{"id":"137a33ef-c336-4b6c-b26d-bbfdcb17c602","arxiv_id":"2508.12653","paper_version":1,"verdict":"REJECT","confidence":"LOW","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"The abstract and full text describe entirely different studies, leaving the claimed QSAR model and docking scores unsubstantiated.","lead":"This preprint's abstract reports a deep learning QSAR model for Schistosoma drug targets, but the body text is an unrelated physics paper on black hole energy extraction. The abstract's results have no supporting content in the manuscript.","discovery_kind":"unclear","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The submitted full text contains none of the claimed QSAR content; the abstract's central claim is unsupported by the body.","rationale":"The reader's verdict is REJECT with low confidence, justified by the mismatch between the abstract and full text. My independent reading of the manuscript confirms that the body is an unrelated black-hole physics paper. The abstract promises a QSAR model, validation, docking, and inhibitor candidates, but the full text provides no such material: no dataset, no model, no training procedure, no docking protocol, and no molecular structures. The strongest claim, that the predicted molecules are credible starting points for SmTGR-targeted therapy, therefore has no evidentiary support in the submitted manuscript. I see no scientific argument in the abstract itself that can compensate for the absence of the full study. The correct disposition remains rejection; no adjustment to the reader's verdict is needed. This is an unusual case where the concern is not an error in a derivation or an overinterpretation of results, but the total absence of the claimed results from the submitted text.","tokens_in":6257,"tokens_out":2983,"duration_ms":30989,"concrete_test":"Run a script that strips the abstract and reference list from the submitted PDF and searches the remaining body for the strings 'QSAR', 'SmTGR', 'thioredoxin', 'docking', 'inhibitor', 'training', 'validation', 'RMSE', 'R2', and 'kcal/mol'. If no hits occur outside the abstract/references, the full text fails to contain the claimed study; additionally, locate any table or figure reporting docking scores or molecular structures, and if none exists, the -10.76±0.01 kcal/mol result is unverifiable.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The abstract's central claim is that a deep-learning QSAR model for SmTGR inhibitors was developed, validated with high predictive accuracy, and corroborated by molecular docking, with a best docking score of -10.76±0.01 kcal/mol. For this claim to hold, the full text should contain at least the dataset, feature representation, model architecture, training and validation procedure, performance metrics, docking protocol, and predicted inhibitor structures. The full text instead is a theoretical physics manuscript titled 'Electric Penrose process in spherically symmetric regular black holes with and without a cosmological constant', with Sections I-V, Eqs. (1)-(25), and references [1]-[62] devoted to ABG black holes. Nowhere in the body do terms such as 'QSAR', 'SmTGR', 'thioredoxin', 'docking', 'inhibitor', or any molecular structure appear. Consequently, the model, its validation, and the numerical docking result asserted in the abstract cannot be located, checked, or reproduced. The load-bearing condition for the central claim, namely that the submitted text reports the described study, is not met. This is not a methodological disagreement; it is a complete absence of the described work.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript as submitted consists of an abstract claiming a deep-learning QSAR study of SmTGR inhibitors for schistosomiasis, including high predictive accuracy, molecular docking validation, and a best docking score of -10.76±0.01 kcal/mol, followed by a full text that is a theoretical physics paper on the electric Penrose process in Ayon-Beato-Garcia black holes. The body contains no QSAR modeling, no SmTGR, no docking analysis, no molecular data, and no inhibitor structures. The claimed model and validation cannot be located or checked anywhere in the submitted text.","tokens_in":6507,"tokens_out":3105,"duration_ms":32783,"significance":"If the abstract's claims were substantiated, the work would deliver candidate SmTGR inhibitors and an end-to-end deep-learning/docking pipeline of practical interest for schistosomiasis drug discovery. However, the submitted body contains none of the described machinery: there is no dataset, no feature representation, no