphINVENT can quickly learn to generate molecules resembling the training set molecules without explicit programming of chemical rules, and have been compared with state-of-the-art generative models using MOSES distribution-based metrics. The study found that the gated-graph neural network performs the best among the six different GNN-based generative models in GraphINVENT.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)

Article Chemistry, Medicinal

Automated De Novo Design in Medicinal Chemistry: Which Types of Chemistry Does a Generative Neural Network Learn?

Christoph Grebner et al.

JOURNAL OF MEDICINAL CHEMISTRY (2020)

Article Biochemical Research Methods

Deep Learning-driven research for drug discovery: Tackling Malaria

Bruno J. Neves et al.

PLOS COMPUTATIONAL BIOLOGY (2020)

Article Chemistry, Medicinal

Deep Generative Models for 3D Linker Design

Fergus Imrie et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

Editorial Material Biochemistry & Molecular Biology

Artificial intelligence in chemistry and drug design

Nathan Brown et al.

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2020)

Article Chemistry, Multidisciplinary

SMILES-based deep generative scaffold decorator for de-novo drug design

Josep Arus-Pous et al.

JOURNAL OF CHEMINFORMATICS (2020)

Article Computer Science, Artificial Intelligence

Dual Adversarial Autoencoders for Clustering

Pengfei Ge et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

Cascade architecture with rhetoric long short-term memory for complex sentence sentiment analysis

Chaojie Ji et al.

NEUROCOMPUTING (2020)

Article Computer Science, Information Systems

Deep Multi-Scale Fusion Neural Network for Multi-Class Arrhythmia Detection

Ruxin Wang et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2020)

Article Computer Science, Artificial Intelligence

Image-Based Model Parameter Optimization Using Model-Assisted Generative Adversarial Networks

Saul Alonso-Monsalve et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Article Pharmacology & Pharmacy

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Daniil Polykovskiy et al.

FRONTIERS IN PHARMACOLOGY (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Attention-Based Graph Evolution

Shuangfei Fan et al.

ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2020, PT I (2020)

Proceedings Paper Computer Science, Information Systems

GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation

Nikhil Goyal et al.

WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020) (2020)

Article Computer Science, Artificial Intelligence

Learning With Interpretable Structure From Gated RNN

Bo-Jian Hou et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Article Chemistry, Multidisciplinary

Scaffold-based molecular design with a graph generative model

Jaechang Lim et al.

CHEMICAL SCIENCE (2020)

Article Chemistry, Multidisciplinary

Constrained Bayesian optimization for automatic chemical design using variational autoencoders

Ryan-Rhys Griffiths et al.

CHEMICAL SCIENCE (2020)

Article Computer Science, Artificial Intelligence

Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks

Panagiotis-Christos Kotsias et al.

NATURE MACHINE INTELLIGENCE (2020)

Article Computer Science, Artificial Intelligence

Generative molecular design in low data regimes

Michael Moret et al.

NATURE MACHINE INTELLIGENCE (2020)

Article Chemistry, Medicinal

GuacaMol: Benchmarking Models for de Novo Molecular Design

Nathan Brown et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)

Article Chemistry, Medicinal

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design

Niclas Stahl et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)

Article Multidisciplinary Sciences

Optimization of Molecules via Deep Reinforcement Learning

Zhenpeng Zhou et al.

SCIENTIFIC REPORTS (2019)

Article Chemistry, Multidisciplinary

Randomized SMILES strings improve the quality of molecular generative models

Josep Arus-Pous et al.

JOURNAL OF CHEMINFORMATICS (2019)

Article Biochemistry & Molecular Biology

ChEMBL: towards direct deposition of bioassay data

David Mendez et al.

NUCLEIC ACIDS RESEARCH (2019)

Editorial Material Chemistry, Multidisciplinary

Advances and challenges in deep generative models for de novo molecule generation

Dongyu Xue et al.

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2019)

Article Chemistry, Multidisciplinary

A de novo molecular generation method using latent vector based generative adversarial network

Oleksii Prykhodko et al.

JOURNAL OF CHEMINFORMATICS (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Explainability Methods for Graph Convolutional Neural Networks

Phillip E. Pope et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)

Article Chemistry, Medicinal

Generative Recurrent Networks for De Novo Drug Design

Anvita Gupta et al.

MOLECULAR INFORMATICS (2018)

Article Chemistry, Medicinal

Application of Generative Autoencoder in De Novo Molecular Design

Thomas Blaschke et al.

MOLECULAR INFORMATICS (2018)

Article Chemistry, Multidisciplinary

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

Marwin H. S. Segler et al.

ACS CENTRAL SCIENCE (2018)

Article Chemistry, Medicinal

Reinforced Adversarial Neural Computer for de Novo Molecular Design

Evgeny Putin et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2018)

Article Chemistry, Medicinal

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

Daniel Merk et al.

MOLECULAR INFORMATICS (2018)

Article Medicine, Research & Experimental

Adversarial Threshold Neural Computer for Molecular de Novo Design

Evgeny Putin et al.

MOLECULAR PHARMACEUTICS (2018)

Article Chemistry, Multidisciplinary

Molecular generative model based on conditional variational autoencoder for de novo molecular design

Jaechang Lim et al.

JOURNAL OF CHEMINFORMATICS (2018)

Article Chemistry, Multidisciplinary

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Rafael Gomez-Bombarelli et al.

ACS CENTRAL SCIENCE (2018)

Article Biochemistry & Molecular Biology

Improving Chemical Autoencoder Latent Space and Molecular De Novo Generation Diversity with Heteroencoders

Esben Jannik Bjerrum et al.

BIOMOLECULES (2018)

Article Chemistry, Multidisciplinary

Molecular de-novo design through deep reinforcement learning

Marcus Olivecrona et al.

JOURNAL OF CHEMINFORMATICS (2017)

Article Economics

Innovation in the pharmaceutical industry: New estimates of R&D costs

Joseph A. DiMasi et al.

JOURNAL OF HEALTH ECONOMICS (2016)

Article Chemistry, Medicinal

ZINC 15-Ligand Discovery for Everyone

Teague Sterling et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2015)

Article Multidisciplinary Sciences

Human-level control through deep reinforcement learning

Volodymyr Mnih et al.

NATURE (2015)

Review Pharmacology & Pharmacy

Matched Molecular Pair Analysis in drug discovery

Alexander G. Dossetter et al.

DRUG DISCOVERY TODAY (2013)

Article Biochemistry & Molecular Biology

Estimation of the size of drug-like chemical space based on GDB-17 data

P. G. Polishchuk et al.

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2013)

Article Chemistry, Multidisciplinary

Quantifying the chemical beauty of drugs

G. Richard Bickerton et al.

NATURE CHEMISTRY (2012)

Article Chemistry, Medicinal

Matched Molecular Pairs as a Medicinal Chemistry Tool

Ed Griffen et al.

JOURNAL OF MEDICINAL CHEMISTRY (2011)

Article Chemistry, Medicinal

Extended-Connectivity Fingerprints

David Rogers et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)

Article Biotechnology & Applied Microbiology

How to improve R&D productivity: the pharmaceutical industry's grand challenge

Steven M. Paul et al.

NATURE REVIEWS DRUG DISCOVERY (2010)

Article Chemistry, Medicinal

ZINC - A free database of commercially available compounds for virtual screening

JJ Irwin et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2005)