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Machine learning based methods have shown potential for optimizing existing molecules with more desirable properties, a critical step towards accelerating new chemical discovery.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
Weininger, D · 1988
Earlier work this paper cites.
Amino acid substitution matrices from protein blocks
Henikoff, S. & Henikoff, J. G · 1992
Earlier work this paper cites.
The art and practice of structure-based drug design: a molecular modeling perspective
Bohacek, R. S., McMartin, C. & Guida, W. C · 1996
Earlier work this paper cites.
An overview of the simultaneous perturbation method for efficient optimization
Spall, J. C · 1998
Earlier work this paper cites.
Origin of low mammalian cell toxicity in a class of highly active antimicrobial amphipathic helical peptides
Hawrani, A., Howe, R. A., Walsh, T. R. & Dempsey, C. E · 2008
Earlier work this paper cites.
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Ertl, P. & Schuffenhauer, A · 2009
Earlier work this paper cites.
Extended-connectivity fingerprints
Rogers, D. & Hahn, M · 2010
Earlier work this paper cites.
AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Trott, O. & Olson, A. J · 2010
Earlier work this paper cites.
Camp: a useful resource for research on antimicrobial peptides
Thomas, S., Karnik, S., Barai, R. S., Jayaraman, V. K. & Idicula-Thomas, S · 2010
Earlier work this paper cites.
Novel classes of antibiotics or more of the same?
Coates, A. R., Halls, G. & Hu, Y · 2011
Earlier work this paper cites.
Ligbuilder 2: a practical de novo drug design approach
Yuan, Y., Pei, J. & Lai, L · 2011
Earlier work this paper cites.
Matched molecular pairs as a medicinal chemistry tool: miniperspective
Griffen, E., Leach, A. G., Robb, G. R. & Warner, D. J · 2011
Earlier work this paper cites.
Quantifying the chemical beauty of drugs
Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S. & Hopkins, A. L · 2012
Earlier work this paper cites.
The enumeration of chemical space
Reymond, J.-L., Ruddigkeit, L., Blum, L. & van Deursen, R · 2012
Earlier work this paper cites.
Estimation of the size of drug-like chemical space based on gdb-17 data
Polishchuk, P. G., Madzhidov, T. I. & Varnek, A · 2013
Earlier work this paper cites.
Matched molecular pair analysis in drug discovery
Dossetter, A. G., Griffen, E. J. & Leach, A. G · 2013
Earlier work this paper cites.
Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Ghadimi, S. & Lan, G · 2013
Earlier work this paper cites.
iamp-2l: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types
Xiao, X., Wang, P., Lin, W.-Z., Jia, J.-H. & Chou, K.-C · 2013
Earlier work this paper cites.
Multi-objective molecular de novo design by adaptive fragment prioritization
Reutlinger, M., Rodrigues, T., Schneider, P. & Schneider, G · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. & Welling, M · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K. & Bengio, Y · 2015
Earlier work this paper cites.
Zinc 15–ligand discovery for everyone
Sterling, T. & Irwin, J. J · 2015
Earlier work this paper cites.
Satpdb: a database of structurally annotated therapeutic peptides
Singh, S. et al · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. & Ba, J · 2015
Earlier work this paper cites.
Dbaasp v. 2: an enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides
Pirtskhalava, M. et al · 2016
Earlier work this paper cites.
Generating sentences from a continuous space
Bowman, S. et al · 2016
Earlier work this paper cites.
Machine learning unifies the modeling of materials and molecules
Bartók, A. P. et al · 2017
Earlier work this paper cites.
Molecular de-novo design through deep reinforcement learning
Olivecrona, M., Blaschke, T., Engkvist, O. & Chen, H · 2017
Earlier work this paper cites.
Objective-reinforced generative adversarial networks (organ) for sequence generation models
Guimaraes, G. L., Sanchez-Lengeling, B., Outeiral, C., Farias, P. L. C. & Aspuru-Guzik, A · 2017
Cited alongside, same era.
Optimizing distributions over molecular space. an objective-reinforced generative adversarial network for inverse-design chemistry (organic)
Sanchez-Lengeling, B., Outeiral, C., Guimaraes, G. L. & Aspuru-Guzik, A · 2017
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S. & Poole, B · 2017
Cited alongside, same era.