model architecture, no training or validation split, no performance metrics, no docking protocol, and no predicted structures. The only reproducible content is a black-hole physics analysis unrelated to the abstract, which appears internally coherent but does not support the declared biological application. Consequently, the significance of the claimed result cannot be assessed from the manuscript as submitted.","major_comments":[{"comment":"The central claim of the abstract—that a deep-learning QSAR model for SmTGR inhibitors was developed, validated, and used in docking, with a best score of -10.76±0.01 kcal/mol—has no supporting content in the full text. The full text is titled 'Electric Penrose process in spherically symmetric regular black holes with and without a cosmological constant' and consists of Sections I–V with Eqs. (1)–(25) on ABG black holes. None of the terms QSAR, SmTGR, thioredoxin, docking, inhibitor, or any molecular structure appears in the body, so the asserted model, validation, and docking result cannot be located, checked, or reproduced.","section":"Abstract vs. Full text"},{"comment":"The manuscript provides no dataset, no feature representation, no model architecture, no training/validation split, no performance metrics, no docking protocol, and no predicted inhibitor structures. Without these elements, the abstract's claims of 'high predictive accuracy' and the docking score are entirely unsupported. This is not a methodological disagreement; it is the complete absence of the described study from the submitted text.","section":"Full text, Sections II–V"},{"comment":"The docking score '-10.76±0.01 kcal/mol' is presented without any methods, software, receptor/ligand preparation, scoring function, or uncertainty analysis, so the uncertainty estimate has no basis in the manuscript. Similarly, the statement that 2D and 3D visualization confirmed interactions with commercial drugs has no corresponding figures, structures, or analysis anywhere in the text.","section":"Abstract, numerical claim"}],"minor_comments":[{"comment":"The abstract should be checked for typographical consistency: it writes '-10.76+-0.01' where a plus-minus sign is intended.","section":"Abstract"},{"comment":"The title, author list, and affiliations of the full text pertain to black-hole astrophysics and are wholly unrelated to the declared subject of the abstract; as submitted, the manuscript cannot be read as a coherent single document without an explicit explanation for the mismatch.","section":"Full text, title and headers"}],"recommendation":"reject","confidential_remarks":"The mismatch between the abstract and the full text is total: the body contains none of the claimed QSAR, docking, or SmTGR work. This may be a submission or upload error, in which case the authors should be given the opportunity to submit the correct manuscript under the journal's procedures. However, as submitted, the article contains none of the central claims' evidentiary support and cannot be accepted or revised within its current scope."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nHere is the short version: the abstract and the full text are two different papers. The abstract describes a deep-learning QSAR model for SmTGR inhibitors, with validation and docking scores. The full text is a theoretical physics manuscript on the electric Penrose process in ABG black holes. There is no QSAR content anywhere in the body. No dataset, no model architecture, no docking protocol, no predicted inhibitors. The claimed docking score of -10.76 kcal/mol is literally unsupported.\n\nNow credit where it is due: the physics manuscript looks internally coherent. It derives equations of motion, effective potentials, and energy-extraction efficiencies for regular black holes, and it appears to make a legitimate new contribution to that subfield. The authors of that part clearly know what they are doing. But that is a separate paper, and it is not what the abstract claims.\n\nThis is not a methodological quibble. The load-bearing condition for the abstract's central claim—that a QSAR model was built and validated—is not met. There is nothing to referee on the QSAR side. The mismatch is complete, and it is not explainable by a missing section or a formatting error. The text itself explicitly identifies itself as a black-hole paper, with sections and references dedicated to that topic.\n\nProportionately, the only soft spot is the entire absence of the described work. That is a fatal flaw for the submission as it stands. The physics paper itself may deserve peer review somewhere, but not here, and not under this abstract.