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。Conditional Molecular Design with Deep Generative Models
Seokho Kang et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
Two Decades under the Influence of the Rule of Five and the Changing Properties of Approved Oral Drugs
Michael D. Shultz
JOURNAL OF MEDICINAL CHEMISTRY (2019)
GuacaMol: Benchmarking Models for de Novo Molecular Design
Nathan Brown et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Alex Zhavoronkov et al.
NATURE BIOTECHNOLOGY (2019)
Identification of Novel Antibacterials Using Machine Learning Techniques
Yan A. Ivanenkov et al.
FRONTIERS IN PHARMACOLOGY (2019)
Randomized SMILES strings improve the quality of molecular generative models
Josep Arus-Pous et al.
JOURNAL OF CHEMINFORMATICS (2019)
Artificial intelligence for aging and longevity research: Recent advances and perspectives
Alex Zhavoronkov et al.
AGEING RESEARCH REVIEWS (2019)
Virtual Compound Libraries in Computer-Assisted Drug Discovery
Niek van Hilten et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
A de novo molecular generation method using latent vector based generative adversarial network
Oleksii Prykhodko et al.
JOURNAL OF CHEMINFORMATICS (2019)
Application of Generative Autoencoder in De Novo Molecular Design
Thomas Blaschke et al.
MOLECULAR INFORMATICS (2018)
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Marwin H. S. Segler et al.
ACS CENTRAL SCIENCE (2018)
Designing Anticancer Peptides by Constructive Machine Learning
Francesca Grisoni et al.
CHEMMEDCHEM (2018)
Opportunities and obstacles for deep learning in biology and medicine
Travers Ching et al.
JOURNAL OF THE ROYAL SOCIETY INTERFACE (2018)
De Novo Design of Bioactive Small Molecules by Artificial Intelligence
Daniel Merk et al.
MOLECULAR INFORMATICS (2018)
Adversarial Threshold Neural Computer for Molecular de Novo Design
Evgeny Putin et al.
MOLECULAR PHARMACEUTICS (2018)
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu et al.
CHEMICAL SCIENCE (2018)
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
Su-In Lee et al.
NATURE COMMUNICATIONS (2018)
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Rafael Gomez-Bombarelli et al.
ACS CENTRAL SCIENCE (2018)
Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification
Polina Mamoshina et al.
FRONTIERS IN GENETICS (2018)
Frechet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery
Kristina Preuer et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2018)
Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery
Daniil Polykovskiy et al.
MOLECULAR PHARMACEUTICS (2018)
Inverse molecular design using machine learning: Generative models for matter engineering
Benjamin Sanchez-Lengeling et al.
SCIENCE (2018)
Deep reinforcement learning for de novo drug design
Mariya Popova et al.
SCIENCE ADVANCES (2018)
Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators
Daniel Merk et al.
COMMUNICATIONS CHEMISTRY (2018)
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology
Artur Kadurin et al.
ONCOTARGET (2017)
druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico
Artur Kadurin et al.
MOLECULAR PHARMACEUTICS (2017)
ChemTS: an efficient python library for de novo molecular generation
Xiufeng Yang et al.
SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS (2017)
Molecular de-novo design through deep reinforcement learning
Marcus Olivecrona et al.
JOURNAL OF CHEMINFORMATICS (2017)
Design of efficient computational workflows for in silico drug repurposing
Quentin Vanhaelen et al.
DRUG DISCOVERY TODAY (2017)
Discovery and Optimization of Materials Using Evolutionary Approaches
Tu C. Le et al.
CHEMICAL REVIEWS (2016)
Applications of Deep Learning in Biomedicine
Polina Mamoshina et al.
MOLECULAR PHARMACEUTICS (2016)
Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data
Alexander Aliper et al.
MOLECULAR PHARMACEUTICS (2016)
The Chemical Space Project
Jean-Louis Reymond
ACCOUNTS OF CHEMICAL RESEARCH (2015)
What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery
Edward O. Pyzer-Knapp et al.
ANNUAL REVIEW OF MATERIALS RESEARCH, VOL 45 (2015)
ZINC 15-Ligand Discovery for Everyone
Teague Sterling et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2015)
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan et al.
SCIENTIFIC DATA (2014)
The high-throughput highway to computational materials design
Stefano Curtarolo et al.
NATURE MATERIALS (2013)
Quantifying the chemical beauty of drugs
G. Richard Bickerton et al.
NATURE CHEMISTRY (2012)
Extended-Connectivity Fingerprints
David Rogers et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)
New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays
Jonathan B. Baell et al.
JOURNAL OF MEDICINAL CHEMISTRY (2010)
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl et al.
JOURNAL OF CHEMINFORMATICS (2009)
Emergent strategies for inverse molecular design
Hu XiangQian et al.
SCIENCE IN CHINA SERIES B-CHEMISTRY (2009)
On the Art of Compiling and Using 'Drug-Like' Chemical Fragment Spaces
Joerg Degen et al.
CHEMMEDCHEM (2008)