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REVIEW 5 major objections 6 minor 43 references

Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTM

T0 review · 5 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Augmented SELFIES improve molecular property prediction over plain SMILES in both classical LSTM and hybrid quantum-classical QK-LSTM models, with relative ROC-AUC gains of about 6 percent.

desk verdict The paper's own Table I shows the SELFIES-vs-SMILES gain vanishes once augmentation is held fixed, so the abstract's causal claim does not survive contact with the data. read the letter →

arxiv 2504.20789 v1 pith:U2A7MI32 submitted 2025-04-29 cs.LG

Collin Beaudoin , Swaroop Ghosh This is my paper · ORCID
classification cs.LG
keywords molecularpropertypredictionSELFIESSMILESdataaugmentationQK-LSTMquantummachinelearningsideeffectLSTM
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that augmenting SELFIES strings—generating multiple valid SELFIES renderings of each molecule instead of a single string—improves molecular property prediction over plain SMILES in both classical LSTM and hybrid quantum-classical models. On the SIDER side-effect dataset, the authors report that LSTM with augmented SELFIES reaches a ROC-AUC of 0.556 versus 0.525 for plain SMILES, and that QK-LSTM (a long short-term memory network with quantum kernel feature maps in its gates) reaches 0.555 versus 0.524, a relative gain of about 6 percent in both domains. The paper presents this as the first test of augmentation for SELFIES and the first application of augmented molecular strings to a quantum kernel LSTM. If correct, it would show that a classical data-level technique transfers to quantum-hybrid models, and that a robust string representation need not cost accuracy.

What carries the argument

The load-bearing mechanism is an augmentation-conversion pipeline: canonical SMILES strings are first converted to canonical form, then augmented by generating up to twenty alternate SMILES enumerations and retaining the five shortest, and the resulting strings are converted to SELFIES and tokenized at the bracket level into index sequences for the embedding layer. For the hybrid quantum-classical branch, the QK-LSTM replaces the classical gate transformations with quantum kernels built from entangler circuits—angle encoding, a parameterized entangling circuit, measurement, and a fully connected squeeze—so that each LSTM gate (forget, input, output) processes the sequence through a quantum feature map. This pipeline is what lets the paper attribute the ROC-AUC difference to the input representation and augmentation rather than to a new architecture.

What would settle it

Rerun all six configurations on a fixed shared split with the same Optuna budget per condition and check whether augmented SELFIES still beats plain SMILES by roughly 0.03 ROC-AUC; as a second check, apply the same pipeline to another MoleculeNet classification dataset, such as BACE or BBBP, where the augmentation advantage can be confirmed or refuted.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a quantitative comparison: on the SIDER side-effect benchmark, models trained on augmented SELFIES outperform models trained on plain SMILES by 0.031 ROC-AUC in both the classical LSTM (0.556 vs. 0.525) and the hybrid quantum-classical QK-LSTM (0.555 vs. 0.524), which the authors report as statistically significant improvements of 5.97% and 5.91%. The same comparison shows that plain SELFIES without augmentation perform worse than SMILES (0.507 vs. 0.525), which the authors read as evidence that augmentation, rather than the SELFIES grammar alone, drives the learning gain. The paper further reports that QK-LSTM matches classical LSTM within 0.001 ROC-AUC, suggesting the quantum kernel architecture can retain accuracy while reducing the hidden dimension from 32–128 to 8–32.

Load-bearing premise

The reported gains rest on the assumption that the random 80/10/10 split, the Optuna hyperparameter search, and the top-three-model averaging treat all six conditions equally; if different conditions saw different splits or different search budgets, the 0.031 ROC-AUC gap could be an artifact of split luck or selection bias rather than an effect of the input representation.

