InChI: open-source chemical structure representation algorithm

AI/ML Papers Using InChI

Papers Citing InChI and Using Various AI/ML Applications

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
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
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
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design
Yuanqi Du, Tianfan Fu, Jimeng Sun, Shengchao Liu
JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015.
DOI: https://scholar.google.com/scholar_url?url=https://arxiv.org/pdf/2203.14500&hl=en&sa=X&d=11912729450092978116&ei=5mRHYo7GL5GJmwH9jKuIDA&scisig=AAGBfm2A2bPRrJ3sTNGpuKj9ix3SdzmTHg&oi=scholaralrt&hist=XkF1yqEAAAAJ:14262565674548175021:AAGBfm0uWO8vxmHzWni4rLbVw6MFcfqxLg&html=&pos=1&folt=cit&fols=
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
Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics.
Vaz, Joel Markus; Balaji, S.
Mol Divers 25 1569-1584 (2021).
DOI: