Rethinking Molecular Graph Backdoors under Chemistry-aware Admission | Paper Citation Record

Observation 3654eae7-2e91-4c5e-abd1-034cfb03c0b0 · outbound

This paper cites Deeper insights into graph convolutional networks for semi-supervised learning.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Deeper insights into graph convolutional networks for semi-supervised learning

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Observation 8bfca69c-57d5-4b8d-9cf8-be44609e691f · outbound

This paper cites Graph neural networks: A review of methods and applications.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Graph neural networks: A review of methods and applications

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Observation 67c7b339-e515-4f3f-a887-b3acca45f4b3 · outbound

This paper cites Moleculenet: a benchmark for molecular machine learning.Chemical science, 9(2):513–530, 2018.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Moleculenet: a benchmark for molecular machine learning.Chemical science, 9(2):513–530, 2018

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This paper cites Neural message passing for quantum chemistry.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Neural message passing for quantum chemistry

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Observation 5502c3f3-ba26-491d-89e4-b62d9079404b · outbound

This paper cites Motif-backdoor: Rethinking the backdoor attack on graph neural networks via motifs.IEEE Transactions on Computational Social Systems, 11(2):2479–2493, 2023.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Motif-backdoor: Rethinking the backdoor attack on graph neural networks via motifs.IEEE Transactions on Computational Social Systems, 11(2):2479–2493, 2023

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Observation 6ed00738-1d18-4d1d-b66a-6ca35ccce1de · outbound

This paper cites Unnoticeable backdoor attacks on graph neural networks.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Unnoticeable backdoor attacks on graph neural networks

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Observation 81da2b8a-0466-4b10-ae0f-3a69c0be0c14 · outbound

This paper cites Rethinking graph backdoor attacks: A distribution-preserving perspective.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Rethinking graph backdoor attacks: A distribution-preserving perspective

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Observation 396622e0-2307-494a-9252-26f3cd91fd1f · outbound

This paper cites Lr-gnn: A graph neural network based on link representation for predicting molecular associations.Briefings in Bioinformatics, 23(1): bbab513, 2022.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Lr-gnn: A graph neural network based on link representation for predicting molecular associations.Briefings in Bioinformatics, 23(1): bbab513, 2022

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Observation 1fda070b-e9fa-4a9d-903b-90d8bfb73c1f · outbound

This paper cites Pre-training graph neural networks for link prediction in biomedical networks.Bioinformatics, 38(8): 2254–2262, 2022.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Pre-training graph neural networks for link prediction in biomedical networks.Bioinformatics, 38(8): 2254–2262, 2022

Reference 9

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Observation 8c1e8083-7df5-427f-bdf1-4e5b58ebe3a6 · outbound

This paper cites A compact review of molecular property prediction with graph neural networks.Drug Discovery Today: Technologies, 37:1–12, 2020.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission A compact review of molecular property prediction with graph neural networks.Drug Discovery Today: Technologies, 37:1–12, 2020

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Observation 91a963e8-92c6-494d-9c76-44e2480d3301 · outbound

This paper cites Enhancing drug discovery with ai: Predictive modeling of pharmacokinetics using graph neural networks and ensemble learning.Intelligent Pharmacy, 3(2):127–140, 2025.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Enhancing drug discovery with ai: Predictive modeling of pharmacokinetics using graph neural networks and ensemble learning.Intelligent Pharmacy, 3(2):127–140, 2025

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Observation 5c707b44-cfcf-43ce-86be-869a3088d330 · outbound

This paper cites Rdkit documentation.Release, 1(1-79):4, 2013.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Rdkit documentation.Release, 1(1-79):4, 2013

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Observation a1a3a956-d897-4651-996d-44348ac195ab · outbound

This paper cites Open babel: An open chemical toolbox.Journal of cheminformatics, 3(1):33, 2011.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Open babel: An open chemical toolbox.Journal of cheminformatics, 3(1):33, 2011

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Observation 74f7604e-5ca2-4d1e-8013-eb395a8736d0 · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Indigo: universal cheminformatics api.Journal of cheminformatics, 3(Suppl 1):P4, 2011

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Observation f170952d-ee9c-4b0a-9f3e-e3088ed8cb1e · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Extended-connectivity fingerprints.Journal of chemical information and modeling, 50(5):742–754, 2010

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Observation 6a05aa07-ac6e-442c-8597-42a68f7dd06a · outbound

This paper cites Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations?Journal of cheminformatics, 7(1):20, 2015.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations?Journal of cheminformatics, 7(1):20, 2015