Editorial extensions

If this is right

  • Augmented SELFIES can serve as a drop-in replacement for SMILES in both classical LSTM and QK-LSTM pipelines, improving ROC-AUC by about 0.03 without changing the model architecture.
  • Hybrid quantum-classical QK-LSTM can match classical LSTM within 0.001 ROC-AUC on molecular property prediction, so near-term quantum hardware constraints do not force an accuracy trade-off on this task.
  • Plain SELFIES without augmentation does not improve over SMILES, implying that representation choice alone is not the driver; augmentation is the active ingredient.
  • The augmentation benefit transfers across string formats and across classical/quantum domains, so it behaves like a property of training-data diversity rather than of the specific encoding.
  • Because the paper finds the largest absolute gains from augmentation for the LSTM, scaling datasets with augmented SELFIES may become increasingly valuable as dataset size grows.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the headline percentages are relative gains on a low absolute baseline (0.525 to 0.556 is a 0.031 ROC-AUC increase), so the practical importance may be smaller than '5.97%' suggests and depends on replication on other benchmarks.
  • Editorial inference: since augmented SMILES (0.562) actually scores slightly higher than augmented SELFIES (0.556), the strongest defensible takeaway is that augmentation helps and SELFIES does not hurt, not that SELFIES is intrinsically superior.
  • Editorial inference: a natural extension is to pair augmented SELFIES with generative or retrosynthesis tasks, where SELFIES' guarantee of valid strings could combine with augmentation to improve both the validity and diversity of proposed molecules.
  • Editorial inference: the significance claim rests on standard-deviation non-overlap; a paired bootstrap over molecules or multiple random seeds would provide a firmer statistical test.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper investigates whether augmenting SELFIES molecular string representations improves drug side-effect prediction over plain SMILES, using LSTM and a hybrid quantum-classical QK-LSTM on the SIDER benchmark. It reports ROC-AUC scores in Table I and claims in the abstract that augmenting SELFIES yields statistically significant improvements of 5.97% (classical) and 5.91% (hybrid) over SMILES. The paper also describes the QK-LSTM architecture, data preprocessing with canonicalization and SMILES augmentation, and an Optuna-driven hyperparameter search with top-three model averaging. The authors conclude that augmentation helps both representations, while conversion to SELFIES yields no statistically significant gain.

Significance. If the central claim were sound, the paper would provide a useful first exploration of augmented SELFIES in both classical and quantum-classical settings, with an architecture-level comparison and a practical benchmark. The work also gives credit for describing the QK-LSTM integration and for publishing its comparison table, which is transparent enough to reveal the main confound. However, the central claim is not supported by the data as presented: the headline contrast varies both augmentation and representation simultaneously, the paper's own Section III.D shows that SELFIES is not better than SMILES once augmentation is held fixed, and no significance test is reported anywhere. The empirical contribution is therefore weakened to a limited ablation study, and the abstract's causal language is unjustified.

