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Nanoscale, 2021, 13, 17854
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Contrastive representation learning of inorganic materials to overcome lack of training datasets

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Chem. Commun., 2022, 58, 6729
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Nonlinearity encoding to improve extrapolation capabilities for unobserved physical states

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Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

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Chem. Sci., 2021, 12, 6879
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Graphical Gaussian process regression model for aqueous solvation free energy prediction of organic molecules in redox flow batteries

Peiyuan Gao, Xiu Yang, Yu-Hang Tang, Muqing Zheng, Amity Andersen, Vijayakumar Murugesan, Aaron Hollas and Wei Wang

Phys. Chem. Chem. Phys., 2021, 23, 24892
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Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools

Kostas Blekos, Kostas Chairetakis, Iseult Lynch and Effie Marcoulaki

J Cheminform, 2023, 15
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GrapheNet: a deep learning framework for predicting the physical and electronic properties of nanographenes using images

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Sci Rep, 2024, 14
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A systematic study of key elements underlying molecular property prediction

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Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn–Sham Approach

Sung Wook Moon, Soohaeng Yoo Willow, Tae Hyeon Park, Seung Kyu Min and Chang Woo Myung

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Deep Learning Model for Efficient Protein–Ligand Docking with Implicit Side-Chain Flexibility

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Physics-Informed Multi-Agent deep reinforcement learning enabled distributed voltage control for active distribution network using PV inverters

Bin Zhang, Di Cao, Weihao Hu, Amer M.Y.M. Ghias and Zhe Chen

International Journal of Electrical Power & Energy Systems, 2024, 155, 109641
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Rotational Variance‐Based Data Augmentation in 3D Graph Convolutional Network

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Chemistry An Asian Journal, 2021, 16, 2610
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Substructure interaction graph network with node augmentation for hybrid chemical systems of heterogeneous substructures

Gyoung S. Na

Computational Materials Science, 2023, 216, 111835
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Judicious training pattern for superior molecular reorganization energy prediction model

Xinxin Niu, Yanfeng Dang, Yajing Sun and Wenping Hu

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Development of a chemically intuitive filter for chemical graph convolutional network

Yoonho Jeong, Jihoo Kim, Yeji Kim and Insung S. Choi

Bulletin Korean Chem Soc, 2022, 43, 934
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On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

Shan He, Julen Segura Abarrategi, Harbil Bediaga, Sonia Arrasate and Humberto González-Díaz

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A review on machine learning approaches and trends in drug discovery

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Artificial intelligence in drug discovery: applications and techniques

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Briefings in Bioinformatics, 2022, 23
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Carbon-efficient reaction optimization of nonoxidative direct methane conversion based on the integrated reactor system

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Machine learning for enterprise modeling assistance: an investigation of the potential and proof of concept

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Generative Adversarial Framework with Composite Discriminator for Organization and Process Modelling—Smart City Cases

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Smart Cities, 2025, 8, 38
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MolNet_Equi: A Chemically Intuitive, Rotation‐Equivariant Graph Neural Network

Jihoo Kim, Yoonho Jeong, Won June Kim, Eok Kyun Lee and Insung S. Choi

Chemistry An Asian Journal, 2024, 19
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Graph Neural Networks and Molecular Docking as Two Complementary Approaches for Virtual Screening: A Case Study on Cruzain

José Leonardo Gómez Chávez, Adriano Martín Luchi, Roxana Noelia Villafañe, Germán Andres Conti, Ernesto Rafael Perez, Emilio Luis Angelina and Nélida María Peruchena

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Hybrid localized graph kernel for machine learning energy‐related properties of molecules and solids

Bastien Casier, Mauricio Chagas da Silva, Michael Badawi, Fabien Pascale, Tomáš Bučko, Sébastien Lebègue and Dario Rocca

J Comput Chem, 2021, 42, 1390
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Custom Tokenization Dictionary, CUSTODI: A General, Fast, and Reversible Data-Driven Representation and Regressor

Shachar Fite, Omri Nitecki and Zeev Gross

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Eigen-guided deep metric learning

Gyoung S. Na

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Artificial intelligence to bring nanomedicine to life

Nikita Serov and Vladimir Vinogradov

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