Discovering H-bonding rules in crystals with inductive logic programming.

Howard Y Ando; Luc Dehaspe; Walter Luyten; Elke Van Craenenbroeck; Henk Vandecasteele; Luc Van Meervelt

doi:10.1021/mp060034z

Home/Archive/PubMed

PubMed · 17140254

Discovering H-bonding rules in crystals with inductive logic programming.

Howard Y Ando·Luc Dehaspe·Walter Luyten·Elke Van Craenenbroeck·Henk Vandecasteele·Luc Van Meervelt

View original source ↗Export metadata ↓

Abstract

In the domain of crystal engineering, various schemes have been proposed for the classification of hydrogen bonding (H-bonding) patterns observed in 3D crystal structures. In this study, the aim is to complement these schemes with rules that predict H-bonding in crystals from 2D structural information only. Modern computational power and the advances in inductive logic programming (ILP) can now provide computational chemistry with the opportunity for extracting structure-specific rules from large databases that can be incorporated into expert systems. ILP technology is here applied to H-bonding in crystals to develop a self-extracting expert system utilizing data in the Cambridge Structural Database of small molecule crystal structures. A clear increase in performance was observed when the ILP system DMax was allowed to refer to the local structural environment of the possible H-bond donor/acceptor pairs. This ability distinguishes ILP from more traditional approaches that build rules on the basis of global molecular properties.

Explore related subjects

Artificial Intelligence Computer Simulation Crystallization Decision Trees Drug Design Electronic Data Processing Forecasting Hydrogen Bonding Models, Biological Molecular Conformation Molecular Structure Software Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Howard Y Ando, Luc Dehaspe, Walter Luyten, Elke Van Craenenbroeck, Henk Vandecasteele, Luc Van Meervelt. Discovering H-bonding rules in crystals with inductive logic programming.. https://doi.org/10.1021/mp060034z

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Deep learning techniques in predicting BRAF mutation status in cutaneous melanoma from histopathologic images.

AIMS: To develop and validate a deep learning framework for discriminating BRAF mutation status in cutaneous melanoma from routine H&E whole-slide images (WSIs) as a proof-of-concept complementary approach alongside molecular testing. METHODS: We built a two-stage pipeline comprising U-Net-based tumour segmentation followed by an Inception v3 classifier. In total, 272 institutional melanoma cases with confirmed BRAF status were used for model development (training and internal validation). Generalisability was assessed in an external test set of 76 cutaneous melanoma cases from the Cancer Genome Atlas (TCGA). Dermatopathologist-defined tumour-rich regions of interest were used to train and evaluate segmentation. WSIs were processed at 20×magnification using 512×512 tiles; slide-level mutation probabilities were obtained by averaging the predicted probabilities across all tumour-enriched tiles. RESULTS: Inception v3 achieved area under the receiver operating characteristic curve values of 0.973 (training), 0.954 (validation) and 0.915 (TCGA testing) and outperformed a ResNet50 baseline, showing stable external generalisation. Performance remained robust in advanced pathological T-category primary tumours (pT3-T4). Tumour probability heatmaps supported spatial interpretability by localising regions contributing most strongly to predicted mutation status. CONCLUSIONS: Deep learning applied to routine H&E WSIs can infer BRAF mutation status in cutaneous melanoma with consistent performance across institutional and external cohorts. Given the observed external sensitivity and negative predictive value, the model is not suitable for rule-out use or for deferring/omitting molecular testing. Any workflow integration is future work and would require prospective validation and calibration of probability outputs in real-world clinical series.

Artificial Intelligence↗

PubMed2026

Artificial Intelligence-Driven Multi-Omics Analysis Reveals Hydroxytyrosol Targeting of the TXNIP-NLRP3 Inflammasome Axis in Traumatic Brain Injury.

