Journal of Chemical Information and Modeling 最新文献

{"title":"Predicting Synergistic and Antagonistic Micellization Effects in Surfactant Blends for Optimized Formulation Design","authors":"Sofía González-Núñez,&nbsp;Carlos Amador,&nbsp;Mariano Martín","doi":"10.1021/acs.jcim.6c02030","DOIUrl":"10.1021/acs.jcim.6c02030","url":null,"abstract":"<p>Surfactant mixturesexhibit non-ideal interactions that governtheir macroscopic behavior, yet their prediction is limited by theneed for experimentally derived interaction parameters. Within Rubingh’sRegular Solution Approximation (RST), these interactions are describedby the parameter β, which is typically obtained by fitting experimentalmixture data, restricting its applicability in large-scale formulationdesign. In this work, a computational hybrid approach combining machinelearning with thermodynamic modeling is introduced to enable end-to-endprediction and optimization of surfactant mixtures. A dataset of interactionparameters derived from experimental binary surfactant mixtures wasconstructed to train predictive models for β, together withmodels for pure-component properties. The predicted, composition-dependentβ values are incorporated into the RST framework to estimatemixture behavior without requiring experimental mixture data, whiletreating the resulting activity coefficients as effective quantities.The overall framework was further validated against a comprehensivedataset of binary surfactant mixtures and further evaluated on ternarysystems, showing good agreement with experimental values across diverseinteraction regimes and compositions. In addition, a multi-objectiveoptimization strategy is introduced to identify formulations thatbalance performance and molecular complexity, enabling efficient explorationof chemical space. A case study on representative systems furtherdemonstrates the applicability of the framework for computationalformulation design. Overall, the proposed approach provides a scalablecomputational tool for surfactant mixture design, reducing relianceon experimental parameterization while enabling rapid virtual screeningof formulation space.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12463–12476"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drug-Like, Shape Profiling and Diversity Assessment of the Molport in-Stock Compound Database","authors":"Andrea Altieri,&nbsp;Normunds Kinne,&nbsp;Andrievs Auzins,&nbsp;Ratis Brodezonoks,&nbsp;Martin Razuks-Ebels,&nbsp;Artis Erglis,&nbsp;Ieva Kluga,&nbsp;Janis Oslejs,&nbsp;Kristine Skulme,&nbsp;Kristaps Boss,&nbsp;Inga Salmina,&nbsp;Uldis Ledainis,&nbsp;Ieva Kovalenko,&nbsp;Roland Capars,&nbsp;Elza Dzene,&nbsp;Helena Daiga Litvina,&nbsp;Enia Tina Susta,&nbsp;Dita Duka,&nbsp;Inese Sulca,&nbsp;Kristine Rauska-Zeltina,&nbsp;Kirils Bidzans,&nbsp;Diana Zelencova-Gopejenko","doi":"10.1021/acs.jcim.6c01636","DOIUrl":"10.1021/acs.jcim.6c01636","url":null,"abstract":"<p>Publicly accessible compound repositories are essentialfor virtualscreening and hit identification, but their continuous growth bringsinconsistent structure representations, reactive groups, and a computationalburden from standardizing millions of entries. Many repositories alsocontain outdated or out-of-stock compounds that are no longer procurable,weakening the practical value of screening results. We present a 20-yeareffort to build and maintain an open-access compound repository: theMolport in-stock catalog, now a collection of 6.1 M purchasable compoundssourced exclusively from validated suppliers. We describe the cheminformaticsworkflow applied to this catalog, which harmonizes structures fromopenly accessible, heterogeneous sources into a standardized format,maintains dynamic currency by including only compounds confirmed instock and removing outdated or unavailable molecules, and profilesphysicochemical properties, druglikeness, three-dimensional (3D) shape,and structural diversity to produce chemically meaningful, medicinallyrelevant subsets. The result is an organized, up-to-date view of purchasablechemical space that supports faster and more reliable identificationof experimentally accessible hit candidates for downstream screeningcampaigns.