Fetching the paper…
Reading the bibliography…
Functional groups (FGs) are molecular substructures that are served as a foundation for analyzing and predicting chemical properties of molecules.
Organic functional group analysis
F. E. Critchfield · 1963
Earlier work this paper cites.
Enzymatic synthesis and degradation of anandamide, a cannabinoid receptor agonist
D. G. Deutsch and S. A. Chin · 1993
Earlier work this paper cites.
Neural networks in chemistry
J. Gasteiger and J. Zupan · 1993
Earlier work this paper cites.
Neural networks in QSAR and drug design
J. Devillers · 1996
Earlier work this paper cites.
Computer prediction of possible toxic action from chemical structure: an update on the derek system
J. E. Ridings, M. D. Barratt, R. Cary, C. G. Earnshaw, C. E. Eggington, M. K. Ellis, P. N. Judson, J. J. Langowski, C. A. Marchant, M. P. Payne, et al · 1996
Earlier work this paper cites.
Artificial neural networks for computer-based molecular design
G. Schneider and P. Wrede · 1998
Earlier work this paper cites.
The effect of halogenation on blood–brain barrier permeability of a novel peptide drug☆
C. Gentry, R. Egleton, T. Gillespie, T. Abbruscato, H. Bechowski, V. Hruby, and T. Davis · 1999
Earlier work this paper cites.
Mass spectrometry and protein analysis
B. Domon and R. Aebersold · 2006
Earlier work this paper cites.
Developmental toxicity of 4-ring polycyclic aromatic hydrocarbons in zebrafish is differentially dependent on ah receptor isoforms and hepatic cytochrome p4501a metabolism
J. P. Incardona, H. L. Day, T. K. Collier, and N. L. Scholz · 2006
Earlier work this paper cites.
Fourier transform infrared spectroscopic analysis of protein secondary structures
J. Kong and S. Yu · 2007
Earlier work this paper cites.
Neural network for graphs: A contextual constructive approach
A. Micheli · 2009
Earlier work this paper cites.
Developing Β-secretase inhibitors for treatment of alzheimer’s disease
A. Ghosh, M. Brindisi, and T. J · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2013
Earlier work this paper cites.
Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules
A. Lusci, G. Pollastri, and P. Baldi · 2013
Earlier work this paper cites.
Estimation of the size of drug-like chemical space based on gdb-17 data
P. G. Polishchuk, T. I. Madzhidov, and A. Varnek · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Earlier work this paper cites.
Computational models to predict endocrine-disrupting chemical binding with androgen or oestrogen receptors
Y. Chen, F. Cheng, L. Sun, W. Li, G. Liu, and Y. Tang · 2014
Earlier work this paper cites.
Multi-task neural networks for qsar predictions
G. E. Dahl, N. Jaitly, and R. Salakhutdinov · 2014
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
A new in silico classification model for ready biodegradability, based on molecular fragments
A. Lombardo, F. Pizzo, E. Benfenati, A. Manganaro, T. Ferrari, and G. Gini · 2014
Cited alongside, same era.
Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
Cited alongside, same era.
I. Wallach, M. Dzamba, and A. Heifets · 2015
Cited alongside, same era.
The (un) reliability of saliency methods
P.-J. Kindermans, S. Hooker, J. Adebayo, M. Alber, K. T. Schütt, S. Dähne, D. Erhan, and B. Kim · 2017
Later among the works it cites.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
Later among the works it cites.
Retrosynthetic reaction prediction using neural sequence-to-sequence models
B. Liu, B. Ramsundar, P. Kawthekar, J. Shi, J. Gomes, Q. Luu Nguyen, S. Ho, J. Sloane, P. Wender, and V. Pande · 2017
Later among the works it cites.
Yolo9000: better, faster, stronger
J. Redmon and A. Farhadi · 2017
Later among the works it cites.
Evaluating the visualization of what a deep neural network has learned
W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K.-R. Müller · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Diffusion-convolutional neural networks
J. Atwood and D. Towsley · 2016
Cited alongside, same era.
Deep learning in drug discovery
E. Gawehn, J. A. Hiss, and G. Schneider · 2016
Cited alongside, same era.
Molecular graph convolutions: moving beyond fingerprints
S. Kearnes, K. McCloskey, M. Berndl, V. Pande, and P. Riley · 2016
Cited alongside, same era.
Robust neuroprotective effects of 2-((2-oxopropanoyl)oxy)-4-(trifluoromethyl)benzoic acid (optba), a htb/pyruvate ester, in the postischemic rat brain
S. Kim, H. Lee, and I. Kim · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
Cited alongside, same era.
Deeptox: toxicity prediction using deep learning
A. Mayr, G. Klambauer, T. Unterthiner, and S. Hochreiter · 2016
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al · 2016
Cited alongside, same era.
K. Schütt, P. Kindermans, H. E. S. Felix, S. Chmiela, A. Tkatchenko, and K. Müller · 2017
Later among the works it cites.
Generating focused molecule libraries for drug discovery with recurrent neural networks
M. H. Segler, T. Kogej, C. Tyrchan, and M. P. Waller · 2017
Later among the works it cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
Later among the works it cites.
Smoothgrad: removing noise by adding noise
D. Smilkov, N. Thorat, B. Kim, F. Viégas, and M. Wattenberg · 2017
Later among the works it cites.
Machine learning for molecular and materials science
K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, and A. Walsh · 2018
Closest in time.
Automatic chemical design using a data-driven continuous representation of molecules
R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, and A. Aspuru-Guzik · 2018
Closest in time.
Constrained graph variational autoencoders for molecule design
Q. Liu, M. Allamanis, M. Brockschmidt, and A. Gaunt · 2018
Closest in time.
Inverse molecular design using machine learning: Generative models for matter engineering
B. Sanchez-Lengeling and A. Aspuru-Guzik · 2018
Closest in time.
Modeling relational data with graph convolutional networks
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling · 2018
Closest in time.
Transfer learning for molecular cancer classification using deep neural networks
R. K. Sevakula, V. Singh, N. K. Verma, C. Kumar, and Y. Cui · 2018
Closest in time.
Moleculenet: a benchmark for molecular machine learning
Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing, and V. Pande · 2018
Closest in time.
Deep learning for molecular design-a review of the state of the art
D. C. Elton, Z. Boukouvalas, M. D. Fuge, and P. W. Chung · 2019
Closest in time.
Explainability methods for graph convolutional neural networks
P. E. Pope, S. Kolouri, M. Rostami, C. E. Martin, and H. Hoffmann · 2019
Closest in time.