Fuzz Testing Molecular Representation Using Deep Variational Anomaly Generation.

Victor H R Nogueira, Rishabh Sharma, Rafael V C Guido, Michael J Keiser

Abstract read

In one paragraph

Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

MolOrgGPT:Journal of chemical information and modeling · 2026
Article

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5 · Who and what money

Authors and funding

4 authors.

Victor H R NogueiraSão Carlos Institute of Physics, University of São Paulo, São Paulo 13563-120, Brazil.

Rishabh SharmaDepartment of Pharmaceutical Chemistry, Department of Bioengineering & Therapeutic Sciences, Institute for Neurodegenerative Diseases, Kavli Institute for Fundamental Neuroscience, Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California 94158, United States.

Rafael V C GuidoSão Carlos Institute of Physics, University of São Paulo, São Paulo 13563-120, Brazil.

Michael J KeiserDepartment of Pharmaceutical Chemistry, Department of Bioengineering & Therapeutic Sciences, Institute for Neurodegenerative Diseases, Kavli Institute for Fundamental Neuroscience, Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California 94158, United States.ORCID 0000-0002-1240-2192

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Researchers are developing increasingly robust molecular representations, motivating the need for thorough methods to stress-test and validate them. Here, we use a variational auto-encoder (VAE), an unsupervised deep learning model, to generate anomalous examples of SELF-referencIng Embedded Strings (SELFIES), a popular molecular string format. These anomalies defy the assertion that all SELFIES convert into valid SMILES strings. Interestingly, we find specific regions within the VAE's internal landscape (latent space), whose decoding frequently generates inconvertible SELFIES anomalies. The model's internal landscape self-organization helps with exploring factors affecting molecular representation reliability. We show how VAEs and similar anomaly generation methods can empirically stress-test molecular representation robustness. Additionally, we investigate reasons for the invalidity of some discovered SELFIES strings (version 2.1.1) and suggest changes to improve them, aiming to spark ongoing molecular representation improvement.

Indexed as

Deep Learning

Identifiers

PMID39908426

DOI10.1021/acs.jcim.4c01876

PMCPMC11863373

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.