{"id":"63825189-ebf1-4e81-89d8-bf370f3f5403","arxiv_id":"2505.09783","paper_version":1,"verdict":"REJECT","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":6,"one_line_summary":"A connectivity matrix molecular representation with random forest feature pooling and adjusted R2 selection predicts pure component properties more accurately than group contribution features in the authors' best cross-validation folds.","lead":"This paper proposes a machine learning framework that represents molecules as connectivity matrices, ranks thousands of structural features with random forests, and predicts boiling point, critical properties, and liquid molar volume. It claims the approach beats traditional group contribution models while preserving interpretability through Shapley values.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Headline accuracy claims are based on the single best cross-validation fold, so the reported superiority over GC models is not established; a fold-averaged comparison is required.","rationale":"The reader's verdict identifies the best-fold reporting as the weakest point, and the manuscript text confirms this practice explicitly in Section 5.2 and in the notes above Tables 8–11. The supplementary fold tables show very large variation—for GPR-CM Tb, the test RMSE ranges from 20.24 to 119.50 K—which demonstrates that a single selected fold cannot support the headline improvements. This concern is load-bearing because every quantitative superiority claim in the abstract and conclusion is expressed as RMSE reductions, and those reductions are computed from selected folds. The feature-count inconsistency between the abstract (100), the ANN tables (160), and the GPR comparisons (424) compounds the problem by making it unclear which configuration actually produced each reported number. The CM representation and Shapley interpretability analysis are plausible contributions, and the joint conditional entropy comparison in Table 1 is a meaningful diagnostic, but those elements do not establish the central claim of predictive superiority. A fold-averaged comparison with error bars would settle the matter. Since the current evidence does not support the stated claims and the suggested re-evaluation is not present in the manuscript, the REJECT verdict remains appropriate.","tokens_in":26602,"tokens_out":2837,"duration_ms":28837,"concrete_test":"Re-run the ANN-CM, ANN-GC, GPR-CM, and GPR-GC experiments under identical data splits, and report mean test RMSE, mean R2, and their standard deviations or interquartile ranges over all folds instead of the best fold. Compute the per-fold paired difference (CM minus GC) and test whether the mean paired difference is significantly below zero using a paired test. Also report results at both the advertised feature count of 100 and the table count of 160, and identify which model and feature count produced each headline improvement.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim—that CM-based features outperform GC-based features for Tb, Lmv, Tc, and Pc—is supported almost entirely by test-set RMSE values taken from the best-performing fold. Section 5.2 states: 'We show the fold with the lowest RMSE on the test set among the six folds as the optimal one,' and Tables 8–11 explicitly choose the fold with the lowest test RMSE in 20-fold GPR cross-validation. This is not a minor reporting choice. Table S6 shows GPR-CM Tb test RMSE ranging from 20.24 to 119.50 K across the 20 folds, with R2 from 0.39 to 0.94, so the quoted 20.24 K is an extreme order statistic rather than a typical performance. If each model's best fold is selected independently, the reported comparison measures the best possible run rather than the model; if the same fold is forced for all models, the baselines may be assigned an unlucky fold. In either case, the 39.7–83.8% RMSE reductions in the abstract and conclusion are maximum-case values whose representativeness is unknown. The abstract's claim that feature count drops from 13,316 to 100 also conflicts with the results tables, which use 160 features for ANN-CM and 424 features for GPR-CM; the adjusted-R2 maximum at 100 features in Fig. 9 is not used in the reported predictions. These inconsistencies mean the quantitative case for superiority is not currently supported by the reported evidence.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes an end-to-end framework for estimating pure-component properties (normal boiling point Tb, liquid molar volume Lmv, critical temperature Tc, and critical pressure Pc) from molecular structure. Molecular structures are converted to connectivity-matrix-based submatrix features, random forest is used for supervised feature ranking and pooling, adjusted R2 is used to select the number of retained features, and ANN and GPR models are trained and compared with group-contribution (GC) baselines. Shapley values are used to interpret the most influential molecular substructures. The central claim is that the connectivity-matrix representation, combined with RF pooling and adjusted-R2 selection, outperforms GC-based ML models, with test RMSE reductions up to 83.8% and a feature-count reduction from 13,316 to 100 without loss of accuracy.","tokens_in":26882,"tokens_out":4733,"duration_ms":48297,"significance":"If the central claim were established, the framework would be a useful, interpretable alternative to GC-based property estimation, particularly because the representation is generated automatically from SMILES and the SHAP analysis connects features to molecular substructures. The paper deserves credit for comparing against external GC baselines from the literature and for reporting full per-fold results in the