major comments (5)
  1. [Abstract and Table I] The claim that 'augmenting SELFIES yields in statistically significant improvements from SMILES' conflates two factors: augmentation and representation. The comparison used for the headline (LSTM Augmented SELFIES 0.556 vs LSTM SMILES 0.525, and QK-LSTM Augmented SELFIES 0.555 vs QK-LSTM SMILES 0.524) varies both augmentation and the string representation. Table I contains the controls needed to separate these factors for the classical model, but those controls contradict the causal interpretation: at fixed representation, Augmented SELFIES (0.556) is worse than Augmented SMILES (0.562), and at zero augmentation, SELFIES (0.507) is worse than SMILES (0.525). The headline difference is therefore attributable to augmentation, not to SELFIES.
  2. [Section III.D, Comparisons] The paper's own discussion undercuts the abstract's statistical claim. Section III.D states that switching from SMILES to SELFIES gives a 0.022 worse performance that is 'within the standard deviation', and switching from augmented SMILES to augmented SELFIES gives a 0.006 worse performance 'again within the standard deviation'. The section then concludes that there is a 'non-statistically significant performance change' when using SELFIES. This is internally consistent with Table I, but it directly contradicts the abstract's assertion of statistically significant improvements from SMILES. The central claim of the paper is thereby unsupported by the manuscript's own comparisons.
  3. [Section III.C, Results (evaluation protocol)] No significance test is reported anywhere in the paper, despite the abstract and Section III.D using the phrase 'statistically significant'. The reported standard deviations overlap for the relevant comparisons (e.g., SMILES-to-SELFIES differences of 0.022 with standard deviations of 0.023 and 0.026). To support any significance claim, the authors need to specify the number of independent runs, the random seeds, whether the same 80/10/10 split was used across all six conditions, and a proper test such as paired bootstrap or a paired difference test. Without this information, the 'statistically significant' language in the abstract is unsubstantiated.
  4. [Section III.C, Results (model selection)] The evaluation protocol selects each configuration's top three performing models by validation ROC-AUC and averages their test scores. This top-k selection from a small number of Optuna configurations is a post-hoc model selection procedure that can bias the reported mean upward and understate variance, especially if the same validation split is reused for selection and early stopping. The manuscript does not state how many Optuna trials were run, how many random configurations were sampled, or how the variance across the three selected models relates to the variance across all runs. This makes the reported '±' values difficult to interpret and the comparison between conditions potentially biased if selection operated differently across conditions.
  5. [Section III.C, Results (missing control)] For the QK-LSTM arm, the paper does not run the augmented-SMILES control. Section III.C states that computational cost motivated focusing on the two extremes, SMILES and augmented SELFIES. However, without QK-LSTM augmented SMILES, the claimed 5.91% improvement in the hybrid quantum-classical domain cannot separate the effect of augmentation from the effect of the representation, and the paper's own argument in Section III.D requires that control to make the decomposition. The missing control is a load-bearing gap for the quantum-domain claim.
minor comments (6)
  1. [Section III.C, Results] There is a typo in 'Early stopping iss applied' — 'iss' should be 'is'.
  2. [Section II.G, Quantum Neural Networks] 'QNNs comprises of three core components' should be 'QNNs comprise three core components'.
  3. [Section II.F, Long Short-Term Memory] 'Long Short-Term Memory (LSTM) networks [24] is a specialized type' should be 'networks ... are'.
  4. [Figure 3 caption] The caption says 'converts classical features ( f1, f4, f3, f4)' but likely should read '(f1, f2, f3, f4)'.
  5. [Abstract] The sentence 'augmenting SELFIES yields in statistically significant improvements from SMILES' contains a grammatical error: 'yields in' should be 'yields statistically significant improvements over SMILES'.
  6. [Section III.A, Data Pre-processing] The paper does not report the number of molecules in the SIDER dataset after canonicalization, the distribution of labels, or the vocabulary size after SELFIES tokenization; these details would help reproducibility and interpretation of the results.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical benchmark comparison against external SIDER data; the only self-citation is background and non-load-bearing.

full rationale

The paper is an empirical evaluation against the external SIDER benchmark (MoleculeNet). Reported ROC-AUC values in Table I are measured on a held-out test split after training, and no quantity used in the evaluation is defined in terms of the claimed result. The QK-LSTM architecture is cited from prior external work [28] and trained directly on data. The only self-citation, [16], appears in Related Works as motivation for hybrid quantum models and is not used to justify any numerical result, not invoked to forbid alternatives, and not used as a uniqueness theorem; it is externally published and non-load-bearing. The abstract's 'statistically significant' language conflicts with the paper's own Section III.D statements that SMILES-to-SELFIES changes are within standard deviation, but that is a statistical-validity and internal-consistency concern, not circular reasoning. Similarly, the missing QK-LSTM Augmented SMILES control and the confounded SMILES-vs-Augmented-SELFIES contrast are experimental-design issues, not derivation circularity. No equation, fitted parameter, or self-citation makes the central claim equivalent to its inputs.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the SIDER labels, on the correctness of RDKit canonicalization and SELFIES conversion, on the representativeness of the random split, and on the validity of the top-3 model selection protocol. The hyperparameters (hidden dimensions, qubit counts, augmentation counts, and model selection) are fitted or chosen by hand and are not reported precisely, so the reader cannot separate modeling choices from the measured effect. No new entities are introduced.

