1 서상민, "딥러닝을 이용한 화합물-단백질 상호작용 예측" 한국정보과학회 46 (46): 1054-1060, 2019
2 B. K. Shoichet, "Virtual screening of chemical libraries" 432 : 862-865, 2004
3 N. Jaques, "Tuning recurrent neural networks with reinforcement learning"
4 M. Manica, "Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders" 2019
5 H. Chen, "The rise of deep learning in drug discovery" 23 : 1241-1250, 2018
6 A. P. Bento, "The ChEMBL bioactivity database: an update" 42 : D1083-D1090, 2014
7 T. Cheng, "Structure-based virtual screening for drug discovery: a problem-centric review" 14 : 133-141, 2012
8 G. Jinesh G, "Smac mimetic enables the anticancer action of BCG‐stimulated neutrophils through TNF‐α but not through TRAIL and FasL" 92 : 233-244, 2012
9 R. J. Williams, "Simple statistical gradient-following algorithms for connectionist reinforcement learning" 8 : 229-256, 1992
10 L. Yu, "Seqgan:Sequence generative adversarial nets with policy gradient" 2017
1 서상민, "딥러닝을 이용한 화합물-단백질 상호작용 예측" 한국정보과학회 46 (46): 1054-1060, 2019
2 B. K. Shoichet, "Virtual screening of chemical libraries" 432 : 862-865, 2004
3 N. Jaques, "Tuning recurrent neural networks with reinforcement learning"
4 M. Manica, "Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders" 2019
5 H. Chen, "The rise of deep learning in drug discovery" 23 : 1241-1250, 2018
6 A. P. Bento, "The ChEMBL bioactivity database: an update" 42 : D1083-D1090, 2014
7 T. Cheng, "Structure-based virtual screening for drug discovery: a problem-centric review" 14 : 133-141, 2012
8 G. Jinesh G, "Smac mimetic enables the anticancer action of BCG‐stimulated neutrophils through TNF‐α but not through TRAIL and FasL" 92 : 233-244, 2012
9 R. J. Williams, "Simple statistical gradient-following algorithms for connectionist reinforcement learning" 8 : 229-256, 1992
10 L. Yu, "Seqgan:Sequence generative adversarial nets with policy gradient" 2017
11 D. P. Kingma, "Semi-supervised learning with deep generative models" 3581-3589, 2014
12 D. Weininger, "SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules" 28 : 31-36, 1988
13 T. Scior, "Recognizing pitfalls in virtual screening: a critical review" 52 : 867-881, 2012
14 Y.-Y. Li, "Pim-3, a proto-oncogene with serine/threonine kinase activity, is aberrantly expressed in human pancreatic cancer and phosphorylates bad to block bad-mediated apoptosis in human pancreatic cancer cell lines" 66 : 6741-6747, 2006
15 J. Born, "PaccMannRL:Designing anticancer drugs from transcriptomic data via reinforcement learning"
16 J. Lim, "Molecular generative model based on conditional variational autoencoder for de novo molecular design" 10 : 1-9, 2018
17 M. Olivecrona, "Molecular de-novo design through deep reinforcement learning" 9 : 48-, 2017
18 S. Hochreiter, "Long short-term memory" 9 : 1735-1780, 1997
19 A. Joulin, "Inferring algorithmic patterns with stack-augmented recurrent nets" 190-198, 2015
20 W. K. Chan, "GLASS: a comprehensive database for experimentally validated GPCR-ligand associations" 31 : 3035-3042, 2015
21 C. A. Lipinski, "Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings" 23 : 3-25, 1997
22 R. Erber, "EphB4 controls blood vascular morphogenesis during postnatal angiogenesis" 25 : 628-641, 2006
23 C. Knox, "DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs" 39 : D1035-D1041, 2010
24 M. Popova, "Deep reinforcement learning for de novo drug design" 4 : eaap7885-, 2018
25 J. Reymond, "Chemical space as a source for new drugs" 1 (1): 30-38, 2010
26 M. K. Gilson, "BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology" 44 : D1045-D1053, 2016
27 R. Gómez-Bombarelli, "Automatic chemical design using a data-driven continuous representation of molecules" 4 : 268-276, 2018
28 P. Baldi, "Autoencoders, unsupervised learning, and deep architectures" 37-49, 2012
29 D. P. Kingma, "Auto-encoding variational bayes"
30 C. Fujii, "Aberrant expression of serine/threonine kinase Pim‐3 in hepatocellular carcinoma development and its role in the proliferation of human hepatoma cell lines" 114 : 209-218, 2005
31 X. Lin, "A review on applications of computational methods in drug screening and design" 25 : 1375-, 2020