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。Different molecular enumeration influences in deep learning: an example using aqueous solubility
Jen-Hao Chen et al.
BRIEFINGS IN BIOINFORMATICS (2021)
A Survey of the Usages of Deep Learning for Natural Language Processing
Daniel W. Otter et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)
Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations
Payel Das et al.
NATURE BIOMEDICAL ENGINEERING (2021)
PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning
Jannis Born et al.
ISCIENCE (2021)
Combining Docking Pose Rank and Structure with Deep Learning Improves Protein-Ligand Binding Mode Prediction over a Baseline Docking Approach
Joseph A. Morrone et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)
Generative Network Complex for the Automated Generation of Drug-like Molecules
Kaifu Gao et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation
Mario Krenn et al.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)
QSAR without borders
Eugene N. Muratov et al.
CHEMICAL SOCIETY REVIEWS (2020)
In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening
Jochen Sieg et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping
Boris Sattarov et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
GuacaMol: Benchmarking Models for de Novo Molecular Design
Nathan Brown et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
Ultra-large library docking for discovering new chemotypes
Jiankun Lyu et al.
NATURE (2019)
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Alex Zhavoronkov et al.
NATURE BIOTECHNOLOGY (2019)
Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening
Lieyang Chen et al.
PLOS ONE (2019)
Deep Convolutional Generative Adversarial Network (dcGAN) Models for Screening and Design of Small Molecules Targeting Cannabinoid Receptors
Yuemin Bian et al.
MOLECULAR PHARMACEUTICS (2019)
Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders
Matteo Manica et al.
MOLECULAR PHARMACEUTICS (2019)
Randomized SMILES strings improve the quality of molecular generative models
Josep Arus-Pous et al.
JOURNAL OF CHEMINFORMATICS (2019)
The importance of synthetic chemistry in the pharmaceutical industry
Kevin R. Campos et al.
SCIENCE (2019)
Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
Robin Winter et al.
CHEMICAL SCIENCE (2019)
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Rafael Gomez-Bombarelli et al.
ACS CENTRAL SCIENCE (2018)
Multi-objective de novo drug design with conditional graph generative model
Yibo Li et al.
JOURNAL OF CHEMINFORMATICS (2018)
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
Martin Simonovsky et al.
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT I (2018)
Profile-QSAR 2.0: Kinase Virtual Screening Accuracy Comparable to Four-Concentration IC50s for Realistically Novel Compounds
Eric J. Martin et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)
Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
Connor W. Coley et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)
Low Data Drug Discovery with One-Shot Learning
Han Altae-Tran et al.
ACS CENTRAL SCIENCE (2017)
Quantifying the chemical beauty of drugs
G. Richard Bickerton et al.
NATURE CHEMISTRY (2012)
Strategies to improve in vivo toxicology outcomes for basic candidate drug molecules
Tim Luker et al.
BIOORGANIC & MEDICINAL CHEMISTRY LETTERS (2011)
Extended-Connectivity Fingerprints
David Rogers et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)
Pharmacological Promiscuity: Dependence on Compound Properties and Target Specificity in a Set of Recent Roche Compounds
Jens-Uwe Peters et al.
CHEMMEDCHEM (2009)
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl et al.
JOURNAL OF CHEMINFORMATICS (2009)
分享论文