free parameters (4)
  • LSTM hidden dimension = not reported (Optuna range 32-128 step 16)
    Chosen by Optuna on validation ROC-AUC; affects model capacity and the reported scores.
  • QK-LSTM hidden dimension and qubit count = not reported (range 8-32 step 4, qubits 3-5)
    Chosen by hand and Optuna; limits quantum expressiveness and runtime.
  • Augmentation sample count and selection rule = 20 generated, 5 shortest selected
    Hand-picked to limit training time; selecting the 5 shortest may bias toward shorter SELFIES strings.
  • Number of Optuna configurations and top-3 selection = 4 configurations, top 3 per configuration averaged
    Post-hoc selection of best models inflates reported ROC-AUC and reduces variance.
assumptions (4)
  • domain assumption SIDER side-effect labels are accurate and complete.
    The paper uses SIDER [20] as ground truth without external verification.
  • domain assumption RDKit canonicalization and SMILES-to-SELFIES conversion preserve molecular identity and validity.
    Used in Section III.A preprocessing; conversion errors would change inputs.
  • domain assumption The random 80/10/10 split is independent and representative across all conditions.
    Section III.C; if splits differ between conditions, comparisons are confounded.
  • domain assumption ROC-AUC averaged over top-3 selected models is a valid estimator of generalization performance.
    Section III.C; the selection procedure is not a standard unbiased estimator.

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Pith. "Pith review of Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTM." pith.science (2026). https://pith.science/paper/U2A7MI32

@misc{pith2026250420789,
  author       = {Pith},
  title        = {Pith review of: Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTM},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U2A7MI32}},
  note         = {Machine review of arXiv:2504.20789}
}
read the original abstract

Identifying molecular properties, including side effects, is a critical yet time-consuming step in drug development. Failing to detect these side effects before regulatory submission can result in significant financial losses and production delays, and overlooking them during the regulatory review can lead to catastrophic consequences. This challenge presents an opportunity for innovative machine learning approaches, particularly hybrid quantum-classical models like the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network. The QK-LSTM integrates quantum kernel functions into the classical LSTM framework, enabling the capture of complex, non-linear patterns in sequential data. By mapping input data into a high-dimensional quantum feature space, the QK-LSTM model reduces the need for large parameter sets, allowing for model compression without sacrificing accuracy in sequence-based tasks. Recent advancements have been made in the classical domain using augmented variations of the Simplified Molecular Line-Entry System (SMILES). However, to the best of our knowledge, no research has explored the impact of augmented SMILES in the quantum domain, nor the role of augmented Self-Referencing Embedded Strings (SELFIES) in either classical or hybrid quantum-classical settings. This study presents the first analysis of these approaches, providing novel insights into their potential for enhancing molecular property prediction and side effect identification. Results reveal that augmenting SELFIES yields in statistically significant improvements from SMILES by a 5.97% improvement for the classical domain and a 5.91% improvement for the hybrid quantum-classical domain.

Figures

Figures reproduced from arXiv: 2504.20789 by the authors.

Figure 1
Figure 1. Sample benzene molecule that relates to both the SMILES and [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. QK-LSTM architecture used for training; ( [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Overview of the LSTM process. B. LSTM Implementation The pre-processed molecular representations train a recur￾rent neural network based on the LSTM architecture. Each molecule, represented as a SMILES or SELFIES sequence, is first mapped through an embedding layer, where each token is embedded into a continuous vector space. The embedding dimension corresponds to the size of the molecular vocabulary. The embedded s… view at source ↗

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Reference graph

Works this paper leans on

43 extracted references · 29 canonical work pages

  1. [1]

    Analyzing learned molecular representations for property prediction,

    K. Yang et al., “Analyzing learned molecular representations for property prediction,” Journal of chemical information and modeling , vol. 59, no. 8, pp. 3370–3388, 2019

    work page 2019

  2. [2]