Relative binding free energy calculations in drug discovery: recent advances and practical considerations
Cournia, Z., Allen, B. & Sherman, W · 2017
Cited alongside, same era.
ZOO: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Chen, P.-Y., Zhang, H., Sharma, Y., Yi, J. & Hsieh, C.-J · 2017
farppi: a webserver for accurate prediction of protein-ligand binding structures for small-molecule ppi inhibitors by mm/pb (gb) sa methods
Wang, Z. et al · 2019
Later among the works it cites.
Characterization of the bioactivity and mechanism of bactenecin derivatives against food-pathogens
Sun, C. et al · 2019
Later among the works it cites.
Zo-adamm: Zeroth-order adaptive momentum method for black-box optimization
Chen, X. et al · 2019
Later among the works it cites.
Deep learning regression model for antimicrobial peptide design
Witten, J. & Witten, Z · 2019
Later among the works it cites.
Machine learning for chemical discovery
Tkatchenko, A · 2020
Closest in time.
Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks
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Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I. et al · 2017
Cited alongside, same era.
Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into chou’s general pseaac
Meher, P. K., Sahu, T. K., Saini, V. & Rao, A. R · 2017
Cited alongside, same era.
Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry
Zhavoronkov, A · 2018
Cited alongside, same era.
Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, R. et al · 2018
Cited alongside, same era.
Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R. & Jaakkola, T · 2018
Cited alongside, same era.
Graph convolutional policy network for goal-directed molecular graph generation
You, J., Liu, B., Ying, Z., Pande, V. & Leskovec, J · 2018
Cited alongside, same era.
mmpdb: An open-source matched molecular pair platform for large multiproperty data sets
Dalke, A., Hert, J. & Kramer, C · 2018
Cited alongside, same era.
Kotsias, P.-C. et al · 2020
Closest in time.
Analysis of therapeutic targets for sars-cov-2 and discovery of potential drugs by computational methods
Wu, C. et al · 2020
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Molecular interaction and inhibition of sars-cov-2 binding to the ace2 receptor
Yang, J. et al · 2020
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Augmenting genetic algorithms with deep neural networks for exploring the chemical space
Nigam, A., Friederich, P., Krenn, M. & Aspuru-Guzik, A · 2020
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Chembo: Bayesian optimization of small organic molecules with synthesizable recommendations
Korovina, K. et al · 2020
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Constrained bayesian optimization for automatic chemical design using variational autoencoders
Griffiths, R.-R. & Hernández-Lobato, J. M · 2020
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Optimol: Optimization of binding affinities in chemical space for drug discovery
Boitreaud, J., Mallet, V., Oliver, C. & Waldispühl, J · 2020
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Core: Automatic molecule optimization using copy & refine strategy
Fu, T., Xiao, C. & Sun, J · 2020
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Improving molecular design by stochastic iterative target augmentation
Yang, K., Jin, W., Swanson, K., Barzilay, R. & Jaakkola, T · 2020
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A deep-learning view of chemical space designed to facilitate drug discovery
Maragakis, P., Nisonoff, H., Cole, B. & Shaw, D. E · 2020
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A primer on zeroth-order optimization in signal processing and machine learning
Liu, S. et al · 2020
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Artificial intelligence method to design and fold alpha-helical structural proteins from the primary amino acid sequence
Qin, Z. et al · 2020
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Molecular sets (moses): a benchmarking platform for molecular generation models
Polykovskiy, D. et al · 2020
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On failure modes in molecule generation and optimization
Renz, P., Van Rompaey, D., Wegner, J. K., Hochreiter, S. & Klambauer, G · 2020
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Cogmol: Target-specific and selective drug design for covid-19 using deep generative models
Chenthamarakshan, V. et al · 2020
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Structure of mpro from sars-cov-2 and discovery of its inhibitors
Jin, Z. et al · 2020
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Happenn is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks
Timmons, P. B. & Hewage, C. M · 2020
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HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation
Hasan, M. M. et al · 2020
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Identification of antiviral drug candidates against sars-cov-2 from fda-approved drugs
Jeon, S. et al · 2020
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Machine learning-guided discovery and design of non-hemolytic peptides
Plisson, F., Ramírez-Sánchez, O. & Martínez-Hernández, C · 2020
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Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations
Das, P. et al · 2021
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Ibm/qmo: V1, DOI: 10.5281/zenodo.5562908 (2021)
Hoffman, S. & Martinelli, S · 2021
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