AI/ML Papers Using InChI

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Papers Citing InChI and Using Various AI/ML Applications

Automated generation of molecular derivatives – DerGen software package
Ilia Kichev, Lyuben Borislavov, AliaTadjer

DOI: https://doi.org/10.1016/j.matpr.2022.04.628

Compound–protein interaction prediction by deep learning: Databases, descriptors and models
Bing-Xue Du, Yuan Qina, Yan-Feng Jiang, Yi Xu, Siu-Ming Yiu, Hui Yu, Jian-Yu Shi
Drug Discovery Today, 2022
DOI: https://doi.org/10.1016/j.drudis.2022.02.023

Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network
T Nikolaienko, O Gurbych, M Druchok
J Comp Chem 43, (2022) 10, 728-739.
DOI: https://doi.org/10.1002/jcc.26831

Molecular Design Learned from the Natural Product Porphyra-334: Molecular Generation via Chemical Variational Autoencoder versus Database Mining via Similarity Search, A Comparative Study
Yuki Harada, Makoto Hatakeyama, Shuichi Maeda, Qi Gao, Kenichi Koizumi, Yuki Sakamoto, Yuuki Ono, and Shinichiro Nakamura
ACS Omega 2022, 7, 10, 8581–8590
DOI: https://doi.org/10.1021/acsomega.1c06453

HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder
Tagir Akhmetshin; Arkadii Lin; Daniyar Mazitov; Evgenii Ziaikin; Timur Madzhidov; Alexandre Varnek
ChemRxiv preprint and has not been peer-reviewed.
DOI: https://chemrxiv.org/engage/chemrxiv/article-details/61aa38576d4e8f3bdba8aead

Translating the Molecules: Adapting Neural Machine Translation to Predict IUPAC Names from a Chemical Identifier
Handsel, J., Matthews, B., Knight, N.J., Coles, S. J.
J Cheminform 13, 79 (2021).
DOI: https://www.doi.org/10.1186/s13321-021-00517-z

Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention
Hyunseung Kim; Jonggeol Na; Won Bo Lee
J. Chem. Inf. Model. 2021, 61, 12, 5804–5814.
DOI: https://doi.org/10.1021/acs.jcim.1c01289