InChI isotopologue and isotopomer specifications: Hunter N. B. Moseley Philippe Rocca‑Serra Reza M. Salek Masanori Arita Emma L. Schymanski
{Moseley, H.N.B., Rocca-Serra, P., Salek, R.M. et al. InChI isotopologue and isotopomer specifications. J Cheminform 16, 54 (2024)}
https://doi.org/10.1186/s13321-024-00847-8

IUPAC International Chemical Identifier (InChI)-related education and training materials through InChI Open Education Resource (OER): Cornell, Andrew P., Kim, Sunghwan, Cuadros, Jordi, Bucholtz, Ehren C., Pence, Harry E., Potenzone, Rudy and Belford, Robert E.
{Chemistry Teacher International, 2024. https://doi.org/10.1515/cti-2023-0009}
https://doi.org/10.1515/cti-2023-0009

Yuel: Improving the Generalizability of Structure-Free Compound–Protein Interaction Predictions: J Wang, NV Dokholyan
{Chem. Inf. Model (2022) 463–471}
https://doi.org/10.1021/acs.jcim.1c01531

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}
http://hdl.handle.net/10451/56544

Automatic kinetic model generation: a novel modeling approach for liquid-phase processes: Gust Popelier
{Dissertation for Master of Science in Chemical Engineering, Ghent U (2022).}

Design and Diversity Analysis of Chemical Libraries in Drug Discovery: Dionisio A. Olmedo, Armando A. Durant-Archibold, José Luis López-Pérez, José L. Medina-Franco
{ChemRxiv. Cambridge: Cambridge Open Engage; 2023; preprint.}
https://chemrxiv.org/engage/chemrxiv/article-details/640e39ae7290f69f8ee0fe51

GenSMILES: An enhanced validity conscious representation for inverse design of molecules: Arun Singh Bhadwal, Kamal Kumar, Neeraj Kumar
{Knowledge-Based Systems, V 268 (2023) 110429, ISSN 0950-7051}
https://doi.org/10.1016/j.knosys.2023.110429

Biosynthesis and Biological Profiling of Collinolactone and Semisynthetic Derivatives and MetaboIDent, a Novel Tool for Automated Dereplication: JC Schmid
{Ph D Dissertation; Mathematics and Natural Sciences. Eberhard Karls University of Tübingen.}
https://tobias-lib.ub.uni-tuebingen.de/xmlui/bitstream/handle/10900/137947/Dissertation_Schmid.pdf?sequence=2&isAllowed=y

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}
https://doi.org/10.1016/j.ccr.2023.215112

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)}
https://doi.org/10.1146/annurev-chembioeng-092220-025342

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}
https://doi.org/10.1117/12.2667694

VisMole: a molecular representation based on voxel for molecular property prediction: Qiang Tong, Jiahao Shen, Xiulei Liu
{5th Int Conf on Comp Inf Sci & AI (CISAI 2022) (March 2023) 1256628}
https://doi.org/10.1117/12.2667694

Physicochemical properties, drug likeness, ADMET, DFT studies, and in vitro antioxidant activity of oxindole derivatives: Imad Ahmad, Haroon Khan, Goncagül Serdaroğlu
{Comp Bio and Chem 104 (2023) 107861}
https://doi.org/10.1016/j.compbiolchem.2023.107861

Combustion, Chemistry, and Carbon Neutrality: Katharina Kohse-Höinghaus
{Proceedings Volume 12566, 5th Int Conf on Comp Info Sci & AI (CISAI 2022); 1256628 (2023)}
https://doi.org/10.1021/acs.chemrev.2c00828

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}
https://doi.org/10.1016/j.dib.2023.109138

Pharmaceutical pollution: Prediction of environmental concentrations from national wholesales data: Samuel A. Welch, Kristine Olsen, Mohammad Nouri Sharikabad, Knut Erik Tollefsen, Merete Grung
{Open Res Europe 2:71 (2022).}
https://doi.org/10.12688/openreseurope.14129.1

Updating the Dermal Sensitisation Thresholds using an expanded dataset and an in silico expert system: Martyn L.Chiltona, Anne Marie Api, Robert S.Foster, G. FrankGerberick, MauraLavelle, Donna S.Macmillan, MihwaNa, Devin O'Brien, Catherine O'Leary-Steele, Mukesh Patel, David J.Ponting, David W.Roberts, Robert J.Safford, Rachael E.Tennant
{Reg Tox and Pharm (133) 105200, 2022.}
https://doi.org/10.1016/j.yrtph.2022.105200

canSAR chemistry registration and standardization pipeline: Daniela Dolciami, Eloy Villasclaras-Fernandez, Christos Kannas, Mirco Meniconi, Bissan Al-Lazikani, Albert A. Antolin
{ J Cheminform 14, 28 (2022). }
https://doi.org/10.1186/s13321-022-00606-7

Digital Discovery: Kohulan Rajan, Christoph Steinbeck and Achim Zielesny
{Digital Discovery, 2022, 1, 84}
DOI: 10.1039/d1dd00013f

Uni-Mol: A Universal 3D Molecular Representation Learning Framework: GengmoZhou1,2∗, ZhifengGao2∗†,QiankunDing2,HangZheng2 Hongteng Xu1, Zhewei Wei1, Linfeng Zhang2,3, Guolin Ke2
{ChemRxiv. Cambridge: Cambridge Open Engage; 2022; This content is a preprint and has not been peer-reviewed}
https://doi.org/10.26434/chemrxiv-2022-jjm0j