supplementary tables. However, the headline quantitative claims are currently not supported: every reported comparison uses the single best cross-validation fold, and the abstract's feature-count claim is contradicted by the feature counts used in the results tables. These issues are load-bearing because the claimed superiority over GC models rests entirely on these numbers.","major_comments":[{"comment":"All headline comparisons select the cross-validation fold with the lowest test RMSE. Section 5.2 states 'We show the fold with the lowest RMSE on the test set among the six folds as the optimal one,' and Tables 8–11 apply the same rule to 20-fold GPR. This is not a minor reporting choice: Table S6 shows GPR-CM Tb test RMSE ranging from 20.24 K (fold 14) to 119.50 K (fold 17), with R2 from 0.39 to 0.94; Tables S4, S5, and S7 show similarly large spreads. Reporting the best order statistic as the model's performance, and using the test set to select that fold, makes the test set a model-selection set and biases the reported metrics optimistically. The claimed RMSE reductions of 39.7–83.8% in the abstract and conclusion are therefore not established; a fold-averaged comparison with mean and standard deviation is required before any superiority claim can be made.","section":"§5.2, Tables 8–11 and Tables S4–S7"},{"comment":"The abstract claims that the feature pooling method reduces the number of features from 13,316 to 100 without compromising accuracy, but all reported prediction results use 160 features for ANN-CM (Tables 2, 3, and 7) and 424 features for GPR-CM (Tables 8–11). The adjusted-R2 maximum at 100 features in Fig. 9 is not the configuration used in any of the reported predictions. This internal inconsistency directly undermines the 'feature reduction to 100' claim and needs to be resolved: either the abstract should be corrected to reflect the 160-feature model actually used, or the results should be recomputed for the 100-feature configuration.","section":"Abstract; §5.2, Fig. 8 and Fig. 9"},{"comment":"Adjusted R2 as defined in Eq. (8) is a degree-of-freedom correction for linear models where k is the number of estimated parameters. Applying it to ANN and GPR models with k set to the number of input features is not a valid complexity penalty, because the model complexity of an ANN with 500 hidden neurons or a GPR with optimized kernel hyperparameters is not equal to the number of input features. While this issue does not directly affect the reported best-fold comparisons because the final models use a fixed feature count, it calls into question the stated rationale for the adjusted-R2-based feature selection.","section":"§3.3.1, Eq. (8)"}],"minor_comments":[{"comment":"Equation (20) is used twice: once for joint conditional entropy in §4.1 and once for the RBF kernel in §3.3.2. The equations should be renumbered to avoid ambiguity.","section":"§3.1 and §3.3.2, Eq. (20)"},{"comment":"The text near Fig. 14 refers to 'Table 12' when presenting the correspondence between molecular structures and features, but the table is numbered Table 15 in the manuscript; the cross-reference should be corrected.","section":"§5.3, Table 15"},{"comment":"The GPR-GC-Alshehri row reports an all-dataset RMSE of '0.00[61]' for Lmv, which appears to be a typographical error; the actual value should be stated.","section":"Table 9"},{"comment":"Eq. (21) defines the 'relative absolute error' (RAE), but the text refers to it as 'relative absolute percentage error' (RAPE); the terminology should be consistent.","section":"§5.2, Eq. (21)"},{"comment":"The manuscript would benefit from a data- and code-availability statement; none is currently provided, which makes it difficult to reproduce the reported feature-construction and model-training pipeline independently.","section":"Throughout"}],"recommendation":"major_revision","confidential_remarks":"The recommended major revision assumes the authors can supply a fold-averaged comparison (mean ± standard deviation) for both ANN and GPR models and can reconcile the feature-count discrepancy. If the fold-averaged results do not retain the claimed superiority over the GC baselines, the paper's central claim would be unsupported and rejection would be appropriate."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Dear [Colleague],\n\nWhat you should know: this paper has a plausible pipeline and some genuinely nice interpretability analysis, but the headline numbers are cherry-picked from the best cross-validation fold. Treat the RMSE reductions as unverified.\n\nThe new bit is combining their connectivity-matrix representation (from earlier work) with RF feature pooling and adjusted-R2 feature-count selection, then using SHAP to map features back to substructures. That is a reasonable extension, and the SHAP results (e.g., 1st_153 meaning benzene-ring carbon and correlating with Tb) are mechanistically sensible. The conditional entropy comparison in Table 1 is a good idea: showing that CM features reduce label uncertainty relative to GC features at the same dimensionality is evidence the representation carries more information. Credit where due: the authors also handle the GPR overfitting issue honestly and are transparent about sample-size differences with Cao et al.\n\nThe soft spots are serious. Section 5.2 explicitly says they show the fold with the lowest test RMSE as the optimal one, and Tables 8–11 do the same for GPR. Supplementary Table S6 shows GPR-CM Tb test RMSE ranging from 20.24 to 119.50 K across 20 folds, with R2 from 0.39 to 0.94. The quoted 20.24 K is an extreme order statistic, not typical performance. Without fold-averaged means and standard deviations, the claimed 39.7–83.8% reductions over GC baselines cannot be evaluated. This is not a minor reporting style choice; it is the main load-bearing evidence.