    A compact review of molecular property prediction with graph neural networks,

    O. Wieder et al. , “A compact review of molecular property prediction with graph neural networks,” Drug Discovery Today: Technologies , vol. 37, pp. 1–12, 2020

    work page 2020

  3. [3]

    Novel technologies for virtual screening,

    T. Lengauer et al. , “Novel technologies for virtual screening,” Drug discovery today, vol. 9, no. 1, pp. 27–34, 2004

    work page 2004

  4. [4]

    Automatic generation of complementary descrip- tors with molecular graph networks,

    C. Merkwirth et al., “Automatic generation of complementary descrip- tors with molecular graph networks,” Journal of chemical information and modeling, vol. 45, no. 5, pp. 1159–1168, 2005

    work page 2005

  5. [5]

    Strategies for pre-training graph neural networks,

    W. Hu et al., “Strategies for pre-training graph neural networks,” arXiv preprint arXiv:1905.12265, 2019

    arXiv 1905

  6. [6]

    A comprehensive survey on graph neural networks,

    Z. Wu et al., “A comprehensive survey on graph neural networks,” IEEE transactions on neural networks and learning systems , vol. 32, no. 1, pp. 4–24, 2020

    work page 2020

  7. [7]

    Large-scale comparison of machine learning methods for drug target prediction on chembl,

    Mayr et al., “Large-scale comparison of machine learning methods for drug target prediction on chembl,” Chemical science, vol. 9, no. 24, pp. 5441–5451, 2018

    work page 2018

  8. [8]

    Galactica: A large language model for science,

    R. Taylor et al., “Galactica: A large language model for science,” arXiv preprint arXiv:2211.09085, 2022

    arXiv 2022

Show all 43 references
  1. [9]

    Augmenting large language models with chemistry tools,

    A. M. Bran, S. Cox, O. Schilter, C. Baldassari, A. D. White, and P. Schwaller, “Augmenting large language models with chemistry tools,” Nature Machine Intelligence , vol. 6, no. 5, pp. 525–535, 2024

    2024

  2. [10]

    Large language models on graphs: A comprehensive survey,

    B. Jin, G. Liu, C. Han, M. Jiang, H. Ji, and J. Han, “Large language models on graphs: A comprehensive survey,” IEEE Transactions on Knowledge and Data Engineering , 2024

    2024

  3. [11]

    Smiles-bert: large scale unsupervised pre-training for molecular property prediction,

    Wang et al. , “Smiles-bert: large scale unsupervised pre-training for molecular property prediction,” in Proceedings of the 10th ACM interna- tional conference on bioinformatics, computational biology and health informatics, 2019, pp. 429–436

    2019

  4. [12]

    Chemformer: a pre- trained transformer for computational chemistry,

    R. Irwin, S. Dimitriadis, J. He, and E. J. Bjerrum, “Chemformer: a pre- trained transformer for computational chemistry,” Machine Learning: Science and Technology, vol. 3, no. 1, p. 015022, 2022. 2https://www.ncbi.nlm.nih.gov/books/NBK548049/

    2022

  5. [13]

    Git-mol: A multi-modal large language model for molecular science with graph, image, and text,

    P. Liu, Y . Ren, J. Tao, and Z. Ren, “Git-mol: A multi-modal large language model for molecular science with graph, image, and text,” Computers in biology and medicine , vol. 171, p. 108073, 2024

    2024

  6. [14]

    Aug- mentation is what you need!