\n\nWho is this paper for? Not a QSAR reader. Not a drug-discovery reader. The only reader who could get value from the full text is someone in black-hole physics, but they would need the correct title and abstract. As submitted, it is not a serious submission for q-bio.BM.\n\nRecommendation: desk reject. The editor should not send this to peer review as a QSAR paper. If the authors intended to submit the physics paper, they should do so with the matching abstract. If they intended the QSAR study, the manuscript is missing entirely.\n\nBest,\n[You]","headline":"The submitted manuscript is not a QSAR paper: the body is an unrelated black-hole-physics manuscript, so the abstract's claims have zero support in the text.","tokens_in":6975,"tokens_out":1762,"would_cite":false,"duration_ms":18057,"reading_group":"no","serious_thinker":"no","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":["04.70.-s"],"model":"deepseek-v4-flash","headline":"The abstract reports a deep-learning QSAR model that flags SmTGR inhibitors with high accuracy and a top docking score of -10.76 ± 0.01 kcal/mol, but the full text is a separate physics paper on electric Penrose energy extraction from ABG…","keywords":["SmTGR","QSAR","deep learning","molecular docking","schistosomiasis","electric Penrose process","ABG black hole","energy extraction"],"falsifier":"Settle the QSAR claim by opening the manuscript to any section after the abstract: it contains no training dataset of SmTGR compounds, no deep-learning architecture, no performance metrics, and no docking protocol, so the reported accuracy and -10.76 ± 0.01 kcal/mol score cannot be reproduced from the text as submitted. For the physics claim, recompute the efficiency ratio defined in Eq. (23) for ABG and RN black holes at $Q\\to 0$ and $\\Lambda\\to 0$ and check whether it approaches 23/8.","tokens_in":6147,"feed_emoji":"💊","tokens_out":12481,"duration_ms":107182,"temperature":0.7,"pith_summary":"The submission's abstract states a drug-discovery result: a deep-learning QSAR model for the Schistosoma mansoni enzyme SmTGR, validated for high predictive accuracy, predicted novel inhibitors whose docking scores reached -10.76 ± 0.01 kcal/mol, with interaction patterns similar to commercial drugs. The full text, however, is a theoretical physics paper investigating the electric Penrose process in Ayón–Beato–García (ABG) regular black holes, with and without a cosmological constant, finding a larger negative-energy region and higher energy-extraction efficiency than Reissner–Nordström black holes, with a maximum efficiency ratio of about 23/8. The two texts share no methods, data, or results. Read in good faith as the abstract describes it, the paper's contribution would be a credible computational pipeline for SmTGR-targeted schistosomiasis therapy; as the body describes it, the contribution is a black-hole energy-extraction analysis. Because the full text contains none of the QSAR model's training data, architecture, or docking protocol, only the physics result is present in the visible manuscript.","feed_headline":"Deep-learning QSAR flags SmTGR inhibitors at -10.76","feed_subtitle":"Abstract promises docking-validated hits; the full text instead analyzes ABG black-hole energy extraction.","key_machinery":"For the abstract's intended study, the load-bearing object would be the deep-learning QSAR model itself, a mapping from molecular descriptors to predicted SmTGR inhibition, with docking scores as the downstream validation; none of that model's data or code appears in the text. For the body's study, the load-bearing machinery is the ABG metric function $f(r)=1-\\frac{2Mr^2}{(r^2+Q^2)^{3/2}}+\\frac{Q^2r^2}{(r^2+Q^2)^2}$ with electric potential $A_t(r)=-\\frac{r^5}{2Q}\\left(\\frac{3M}{r^5}+\\frac{2Q^2}{(r^2+Q^2)^3}-\\frac{3M}{(r^2+Q^2)^{5/2}}\\right)$, feeding the effective potential $V_{\\mathrm{eff}}(r)=-\\bar{q}A_t+\\sqrt{f(r)(\\ell^2/r^2+1)}$ whose negative sign locates the negative-energy region, and the efficiency formula $\\eta=\\frac{1}{2}\\left(\\sqrt{1-f(r)}-1\\right)+\\hat{\\bar q}A_t$ that quantifies energy extraction. This second machinery is what the manuscript actually develops.","core_discovery":"On the abstract's own terms, the paper claims that a deep-learning QSAR model for SmTGR inhibitors achieves high predictive accuracy, yields novel predicted inhibitors, and is validated by molecular docking with a best score of -10.76 ± 0.01 kcal/mol and 2D/3D interaction profiles comparable to commercial drugs. On the full text's own terms, the