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SMILES Enumeration as Data Augmentation for Neural Network Modeling of
  Molecules

SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules

21 March 2017
E. Bjerrum
ArXivPDFHTML

Papers citing "SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules"

26 / 26 papers shown
Title
Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTM
Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTM
Collin Beaudoin
Swaroop Ghosh
36
0
0
29 Apr 2025
MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design
MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design
Zimo Yan
Jie Zhang
Zheng Xie
Chang-rui Liu
Yong-Jin Liu
Yiping Song
36
0
0
22 Apr 2025
Language models in molecular discovery
Language models in molecular discovery
Chaoqi Wang
Yibo Jiang
Chenghao Yang
Han Liu
Yuxin Chen
23
7
0
28 Sep 2023
Recent advances in artificial intelligence for retrosynthesis
Recent advances in artificial intelligence for retrosynthesis
Zipeng Zhong
Jie Song
Zunlei Feng
Tiantao Liu
Lingxiang Jia
Shaolun Yao
Tingjun Hou
Mingli Song
29
5
0
14 Jan 2023
Deep learning methods for drug response prediction in cancer:
  predominant and emerging trends
Deep learning methods for drug response prediction in cancer: predominant and emerging trends
A. Partin
Thomas Brettin
Yitan Zhu
Oleksandr Narykov
Austin R. Clyde
Jamie Overbeek
Department of Materials Science
6
54
0
18 Nov 2022
Investigation of chemical structure recognition by encoder-decoder
  models in learning progress
Investigation of chemical structure recognition by encoder-decoder models in learning progress
Katsuhisa Morita
T. Mizuno
Hiroyuki Kusuhara
19
8
0
24 Oct 2022
Faster and more diverse de novo molecular optimization with double-loop
  reinforcement learning using augmented SMILES
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES
E. Bjerrum
Christian Margreitter
Thomas Blaschke
Raquel Lopez-Rios de Castro
32
11
0
22 Oct 2022
A smile is all you need: Predicting limiting activity coefficients from
  SMILES with natural language processing
A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing
Benedikt Winter
Clemens Winter
J. Schilling
A. Bardow
25
28
0
15 Jun 2022
MolGenSurvey: A Systematic Survey in Machine Learning Models for
  Molecule Design
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design
Yuanqi Du
Tianfan Fu
Jimeng Sun
Shengchao Liu
AI4CE
31
86
0
28 Mar 2022
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction
  Representation
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation
Mohammadamin Tavakoli
Alexander Shmakov
Francesco Ceccarelli
Pierre Baldi
GNN
33
8
0
02 Jan 2022
AugLiChem: Data Augmentation Library of Chemical Structures for Machine
  Learning
AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning
Rishikesh Magar
Yuyang Wang
Cooper Lorsung
Chen Liang
Hariharan Ramasubramanian
Peiyuan Li
A. Farimani
28
27
0
30 Nov 2021
Permutation invariant graph-to-sequence model for template-free
  retrosynthesis and reaction prediction
Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction
Zhengkai Tu
Connor W. Coley
30
90
0
19 Oct 2021
Large-Scale Chemical Language Representations Capture Molecular
  Structure and Properties
Large-Scale Chemical Language Representations Capture Molecular Structure and Properties
Jerret Ross
Brian M. Belgodere
Vijil Chenthamarakshan
Inkit Padhi
Youssef Mroueh
Payel Das
AI4CE
27
272
0
17 Jun 2021
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph
  Generative Models for Therapeutic Candidates
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates
Jenna A. Bilbrey
Logan T. Ward
Sutanay Choudhury
Neeraj Kumar
Ganesh Sivaraman
24
1
0
07 May 2021
TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention
  Networks
TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks
Anna Weber
Jannis Born
María Rodríguez Martínez
11
129
0
21 Apr 2021
Benchmarking Deep Graph Generative Models for Optimizing New Drug
  Molecules for COVID-19
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19
Logan T. Ward
Jenna A. Bilbrey
Sutanay Choudhury
Neeraj Kumar
Ganesh Sivaraman
GNN
27
3
0
09 Feb 2021
Advanced Graph and Sequence Neural Networks for Molecular Property
  Prediction and Drug Discovery
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
Zhengyang Wang
Meng Liu
Youzhi Luo
Zhao Xu
Yaochen Xie
...
Lei Cai
Q. Qi
Zhuoning Yuan
Tianbao Yang
Shuiwang Ji
36
100
0
02 Dec 2020
Predicting Chemical Properties using Self-Attention Multi-task Learning
  based on SMILES Representation
Predicting Chemical Properties using Self-Attention Multi-task Learning based on SMILES Representation
Sangrak Lim
Yong Oh Lee
19
17
0
19 Oct 2020
Mixup-breakdown: a consistency training method for improving
  generalization of speech separation models
Mixup-breakdown: a consistency training method for improving generalization of speech separation models
Max W. Y. Lam
Jun Wang
Dan Su
Dong Yu
33
22
0
28 Oct 2019
SMILES-X: autonomous molecular compounds characterization for small
  datasets without descriptors
SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors
G. Lambard
Ekaterina Gracheva
24
20
0
20 Jun 2019
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation
  Models
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Daniil Polykovskiy
Alexander Zhebrak
Benjamín Sánchez-Lengeling
Sergey Golovanov
Oktai Tatanov
...
Simon Johansson
Hongming Chen
Sergey I. Nikolenko
Alán Aspuru-Guzik
Alex Zhavoronkov
ELM
194
633
0
29 Nov 2018
KekuleScope: prediction of cancer cell line sensitivity and compound
  potency using convolutional neural networks trained on compound images
KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images
I. Cortés-Ciriano
A. Bender
MedIm
27
51
0
22 Nov 2018
Molecular Transformer - A Model for Uncertainty-Calibrated Chemical
  Reaction Prediction
Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction
P. Schwaller
Teodoro Laino
John McGuinness
A. Horváth
Constantine Bekas
A. Lee
25
719
0
06 Nov 2018
Multimodal Deep Neural Networks using Both Engineered and Learned
  Representations for Biodegradability Prediction
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction
Garrett B. Goh
Khushmeen Sakloth
Charles Siegel
Abhinav Vishnu
J. Pfaendtner
HAI
28
11
0
13 Aug 2018
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for
  Transferable Chemical Property Prediction
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction
Garrett B. Goh
Charles Siegel
Abhinav Vishnu
Nathan Oken Hodas
18
90
0
07 Dec 2017
Retrosynthetic reaction prediction using neural sequence-to-sequence
  models
Retrosynthetic reaction prediction using neural sequence-to-sequence models
Bowen Liu
Bharath Ramsundar
Prasad Kawthekar
Jade Shi
Joseph Gomes
Quang Luu Nguyen
Stephen Ho
Jack L. Sloane
P. Wender
Vijay S. Pande
20
411
0
06 Jun 2017
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