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12839–12851"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energetically Anchored Machine-Learning Interatomic Potential Embeddings for Reliable Hydrogen Evolution Electrocatalyst Prediction","authors":"Ching Lin,&nbsp;Po-Wen Chen,&nbsp;Tien-Hsiang Hsueh","doi":"10.1021/acs.jcim.6c01720","DOIUrl":"10.1021/acs.jcim.6c01720","url":null,"abstract":"<p>Reliable prediction of hydrogen adsorption free energy(Δ<em>G</em>_H) is essential for acceleratingelectrocatalystdiscovery for the alkaline hydrogen evolution reaction (HER), yetpractical machine-learning workflows remain limited by inconsistentenergetic definitions, heterogeneous density functional theory (DFT)protocols, and poor out-of-distribution (OOD) generalization. Here,we present an energetically anchored machine-learning framework thatintegrates machine-learning interatomic potential (MLIP)-derived energeticdescriptors with pretrained Crystal Hamiltonian Graph Neural Network(CHGNet) latent embeddings to predict DFT-defined hydrogen adsorptionenergetics across chemically diverse catalyst surfaces. The frameworkemploys protocol-consistent single-point MLIP energy evaluation onreference geometries to construct thermodynamically aligned energeticdescriptors while combining them with structural representations throughgradient-boosting regression. Using curated data sets from CatalysisHub and AQCat25 containing single-, bi-, and trimetallic adsorptionsystems, the embedding-augmented energetic model achieved high predictiveaccuracy (<em>R</em>² = 0.976, MAE = 0.054 eV,RMSE = 0.105 eV) under site-level data splitting, substantially outperformingembedding-only and physicochemical-descriptor-only models. Shapleyadditive explanations analysis revealed a hierarchical learning mechanismin which the protocol-consistent energetic descriptor serves as thedominant thermodynamic anchor, whereas structural descriptors providesecondary refinements that improve adsorption-energy discrimination,particularly near the thermoneutral regime relevant to catalyst screening.Explicit evaluation of MLIP-only geometry workflows further demonstratedthat the framework retains meaningful adsorption-energy ranking capabilitydespite degradation introduced by MLIP-relaxed geometries. AdditionalOOD and transfer-learning analyses showed that predictive robustnessdepends strongly on the balance between compositional diversity andreference-protocol consistency. These results establish energeticallyanchored MLIP embeddings as an effective strategy for scalable post-DFTadsorption-energy refinement and data-efficient electrocatalyst screeningwhile clarifying the practical limitations of MLIP-driven workflowsfor heterogeneous catalysis.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12375–12390"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148552318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in the Discovery and Design of Ionic Liquids for Thermal Energy Applications","authors":"Pawan Chhipa,&nbsp;Zachariah S. Baird,&nbsp;Dinis O. Abranches,&nbsp;Oliver Järvik","doi":"10.1021/acs.jcim.6c02407","DOIUrl":"10.1021/acs.jcim.6c02407","url":null,"abstract":"<p>This review addressesa critical need in the emerging intersectionof digital chemistry and material properties by critically evaluatingthe current capabilities and future prospects of applying machinelearning (ML) to ionic liquids (ILs) for thermal energy storage (TES)applications. We examine available data sets for key thermophysicalproperties relevant to TES, including thermal conductivity, heat capacity,density, viscosity, surface tension, melting point, enthalpy, CO₂ solubility, and toxicity. Particular emphasis is placed onchemical space coverage, biases, and practical limitations. We comparedifferent modeling approaches, spanning from conventional QSPR/QSARand group-contribution methods to contemporary ML approaches, includingkernel models, ensemble methods, graph neural networks (GNNs), anddeep learning architectures. The impact of molecular representationsand descriptor choices and validation strategies in determining modelrobustness