    I. V . Tetko, P. Karpov, E. Bruno, T. B. Kimber, and G. Godin, “Aug- mentation is what you need!” in International Conference on Artificial Neural Networks. Springer, 2019, pp. 831–835

    2019

  7. [15]

    Smiles enumeration as data augmentation for neural network modeling of molecules,

    E. J. Bjerrum, “Smiles enumeration as data augmentation for neural network modeling of molecules,” arXiv preprint arXiv:1703.07076 , 2017

    2017 arXiv

  8. [16]

    Quantum machine learning for material synthesis and hardware security,

    C. Beaudoin, S. Kundu, R. O. Topaloglu, and S. Ghosh, “Quantum machine learning for material synthesis and hardware security,” in Pro- ceedings of the 41st IEEE/ACM International Conference on Computer- Aided Design, 2022, pp. 1–7

    2022

  9. [17]

    Advancing graph represen- tation learning with large language models: A comprehensive survey of techniques,

    Q. Mao, Z. Liu, C. Liu, Z. Li, and J. Sun, “Advancing graph represen- tation learning with large language models: A comprehensive survey of techniques,” arXiv preprint arXiv:2402.05952 , 2024

    2024 arXiv

  10. [18]

    De novo drug design by chemical language modeling,

    R. ¨Ozc ¸elik and F. Grisoni, “De novo drug design by chemical language modeling,” in An Introduction to Generative Drug Discovery . CRC Press, pp. 45–66

  11. [19]

    Moleculenet: a benchmark for molecular machine learning,

    Wu et al., “Moleculenet: a benchmark for molecular machine learning,” Chemical science, vol. 9, no. 2, pp. 513–530, 2018

    2018

  12. [20]

    The sider database of drugs and side effects,

    M. Kuhn et al., “The sider database of drugs and side effects,” Nucleic acids research, vol. 44, no. D1, pp. D1075–D1079, 2016

    2016

  13. [21]

    An introduction to roc analysis,

    T. Fawcett, “An introduction to roc analysis,” Pattern recognition letters, vol. 27, no. 8, pp. 861–874, 2006

    2006

  14. [22]

    Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules,

    D. Weininger, “Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules,” Journal of chemical information and computer sciences , vol. 28, no. 1, pp. 31–36, 1988

    1988

  15. [23]

    Self-referencing embedded strings (selfies): A 100% robust molecular string representation,

    M. Krenn et al., “Self-referencing embedded strings (selfies): A 100% robust molecular string representation,” Machine Learning: Science and Technology, vol. 1, no. 4, p. 045024, 2020

    2020

  16. [24]

    Long short-term memory,

    Hochreiter et al. , “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997

    1997

  17. [25]

    Finding structure in time,

    J. L. Elman, “Finding structure in time,” Cognitive science , vol. 14, no. 2, pp. 179–211, 1990

    1990

  18. [26]

    Expressibility and entan- gling capability of parameterized quantum circuits for hybrid quantum- classical algorithms,

    S. Sim, P. D. Johnson, and A. Aspuru-Guzik, “Expressibility and entan- gling capability of parameterized quantum circuits for hybrid quantum- classical algorithms,” Advanced Quantum Technologies , vol. 2, no. 12, p. 1900070, 2019

    2019

  19. [27]

    Quantum long short-term memory,

    S. Y .-C. Chen, S. Yoo, and Y .-L. L. Fang, “Quantum long short-term memory,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2022, pp. 8622–8626

    2022

  20. [28]

    Quantum kernel-based long short- term memory,

    Y .-C. Hsu, T.-Y . Li, and K.-C. Chen, “Quantum kernel-based long short- term memory,” arXiv preprint arXiv:2411.13225 , 2024

    2024 arXiv

  21. [29]

    Quantum machine learning in feature hilbert spaces,

    M. Schuld and N. Killoran, “Quantum machine learning in feature hilbert spaces,” Physical review letters, vol. 122, no. 4, p. 040504, 2019

    2019

  22. [30]

    Pennylane: Automatic differentiation of hybrid quantum-classical computations,

    V . Bergholm, J. Izaac, M. Schuld, C. Gogolin, M. S. Alam, S. Ahmed, J. M. Arrazola, C. Blank, A. Delgado, S. Jahangiri et al. , “Pennylane: Automatic differentiation of hybrid quantum-classical computations,” arXiv preprint arXiv:1811.04968 , 2018

    2018 arXiv

  23. [31]