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Observation d53aaab6-b4fb-4af4-b899-ecd3c97e9667 · outbound

This paper cites The graph neural network model.IEEE Transactions on Neural Networks, 20(1):61–80, 2009.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission The graph neural network model.IEEE Transactions on Neural Networks, 20(1):61–80, 2009

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Resolution: malformed identifier; arxiv_id, observed 2026-07-04T10:19:47.748797Z

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Observation f1f7a792-3671-40e6-8077-05328d595912 · outbound

This paper cites an unresolved cited work.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Unresolved cited work

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Observation ae5da300-6b27-4e0d-83b8-15ce2884f40a · outbound

This paper cites Kipf and Max Welling.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Kipf and Max Welling

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Observation c26fc13d-79e1-40ba-911d-4782cf175176 · outbound

This paper cites Hamilton, Rex Ying, and Jure Leskovec.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Hamilton, Rex Ying, and Jure Leskovec

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Observation 8df3c8e2-86f0-4f08-b71b-10b8a895764c · outbound

This paper cites Self- supervised graph transformer on large-scale molecular data.Advances in neural information processing systems, 33:12559–12571, 2020.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Self- supervised graph transformer on large-scale molecular data.Advances in neural information processing systems, 33:12559–12571, 2020

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Observation 33ce39ba-f08d-4182-929a-7bd1e925bfed · outbound

This paper cites BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

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Observation b0ca472b-3601-4e3c-8f4a-cd5ed50d0d2a · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Input-aware dynamic backdoor attack.Advances in Neural Information Processing Systems, 33:3454–3464, 2020

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Observation a5ad8aac-2cf1-4eff-9fae-eed5c4e9533d · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Lira: Learnable, imperceptible and robust backdoor attacks

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Observation 33508a53-eaf1-48e5-bfd7-a4c5fd683844 · outbound

This paper cites Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Graph backdoor

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Observation 329105c0-a9dd-4263-82f0-c31d962ee9a9 · outbound

This paper cites Chechik and T.

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Chechik and T

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Observation f0ce09bb-7886-4c1c-8270-927b8c34695e · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Spectral signatures in backdoor attacks.Advances in neural information processing systems, 31, 2018

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Observation a393fc7b-f74f-40a8-872f-82c1c2483556 · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Dshield: Defending against backdoor attacks on graph neural networks via discrepancy learning

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Robustness Inspired Graph Backdoor Defense

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Observation bfc62d6f-2171-42d0-b7e8-882c5822101b · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Robust graph convolutional networks against adversarial attacks

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Gnnguard: Defending graph neural networks against adversarial attacks

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Graph structure learning for robust graph neural networks

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Certified robustness of graph neural networks against adversarial structural perturbation

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Distributed backdoor attacks on federated graph learning and certified defenses

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Deterministic certification of graph neural networks against graph poisoning attacks with arbitrary perturbations

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Observation 00fc5973-e4c7-4573-977c-45cf0bc15139 · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission A bayesian approach to in silico blood-brain barrier penetration modeling.Journal of chemical information and modeling, 52(6): 1686–1697, 2012

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Computational modeling of β-secretase 1 (bace-1) inhibitors using ligand based approaches.Journal of chemical information and modeling, 56(10):1936–1949, 2016

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission The sider database of drugs and side effects

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Observation cd82f04e-63fd-46e4-8edd-131f9dc7b642 · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Unresolved cited work

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Observation be1f7132-4e24-4bfd-a7c7-e7c033081448 · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Pubchem’s bioassay database.Nucleic acids research, 40(D1):D400–D412, 2012

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Maximum unbiased validation (muv) data sets for virtual screening based on pubchem bioactivity data.Journal of chemical information and modeling, 49(2):169–184, 2009

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings.Advanced drug delivery reviews, 23(1-3):3–25, 1997

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Observation d651ee81-51fd-42ae-8e77-bc1d56e6c307 · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Molecular properties that influence the oral bioavailability of drug candidates.Journal of medicinal chemistry, 45(12):2615–2623, 2002

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Quantifying the chemical beauty of drugs.Nature chemistry, 4(2):90–98, 2012

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Observation 98664bde-e302-40ea-84bc-3bb7798828f1 · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Unresolved cited work

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Observation 46caf574-84b2-4cdc-90c4-f6dcef39e854 · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Unresolved cited work

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Observation e3954824-9699-4135-94c5-949d78ec293d · outbound

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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission Equivalence tests: A practical primer for t tests, correlations, and meta-analyses

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