and transferability is examined. To structure this assessment,we introduce a three-tier property-selection framework for IL-basedTES and apply a six-criterion evaluation checklist consistently acrossall nine reviewed properties. The analysis is supported by literature-summarytables that consolidate data sets, chemical space coverage, modelingapproaches, and validation practices across different property domains.Finally, we outline recommendations for data set standardization,open sharing of data and code, and the establishment of benchmarkvalidation protocols to improve reproducibility and accelerate thediscovery of ILs for TES applications.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12231–12253"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148858579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Charge-Driven Fibril Recognition and Covalent Disruption of Aβ42 by Paddlewheel Diruthenium Complexes","authors":"Alejandro Feito,&nbsp;Andrés R. Tejedor,&nbsp;Alberto Ocana,&nbsp;Aarón Terán,&nbsp;Antonello Merlino,&nbsp;Daniela Marasco,&nbsp;Santiago Herrero,&nbsp;Jorge R. Espinosa","doi":"10.1021/acs.jcim.6c02388","DOIUrl":"10.1021/acs.jcim.6c02388","url":null,"abstract":"<p>The inhibition of Aβ₄₂ (β-amyloid)fibrilformation is a key therapeutic strategy in Alzheimer’s diseaseresearch. Paddlewheel diruthenium complexes have shown promising activityagainst Aβ₄₂ aggregation and preformed fibril disaggregation,yet their molecular mode of action remains poorly understood. In thiswork, we perform atomistic simulations to explore how charge modulationinfluences the interactions of three analogous paddlewheel dirutheniumcomplexes, the parent neutral complex [Ru₂Cl(D-<em>p</em>-FPhF)(O₂CCH₃)₃], andits anionic [Ru₂Cl₂(D-<em>p</em>-FPhF)(O₂CCH₃)₃]⁻ and cationic[Ru₂(D-<em>p</em>-FPhF)(O₂CCH₃)₃]⁺ counterparts (D-<em>p</em>-FPhF^– is the <em>N</em>,<em>N</em>′-bis(4-fluorophenyl)formamidinatoligand) with Aβ₄₂. Our results indicate that electrostatictuning governs binding affinity and the extent of interaction acrossthe Aβ₄₂ fibril surface. As the complexes’charge changes from −1 to +1, the interaction pattern shiftsfrom localized contacts to widespread, multi-site engagement encompassingkey charged, aromatic, and hydrophobic regions of Aβ₄₂. This enhanced binding correlates with longer-lived, thermodynamicallystable interactions at the fibril interface, which effectively lowerthe free energy penalty for fibril disassembly. Overall, our findingspropose a mechanism in which charge-dependent activation through ligandexchange enhances fibril recognition and promotes disruptive bindingmodes, demonstrating the potential of charge-tunable diruthenium complexesas therapeutic modulators of Aβ₄₂ fibril stability.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12772–12782"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative AI-Assisted Molecular Design of AChEIs","authors":"Dhairiya Agarwal,&nbsp;Rakesh Kumar Gautam,&nbsp;Vaibhav Gupta,&nbsp;Vishal Chaurasia,&nbsp;Anju Sharma,&nbsp;Tanmaykumar Varma,&nbsp;Gyan Modi,&nbsp;Sankar K. Guchhait,&nbsp;Prabha Garg","doi":"10.1021/acs.jcim.6c02233","DOIUrl":"10.1021/acs.jcim.6c02233","url":null,"abstract":"<p>Alzheimer’s disease (AD) remains a major neurodegenerativedisorder with limited therapeutic options, while currently approvedacetylcholinesterase inhibitors (AChEIs), such as donepezil, are associatedwith adverse effects including cardiotoxicity. Here, we integrateda deep learning-based framework to design novel AChEIs candidateswith improved predicted cardiac safety. A reinforcement learning-guidedGraphVAE (RL-GraphVAE) was employed for target-biased molecular generation.The generated compounds were prioritized through CardiotoxPred-basedcardiotoxicity screening, molecular docking, triplicate moleculardynamics simulations, and chemical synthesis. Experimental evaluationidentified D0209 as a lead molecule exhibiting a noncompetitive inhibitionmechanism. Notably, compared with donepezil, D0209 showed ∼43-foldlower hERG channel inhibition, indicating an improved cardiac safetyprofile. Overall, this integrated computational and experimental workflowdemonstrates the utility of generative modeling for the discoveryof novel leads with improved predicted safety profiles, providingpromising starting points for further optimization and biologicalevaluation.