    Optuna: A next-generation hyperparameter optimization framework,

    T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A next-generation hyperparameter optimization framework,” in The 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 2623–2631

    2019

  24. [32]

    Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented smiles,

    E. J. Bjerrum, C. Margreitter, T. Blaschke, S. Kolarova, and R. L.-R. de Castro, “Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented smiles,” Journal of Computer-Aided Molecular Design , vol. 37, no. 8, pp. 373–394, 2023

    2023

  25. [33]

    Retrosynthetic reaction prediction using neural sequence-to-sequence models,

    B. Liu, B. Ramsundar, P. Kawthekar, J. Shi, J. Gomes, Q. Luu Nguyen, S. Ho, J. Sloane, P. Wender, and V . Pande, “Retrosynthetic reaction prediction using neural sequence-to-sequence models,” ACS central science, vol. 3, no. 10, pp. 1103–1113, 2017

    2017

  26. [34]

    Attention is all you need,

    Vaswani et al., “Attention is all you need,” Advances in neural informa- tion processing systems , vol. 30, 2017

    2017

  27. [35]

    Chatmol: interactive molecular discovery with natural language,

    Z. Zeng, B. Yin, S. Wang, J. Liu, C. Yang, H. Yao, X. Sun, M. Sun, G. Xie, and Z. Liu, “Chatmol: interactive molecular discovery with natural language,” Bioinformatics, vol. 40, no. 9, p. btae534, 2024

    2024

  28. [36]

    Molgpt: molecular generation using a transformer-decoder model,

    V . Bagal, R. Aggarwal, P. Vinod, and U. D. Priyakumar, “Molgpt: molecular generation using a transformer-decoder model,” Journal of chemical information and modeling, vol. 62, no. 9, pp. 2064–2076, 2021

    2021

  29. [37]

    Litegem: Lite geometry enhanced molecular repre- sentation learning for quantum property prediction,

    Zhang et al. , “Litegem: Lite geometry enhanced molecular repre- sentation learning for quantum property prediction,” arXiv preprint arXiv:2106.14494, 2021

    2021 arXiv

  30. [38]

    Chemrl-gem: Geometry enhanced molecular representation learning for property prediction,

    Fang et al., “Chemrl-gem: Geometry enhanced molecular representation learning for property prediction,” arXiv preprint arXiv:2106.06130 , 2021

    2021 arXiv

  31. [39]

    Self-supervised graph transformer on large-scale molecular data,

    Rong et al., “Self-supervised graph transformer on large-scale molecular data,” Advances in Neural Information Processing Systems , vol. 33, pp. 12 559–12 571, 2020

    2020

  32. [40]

    Adverse drug reaction detection from social media based on quantum bi-lstm with attention,

    X. Wang, X. Wang, and S. Zhang, “Adverse drug reaction detection from social media based on quantum bi-lstm with attention,” IEEE Access , vol. 11, pp. 16 194–16 202, 2022

    2022

  33. [41]

    Quantum long short-term memory for drug discovery,

    L. Zhang, Y . Xu, M. Wu, L. Wang, and H. Xu, “Quantum long short-term memory for drug discovery,” arXiv preprint arXiv:2407.19852 , 2024

    2024 arXiv

  34. [42]

    Randomized smiles strings improve the quality of molecular generative models,

    J. Ar ´us-Pous, S. V . Johansson, O. Prykhodko, E. J. Bjerrum, C. Tyr- chan, J.-L. Reymond, H. Chen, and O. Engkvist, “Randomized smiles strings improve the quality of molecular generative models,” Journal of cheminformatics, vol. 11, pp. 1–13, 2019

    2019

  35. [43]

    What can large language models do in chemistry? a comprehensive benchmark on eight tasks,

    T. Guo, B. Nan, Z. Liang, Z. Guo, N. Chawla, O. Wiest, X. Zhang et al., “What can large language models do in chemistry? a comprehensive benchmark on eight tasks,” Advances in Neural Information Processing Systems, vol. 36, pp. 59 662–59 688, 2023

    2023

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