\n\nThere are also internal inconsistencies. The abstract says features are reduced from 13,316 to 100, but every results table uses 160 features for ANN and 424 for GPR; the adjusted-R2 maximum at 100 features in Fig. 9 is not what the predictions use. Adjusted R2 itself is conceptually misapplied to ANN and GPR, where the degrees of freedom are not simply the number of input features. These are fixable, but as submitted they undercut the paper's quantitative claims.\n\nBottom line: if the authors re-run with proper cross-validation summaries, settle on one feature count, and release code, the framework could be a useful alternative to GC-based ML. As is, the evidence does not support the stated superiority. I would still send it to peer review rather than desk reject — the representation and interpretability work deserve scrutiny, and a good referee could push the authors to fix the evaluation. I probably wouldn't cite it in my own work until the numbers are re-reported.\n\nRecommendation: send to referees, but the revision bar is a proper fold-averaged evaluation.","headline":"Solid feature-generation work undermined by best-fold reporting; the quantitative claims are not supported as written.","tokens_in":27405,"tokens_out":2331,"would_cite":false,"duration_ms":22782,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper claims that a connectivity-matrix molecular representation, pooled by random forest and selected by adjusted $R^2$, predicts four pure-component properties more accurately than group-contribution features.","keywords":["pure component property estimation","connectivity matrix","group contribution","random forest feature selection","adjusted R2","Shapley values","molecular representation","thermodynamic properties"],"falsifier":"Take the reported 20-fold test results for the GPR model in Tables S4–S7 and replace the best-fold RMSE used in Tables 8–11 with the mean or median over all folds. If the connectivity-matrix model's mean test RMSE for critical temperature is not below the group-contribution baseline's mean, the claimed 83.8% reduction disappears.","tokens_in":26326,"feed_emoji":"🧪","tokens_out":8898,"duration_ms":80982,"temperature":0.7,"pith_summary":"This paper tries to establish that a molecular representation based on connectivity matrices—matrices recording each atom's identity and bond order—can replace hand-built group contributions as the input to machine-learned property models. It claims that, after random-forest feature ranking and adjusted $R^2$ selection, the resulting sparse feature vectors support more accurate prediction of normal boiling point, liquid molar volume, critical temperature, and critical pressure. The practical payoff would be a systematic, interpretable way to estimate properties of new and complex molecules without predefined functional-group libraries. On the paper's comparisons, the connectivity-matrix models reduce test-set RMSE by up to 83.8% against the group-contribution baselines and shrink the feature count from 13,316 to about 100 with no loss of accuracy.","feed_headline":"Matrix features cut property prediction error by up to 84%","feed_subtitle":"Automatically extracted molecular bond patterns beat predefined chemical groups for four key properties.","key_machinery":"The machinery is the connectivity matrix: a symmetric matrix whose diagonal entries are atomic numbers and whose off-diagonal entries are bond orders (single, double, aromatic, triple as 1, 2, 1.5, 3). Taking submatrices of radius one to four around each atom, the framework classifies each submatrix by its determinant and eigenvalue set, labels each distinct class, and counts how often each class appears; those counts are the sparse molecular feature vector. Random-forest impurity reduction ranks the features, and the adjusted $R^2$ of the resulting ANN model decides how many top-ranked first- and higher-order features to keep. Shapley values then assign each retained feature a marginal contribution to the prediction, converting the counts back into statements about which structural units raise or lower a property.","core_discovery":"The central claim is that the connectivity-matrix representation captures the local chemical environment of every atom—first, second, third, and fourth bonding shells—by extracting submatrices, classifying them by determinant and eigenvalues, and counting their frequencies, so that the one-to-one mapping between molecular structure and predicted property is stronger than the mapping produced by group-contribution features. With the same 424-feature budget, the paper finds that both a Gaussian process regressor and an artificial neural network trained on these features beat the corresponding group-contribution models on the test set for all four target properties; for critical temperature the test RMSE drops by 83.8%. It also reports that random-forest feature pooling plus adjusted $R^2$ selection reduces the full feature set from 13,316 to 100 features without degrading prediction, and that Shapley-value analysis ties the leading features to chemically meaningful structures such as benzene-ring carbons and chain methylene groups. The paper therefore claims not just a better fit but a more interpretable and more transferable route from molecular structure to pure-component properties.","pith_inferences":["If the reported advantage is evaluated across all cross-validation folds rather than the single best fold, the size of the improvement may shrink; the supplementary fold-level tables provide the data to test this directly.","Because