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12723–12739"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CUE: A Chemical Uncertainty-Aware Embedding Framework for Multimodal Drug Selectivity Prediction","authors":"Jin Hyuk Kim,&nbsp;Gyeong Hwan Kim,&nbsp;Hyeon Jun Park,&nbsp;Jonghwan Choi","doi":"10.1021/acs.jcim.6c01761","DOIUrl":"10.1021/acs.jcim.6c01761","url":null,"abstract":"<p>Accurate prediction of drug selectivity is critical forprioritizingcandidate compounds with reduced off-target effects in AI-based drugdiscovery. Although selectivity can be inferred indirectly from drug–targetaffinity (DTA) prediction, cumulative errors from affinity predictionsacross multiple targets can reduce reliability, motivating the developmentof methods that directly predict compound-level selectivity. We proposea Chemical Uncertainty-aware Embedding (CUE) framework that integratesmolecular fingerprints and 2D molecular structure image embeddings.The two modalities are combined via Loss Trajectory Analysis for Uncertainty(LTAU)-based weighted fusion, which adaptively reweights featuresaccording to their predictive reliability. Across eight benchmarkdata sets, CUE achieved RMSE values ranging from 0.163 to 1.691 andoutperformed affinity-based and quantitative structure–activityrelationship (QSAR) baseline models. Furthermore, a virtual screeningcase study for EGFR(T790M/C797S) inhibitors further demonstrated itsutility for identifying selective hits. The source code is availableat https://github.com/jjjabcd/CUE.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12402–12416"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale Interpretable Deep-Learning Framework for the Identification and Visualization of Deformation Stages in Molecular Dynamics Trajectory","authors":"Zhengwu Long,&nbsp;Xinyu Wang,&nbsp;Lingyun You","doi":"10.1021/acs.jcim.6c01426","DOIUrl":"10.1021/acs.jcim.6c01426","url":null,"abstract":"<p>Microscopic deformation stage recognitionfrom moleculardynamics(MD) trajectories is crucial for understanding the evolution of materialdamage; however, traditional empirical analysis and black-box singledeep-learning models lack both high-throughput spatiotemporal modelingand transparent physical interpretability. This work develops a multiscaleinterpretable deep-learning framework to automatically classify elastic,plastic, and fracture stages from uniaxial tensile MD trajectoriesand physically decode model decision logic. First, a 3D atomic trajectoryis converted to 2D gray-scale image sequences; a CNN-LSTM hybrid architectureis built to jointly extract spatial atomic textures and long-timedeformation dynamics, reaching 98.8% test accuracy and far surpassingspatial-only CNN baselines in both supervised classification and unsupervisedclustering. More importantly, a hierarchical multiscale interpretabilitytoolkit, including multilayer feature heatmaps, smoothed gradientsaliency maps, gradient-weighted class activation maps, and regularizedSHAP attribution, is integrated to quantify positive/negative featurecontributions and localize model focus regions. The visualized attentionzones perfectly match core physical fields (von Mises strain, atomicdisplacement, and nonaffine deformation), resolving the explainabilitygap of conventional MD data mining pipelines. This work establishesa generalizable, trustworthy AI paradigm that connects data-drivenprediction to intrinsic microscale mechanical mechanisms for computationalmaterial research.