InChI: structure-based chemical identifier

AI/ML Papers Using InChI

Papers Citing InChI and Using Various AI/ML Applications

Recent advances in computational modeling of MOFs: From molecular simulations to machine learning
Hakan Demir, Hilal Daglar, Hasan Can Gulbalkan, Gokhan Onder Aksu, Seda Keskin
Coordination Chemistry Reviews v 484, (1 June 2023) 215112
DOI: https://doi.org/10.1016/j.ccr.2023.215112
GC-EI-MS datasets of trimethylsilyl (TMS) and tert-butyl dimethyl silyl (TBDMS) derivatives for development of machine learning-based compound identification approaches
Milka Ljoncheva, Sintija Stevanoska, Tina Kosjek, Sašo Džeroski
(2022) J.Chem. 14(1):62
DOI: https://doi.org/10.1016/j.dib.2023.109138
Transformer Performance for Chemical Reactions: Analysis of Different Predictive and Evaluation Scenarios
Fernando Jaume-Santero, Alban Bornet*, Alain Valery, Nona Naderi, David Vicente Alvarez, Dimitrios Proios, Anthony Yazdani, Colin Bournez, Thomas Fessard, and Douglas Teodoro
J. Chem. Inf. Model. 2023, 63, 7, 1914–1924
DOI: https://doi.org/10.1117/12.2667694
Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures
Fabian Jirasek and Hans Hasse
Ann Rev of Chem and Bio Eng, Vol 14 (June 2023)
DOI: https://doi.org/10.1146/annurev-chembioeng-092220-025342
The LOTUS initiative for open knowledge management in natural products research
Adriano Rutz, Maria Sorokina, Jakub Galgonek, Daniel Mietchen, Egon Willighagen, Arnaud Gaudry, James G Graham, Ralf Stephan, Roderic Page, Jiří Vondrášek, Christoph Steinbeck, Guido F Pauli, Jean-Luc Wolfender, Jonathan Bisson Is a corresponding author , Pierre-Marie Allard
research eLife 11:e70780 (2022).https://doi.org/10.7554/eLife.70780
DOI: https://doi.org/10.7554/eLife.70780
Unraveling compound taxonomies in untargeted metabolomics through artificial intelligence
Henrique dos Santos Silva
Dissertation for Master's Degree in Biochemistry Specialization in Biochemistry, U Lisbon, Portugal
DOI: http://hdl.handle.net/10451/56544
Bayesian multi-model-based 13C15N-metabolic flux analysis quantifies carbon-nitrogen metabolism in mycobacteria
Khushboo Borah, Martin Bey, Ye Xu, Jim Barber, Catia Costa, Jane Newcombe, Khushboo Borah, Martin Bey, Ye Xu, Jim Barber, Catia Costa, Jane Newcombe, Axel Theorell, Melanie J Bailey, Dany JV Beste, Johnjoe McFadden, Katharina Nöh
bioRxiv preprint 2022.
DOI: https://doi.org/10.1101/2022.03.08.483448
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
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