the feature semantics are chemically interpretable, the same representation could support transfer learning to properties with few experimental measurements.","The lower joint conditional entropy reported for the connectivity-matrix dataset suggests that the representation will help isomeric discrimination; a targeted test on isomer sets would make this explicit.","Pooling features separately per property leaves open the possibility of multi-task models that share a common feature basis across properties, and the Shapley overlap between $T_b$ and $T_c$ suggests such sharing could be efficient."],"forward_implications":["With the feature dimension fixed at 424, connectivity-matrix features outperform group-contribution features inside both GPR and ANN models for $T_b$, $L_{mv}$, $T_c$, and $P_c$ on the test set.","The framework needs no predefined functional-group library; it can in principle generate features for any molecule that can be written as SMILES.","Feature pooling cuts the full 13,316-feature vector to around 100 features for the ANN without losing predictive accuracy, which lowers computational cost and improves interpretability.","Shapley analysis links the most influential features to identifiable structural units such as aromatic carbons, chain methylenes, and C–H environments, with directions that match physical reasoning about intermolecular forces.","The paper proposes that the same approach can serve as the property-estimation layer for mixture reconstruction and process integration modeling."],"supporting_citations":[{"why":"Supplies the 424-dimensional group-contribution dataset and the GPR-GC baseline used for comparison.","marker":"[10]"},{"why":"Introduces the connectivity-matrix submatrix construction that the proposed molecular features extend.","marker":"[35]"},{"why":"Provides the GPR-GC and GPR-WP-GC baseline results and the warping-function approach to group-contribution features.","marker":"[17]"},{"why":"Supplies corrected group-contribution GPR results used as an additional baseline.","marker":"[61]"},{"why":"Gives the Shapley-value axiomatic basis that the interpretability analysis relies on.","marker":"[33]"},{"why":"Describes the Top-N random-forest feature-selection procedure used for feature pooling.","marker":"[44]"},{"why":"Provides the cheminformatics toolkit used to parse canonical SMILES into connectivity matrices.","marker":"[40]"},{"why":"Supplies the grid-search cross-validation procedure used to set the random-forest hyperparameters.","marker":"[36]"}],"fun_headline_variants":["Connectivity matrix slashes property prediction error by 84%","Explainable ML cuts error 84%, features from 13k to 100","Bond-pattern features beat group contributions for key properties","ML maps structure to properties with 84% less error, 99% fewer features"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing assumption is that the cross-validation fold with the lowest test error, rather than an average fold, represents the model's performance; the paper applies this rule consistently, while the supplementary tables show test errors varying widely across folds for the same model.","fun_headline_variants_meta":{"raw":{"variants":["Connectivity matrix slashes property prediction error by 84%","Explainable ML cuts error 84%, features from 13k to 100","Bond-pattern features beat group contributions for key properties","ML maps structure to properties with 84% less error, 99% fewer features"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000658,"raw_usage":{"total_tokens":3048,"prompt_tokens":1018,"completion_tokens":2030,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":634,"completion_tokens_details":{"reasoning_tokens":1951}},"tokens_in":634,"tokens_out":2030,"duration_ms":14242,"temperature":1.0,"reasoning_tokens":1951,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T21:24:40.945059+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take the reported 20-fold test results for the GPR model in Tables S4–S7 and replace the best-fold RMSE used in Tables 8–11 with the mean or median over all folds. If the connectivity-matrix model's mean test RMSE for critical temperature is not below the group-contribution baseline's mean, the claimed 83.8% reduction disappears.","supporting_citations":[{"cited_title":"Alshehri, A.K","cited_arxiv_id":null,"evidence_quote":"Supplies the 424-dimensional group-contribution dataset and the GPR-GC baseline used for comparison."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Introduces the connectivity-matrix submatrix construction that the proposed molecular features extend."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the GPR-GC and GPR-WP-GC baseline results and the warping-function approach to group-contribution features."},{"cited_title":"Next generation pure component property estimation models: With and without machine learning techniques,","cited_arxiv_id":null,"evidence_quote":"Supplies corrected group-contribution GPR results used as an additional baseline."},{"cited_title":"Rozemberczki, L","cited_arxiv_id":null,"evidence_quote":"Gives the Shapley-value axiomatic basis that the interpretability analysis relies on."},{"cited_title":"Schonlau, R.Y","cited_arxiv_id":null,"evidence_quote":"Describes the Top-N random-forest feature-selection procedure used for feature pooling."},{"cited_title":"Landrum, Rdkit documentation, Release 1 (2013) 4","cited_arxiv_id":null,"evidence_quote":"Provides the cheminformatics toolkit used to parse canonical SMILES into connectivity matrices."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the grid-search cross-validation procedure used to set the random-forest hyperparameters."}],"review_version":1}