AI/ML Papers Using InChI

Papers Citing InChI and Using Various AI/ML Applications

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
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
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ChemRxiv preprint and has not been peer-reviewed.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
Machine Learning guided early drug discovery of small molecules
Nikhil Pillai, Aparajita Dasgupt, Sirimas Sudsakorn, Jennifer Fretlan, Panteleimon D.Mavroudis
Drug Discovery Today,2022.
DOI: https://doi.org/10.1016/j.drudis.2022.03.017
Reconstruction of lossless molecular representations
Umit V. Ucak, Islambek Ashyrmamatov, and Juyong Lee
chemRxiv preprint 2022.
DOI: https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/62273eb250b6211bf1ed8132/original/reconstruction-of-lossless-molecular-representations.pdf