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12326–12345"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149010290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AGENTi: APCI-ESI Analyte Ionization Fidelity Pre-Screening Workflow using Multimodel Inference","authors":"Mithony Keng,&nbsp;Kenneth M Merz, Jr.","doi":"10.1021/acs.jcim.6c01085","DOIUrl":"10.1021/acs.jcim.6c01085","url":null,"abstract":"<p>Ionization sources convert neutral analytes into ions,enablingtheir subsequent separation and detection by mass spectrometry. Dependingon the technique, this mechanism occurs within a solution or in thegas phase. The two most common methods to generate ions are electrosprayionization (ESI) and atmospheric-pressure chemical ionization (APCI),respectively. Both ESI and APCI can generate the same resultant ionspecies; however, it has been shown that preferential charge sitescan change in response to an ionization source environment. Therefore,an additional quality control is warranted <em>a priori</em> to delineate erroneous analyte identification and/or unequivocalstructural assignment. For protonation, protomer fidelity can be assessedby comparing the ion-mobility collision cross section (CCS) valuesobtained for both ESI and APCI. In this work, we utilized proxy standards(i.e., controls) derived from the available literature to developa proof-of-concept multimodel inference application called AGENTito probe differences in ionization for high-throughput protomer shiftexploratory analysis. AGENTi uses in sequence the TensorFlow Autoencoderanomaly detection and the YDF Gradient Boosted Tree machine learningframeworks to generate more balanced and robust anomaly scores. Wefound that using a multimodel screening approach can reduce falsepositive capture at an acceptable loss of true positives. Moreover,we expect to achieve a better recovery of true positives with an improvementon the current Gradient Boosted Tree regression model for ΔCCSprediction as more ESI-APCI dual data are obtained for training. Inall, we demonstrated in this pilot study that our foundation modelfor <em>a priori</em> probing of ionization pathway fidelityis feasible and efficient with AGENTi. The AGENTi prototype applicationand documentation are accessible at https://github.com/mitkeng/AGENTi.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12316–12325"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149049658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Untranslated Region-FNex: An Integrative Multimodal Framework for Functional Untranslated Region Variant Prioritization","authors":"Yaning Li,&nbsp;Fang Ge,&nbsp;Lei Pan,&nbsp;Ming Zhang","doi":"10.1021/acs.jcim.6c01582","DOIUrl":"10.1021/acs.jcim.6c01582","url":null,"abstract":"<p>Genetic variants in untranslated regions (UTRs) can alterpost-transcriptionalregulation, yet functional interpretation remains difficult becauseregulatory mechanisms differ between 5′UTR and 3′UTRcontexts and are not fully captured by generic noncoding-variant scores.We developed UTR-FNex, an interpretable machine-learning frameworkfor prioritizing candidate functional variants in both UTR regions.We first constructed MetaUTR, a harmonized resource of approximately200,000 UTR variant loci, and derived model-development sets containing14,627 3′UTR and 3533 5′UTR variants after confidence-basedfiltering. UTR-FNex integrates region-specific sequence, RNA-structure,regulatory-motif, miRNA, conservation, and DNABERT-derived allelerepresentations, with a neural feature enhancement (NFE) module providingadditional interaction summaries. Across 10 repeated random hold-outs,mean AUROCs were 0.876 for 3′UTR and 0.974 for 5′UTR.Under gene-grouped evaluation, AUROCs decreased to 0.769 and 0.672,respectively, indicating substantial context dependence, especiallyfor 5′UTR. In an external prostate-cancer 3′UTR cohort,UTR-FNex achieved an AUROC of 0.720 (95% CI 0.690–0.750), comparedwith 0.696 (95% CI 0.660–0.733) for FunUV; however, FunUV achievedhigher AUPRC and stronger top-<em>k</em> enrichment. A TP53rs78378222 case study connected model sensitivity to a known polyadenylation-signalcontext, whereas RNAfold-derived differences were window-dependentand were treated as exploratory. These results support UTR-FNex asa multimodal prioritization framework while defining important limitsin cross-gene and cross-source generalization. Source code is availableat https://github.com/liyaning233/UTR-FNex, and MetaUTR is deposited at 10.5281/zenodo.19719670.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12989–13004"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}