Traumatic brain injury (TBI) induces secondary neuroinflammation driven by oxidative stress, inflammasome activation, and immune remodeling, yet specific mechanism-guided pharmacological interventions remain limited. This study established an artificial intelligence (AI)-integrated network pharmacology and multi-omics framework to evaluate whether hydroxytyrosol (HT), an olive-derived natural polyphenol, may regulate TBI-related neuroinflammatory targets centered on the TXNIP/NLRP3 inflammasome axis. Starting from the SMILES structure of HT, potential targets were predicted using PharmMapper, SwissTargetPrediction, and the Similarity Ensemble Approach and were standardized to UniProt identifiers. TBI-associated genes were integrated from GeneCards, DisGeNET, OMIM, and the Therapeutic Target Database. The overlapping target set was analyzed using STRING-based protein-protein interaction (PPI) networks, MCODE, CytoHubba, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Public GEO transcriptomic datasets (GSE123831 and GSE104687) were used for cross-platform expression validation, differential expression analysis, and exploratory CIBERSORT-based immune infiltration estimation. Random forest (RF), multilayer perceptron (MLP), graph convolutional network (GCN), graph attention network (GAT), SHAP/LIME explainability analysis, LASSO inflammatory-risk scoring, and two-sample Mendelian randomization (MR) were further applied for target prioritization, immune phenotype mapping, and genetic association analysis. Seventy-three overlapping HT-TBI targets were identified. PPI and topology analyses prioritized TXNIP, NLRP3, CASP1, MAPK1, and TP53 as key hubs enriched in inflammasome activation, oxidative stress, apoptosis, and NOD-like receptor signaling. TXNIP, NLRP3, and CASP1 were consistently upregulated in both TBI transcriptomic datasets. LM22-based immune deconvolution suggested increased pro-inflammatory immune signatures and a positive TXNIP-M1 macrophage association (r = 0.63, p < 0.001), which should be interpreted as a transcriptome-derived hypothesis rather than validated murine immune-cell proportions. AI-based models consistently ranked TXNIP/NLRP3 as high-contribution features under internal validation, and removal of these targets reduced model performance. A five-gene inflammatory score achieved an internally evaluated AUC of 0.87, while two-sample MR supported positive genetic associations involving TXNIP expression, TBI risk, NLRP3 and IL-1β expression. Collectively, these findings prioritize the TXNIP/NLRP3/CASP1 module as a computationally supported candidate mechanism through which HT may influence oxidative stress-inflammasome-immune coupling in TBI. This study provides an interpretable drug-target-pathway-phenotype framework and identifies TXNIP, NLRP3, and CASP1 as priority nodes for future experimental validation.

Artificial Intelligence↗

PubMed2026

How Following Medical Artificial Intelligence Advice Can Mitigate Malpractice Liability: Cross-National Insights from a Randomized Trial.

Artificial intelligence (AI) increasingly influences clinical decision-making, yet its recommendations may diverge from standard care. Although malpractice concerns are thought to discourage physicians from following AI advice, experimental evidence from the United States suggests the opposite: lay jurors are more likely to hold physicians liable when they reject AI recommendations. Whether this pattern extends to systems in which court-appointed experts, not lay jurors, determine liability remains unknown. Methods: To examine how physicians and laypeople in expert-based and lay-juror legal systems evaluate physicians' acceptance or rejection of AI recommendations, particularly when those recommendations deviate from standard care, we designed a randomized vignette study: a 2 × 2 factorial design varying the AI recommendation (standard vs. nonstandard care) and a fictional physician's decision (accept vs. reject). The study was conducted online in 2023 among nationally representative samples of U.S. and German adults and from 2023 to 2024 among German physicians. In total, 387 German physicians, 2291 U.S. adults, and 2283 German adults participated; those not completing the survey or failing attention checks were excluded per preregistered criteria. Participants were randomly assigned to 1 of 4 vignettes, varying the AI recommendation (standard vs. nonstandard care) and physician's decision (accept vs. reject). The reasonableness of the fictional physician's decision was measured, rated by participants on a Likert scale. Results: Analysis, following preregistered exclusion criteria, included 248 German physicians, 1202 U.S. adults, and 1358 German adults. Physicians accepting standard-care AI recommendations were rated more reasonable than those rejecting them (U.S. laypeople: t = 5.36; 95% CI, 0.45-0.97; P < 0.001; German physicians: t = 2.47; 95% CI, 0.14-1.30; P = 0.02; German laypeople: t = 4.14; 95% CI, 0.27-0.76; P < 0.001). Ratings of physicians accepting versus rejecting AI nonstandard-care recommendations were statistically equivalent. Equivalence was tested at an α-value of 0.05 using a two 1-sided tests procedure, reported with 90% CIs per standard convention (U.S. laypeople: t = -4.90; 90% CI, -0.1 to 0.36; P < 0.001; German physicians: t = -1.76; 90% CI, -0.12 to 0.67; P = 0.04; German laypeople: t = 5.35; 90% CI, -0.35 to 0.06; P < 0.001). Conclusion: Across the United States and Germany, samples representative of lay jurors and court-appointed experts viewed accepting standard-care AI advice as more reasonable, whereas accepting or rejecting nonstandard-care AI advice was judged similarly. Contrary to predictions, malpractice liability regimes do not necessarily pose a barrier to AI use in precision medicine.

Artificial Intelligence↗