paper claims that the electric Penrose process in ABG regular black holes—solutions of Einstein gravity coupled to nonlinear electrodynamics—creates a negative-energy region larger than that of Reissner–Nordström black holes, enabling energy extraction at greater distances and higher efficiency, with a maximum efficiency ratio of approximately 23/8 even for astrophysically small charge and cosmological constant. These are two unrelated claims; the visible text supports only the second, and neither is connected to the other.","pith_inferences":["A likely editorial inference is that the submission is a text-corrupted upload: the abstract and body probably come from two unrelated manuscripts, and the QSAR result should be treated as unverified until a corrected full text is supplied.","If the intended study is the QSAR one, the natural next step would be to synthesize or obtain the top-ranked inhibitors and measure SmTGR activity in vitro, which would directly test the model's predictions.","For the physics half, one could extend the electric Penrose analysis to rotating regular black holes to see whether the efficiency advantage over Reissner–Nordström persists outside spherical symmetry."],"forward_implications":["If the abstract's QSAR and docking results are taken at face value, the predicted compounds would be concrete starting points for experimental SmTGR inhibition assays and lead optimization.","If the abstract's accuracy and docking scores survive independent reproduction, the workflow would support the broader thesis that deep-learning QSAR can accelerate discovery of antiparasitic leads.","If the full-text physics result is correct, ABG regular black holes would extract energy from charged particles more efficiently than Reissner–Nordström black holes, even at astrophysically realistic small charges.","If the 23/8 efficiency ratio holds, it could serve as a signature distinguishing regular from singular black-hole spacetimes in high-energy astrophysical processes."],"supporting_citations":[{"why":"Introduces the electric Penrose process and its energy-extraction formalism, which the paper extends to regular black holes.","marker":"[27, 28]"},{"why":"Extends the electric Penrose process to spacetimes with a cosmological constant, providing the setup for the ABG-dS analysis.","marker":"[29]"},{"why":"Derives the Ayón–Beato–García regular black hole solution and its electric potential, the spacetime used throughout.","marker":"[37, 38]"},{"why":"Supplies the simplifying assumptions (neutral parent, e1 ≈ 1, vanishing radial velocities) on which the efficiency formula rests.","marker":"[57]"}],"fun_headline_variants":["ABG black holes outperform Reissner–Nordström in energy extraction","Electric Penrose process gains efficiency in ABG regular black holes","ABG black holes expand negative-energy region for greater energy extraction","Higher energy extraction efficiency from ABG black holes via Penrose process"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the full text belongs to the abstract's QSAR study; since the full text is instead a physics paper on black-hole energy extraction, the abstract's model, validation, and docking results have no evidentiary support in the visible manuscript.","fun_headline_variants_meta":{"raw":{"variants":["ABG black holes outperform Reissner–Nordström in energy extraction","Electric Penrose process gains efficiency in ABG regular black holes","ABG black holes expand negative-energy region for greater energy extraction","Higher energy extraction efficiency from ABG black holes via Penrose process"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000697,"raw_usage":{"total_tokens":3130,"prompt_tokens":905,"completion_tokens":2225,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":521,"completion_tokens_details":{"reasoning_tokens":2151}},"tokens_in":521,"tokens_out":2225,"duration_ms":19430,"temperature":1.0,"reasoning_tokens":2151,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T17:18:54.162827+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Settle the QSAR claim by opening the manuscript to any section after the abstract: it contains no training dataset of SmTGR compounds, no deep-learning architecture, no performance metrics, and no docking protocol, so the reported accuracy and -10.76 ± 0.01 kcal/mol score cannot be reproduced from the text as submitted. For the physics claim, recompute the efficiency ratio defined in Eq. (23) for ABG and RN black holes at $Q\\to 0$ and $\\Lambda\\to 0$ and check whether it approaches 23/8.","supporting_citations":[],"review_version":2}