Intelligent Pharmacy最新文献

{"title":"CARE-Cirrhosis: A multi-level explainability framework integrating predictive modeling and personalized clinical recommendation in cirrhosis care","authors":"Deepak Kumar ,&nbsp;Brijesh Bakariya ,&nbsp;Chaman Verma ,&nbsp;Zoltan Illes","doi":"10.1016/j.ipha.2025.12.003","DOIUrl":"10.1016/j.ipha.2025.12.003","url":null,"abstract":"<div><h3>Objective</h3><div>This study introduces CARE-Cirrhosis (Cirrhosis Ascites Risk Prediction and Explainability with Recommendation Engine), a unified methodological framework that systematically integrates predictive modeling, multi-level explainability, and a personalized recommendation engine into a single, deployable clinical decision-support architecture. Rather than applying interpretability tools in isolation, the framework embeds Explainable AI (XAI) methods like SHapley Additive exPlanations (SHAP), Local Interpretable Model agnostic Explanations (LIME), and counterfactual reasoning within an operational pipeline that transforms predictive outputs into transparent, actionable, patient-specific clinical guidance. Thus, it is advancing the methodological foundations of interpretable machine learning(ML) for biomedical applications.</div></div><div><h3>Methods</h3><div>Data from the Mayo Clinic Primary Biliary Cirrhosis (PBC) cohort (<em>n</em> = 418) were analyzed using Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGB), evaluated under stratified 5-fold cross-validation(CV). Multi-level interpretability was achieved through XAI methods like global attribution (SHAP), local surrogate reasoning (LIME), and counterfactual analysis (DiCE). These layers were synthesized within a unified interpretability framework, linked to a rule-based recommendation engine for generating patient-specific, physiologically plausible “what-if” scenarios. The pipeline was implemented as a mobile application to demonstrate translational applicability and real-time deployment feasibility.</div></div><div><h3>Results</h3><div>All models demonstrated strong discriminative performance (AUROC 0.90–0.92). SHAP identified albumin, platelets, prothrombin, and edema as consistent global predictors, while counterfactual reasoning delineated clinically meaningful feature thresholds (probability 0.3–0.4). The interpretability synthesis enabled cross-validation of feature attributions across explanation paradigms, improving transparency and robustness. The integrated recommendation module generated individualized monitoring strategies and actionable insights.</div></div><div><h3>Conclusion</h3><div>CARE-Cirrhosis establishes a generalizable methodological approach for unifying predictive modeling, explainability, and clinical recommendation within a single, deployable framework. By demonstrating a reproducible process for multi-level interpretability integration, it advances the methodological scope of biomedical informatics beyond model development toward transparent, interpretable, and actionable decision-support systems applicable across clinical domains.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"4 1","pages":"Pages 109-125"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147413024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetes-associated metabolic-immune signature stratifies survival and immunotherapy response in pancreatic adenocarcinoma through multi-omics integration","authors":"Le Tang ,&nbsp;Mengwei Yang ,&nbsp;Tongji Xie ,&nbsp;Guangyu Fan ,&nbsp;Haohua Zhu ,&nbsp;Jiari Yao ,&nbsp;Xiaohong Han ,&nbsp;Yuankai Shi","doi":"10.1016/j.ipha.2025.12.004","DOIUrl":"10.1016/j.ipha.2025.12.004","url":null,"abstract":"<div><h3>Objective</h3><div>Pancreatic adenocarcinoma (PAAD) poses a significant threat, especially to patients with diabetes mellitus (DM). While numerous studies have explored prognostic factors for pancreatic cancer, research specifically focusing on PAAD patients with DM remains scarce. This study aims to address this gap by identifying differentially expressed genes (DEGs) between DM and non-DM individuals and developing a reliable prognostic model to predict overall survival (OS) in PAAD patients with DM.</div></div><div><h3>Methods</h3><div>PAAD patients with DM were divided into a training group (70%) and a test group (30%). Univariate COX analysis was used to screen genes associated with OS. A 10-gene risk model (<em>ACACA</em>, <em>ATG7</em>, <em>DEFB123</em>, <em>FSTL3</em>, <em>NIPSNAP3B</em>, <em>RASSF1</em>, <em>RBPJ</em>, <em>SLC35F2</em>, <em>SLC37A1</em> and <em>ZC3H12D</em>) was constructed via LASSO-penalized COX regression combined with ten-fold cross-validation. Additionally, multi-omics findings were integrated, and in vitro functional studies were conducted to validate the oncogenic roles of key metabolic regulators (<em>ACACA</em> and <em>ATG7</em>) under high-glucose conditions.</div></div><div><h3>Results</h3><div>The constructed model demonstrated strong predictive ability, with a <em>C-index</em> of 0.83 in the training group and 0.76 in the test group, indicating good performance across both cohorts. Further analysis revealed that the high-risk group exhibited a tumor-growth and angiogenic phenotype (which may drive rapid tumor progression), while the low-risk group showed an immune-active phenotype (suggesting a favorable immunological microenvironment).</div></div><div><h3>Conclusions</h3><div>This prognostic model can predict the OS of PAAD patients with DM. Low-risk patients may be suitable candidates for immunotherapy, whereas high-risk patients may benefit from alternative strategies such as anti-angiogenic therapy.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"4 1","pages":"Pages 126-138"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147413025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network pharmacology and molecular simulation-based analysis of Citrus aurantium metabolites targeting breast cancer pathways","authors":"Taufik Muhammad Fakih ,&nbsp;Haura Syabihah ,&nbsp;Tri Awalani Sapa'ati Gusti ,&nbsp;Tifanil Oktafira ,&nbsp;Nency Oktavia Sri Mulyani ,&nbsp;Hilda Aprilia Wisnuwardhani ,&nbsp;Muchtaridi Muchtaridi","doi":"10.1016/j.ipha.2025.11.001","DOIUrl":"10.1016/j.ipha.2025.11.001","url":null,"abstract":"<div><div><em>Citrus aurantium</em> is a plant known for its diverse bioactive constituents, yet its therapeutic potential in hormone-dependent breast cancer remains underexplored. This study applied a network pharmacology approach to identify breast cancer-associated targets, followed by molecular docking, molecular dynamics simulation, and MM-PBSA energy analysis to evaluate ligand–receptor interactions. From an initial dataset of 111 metabolites, 24 compounds were predicted to interact with ESR1, a central node identified through protein–protein interaction network analysis. Several flavonoid derivatives demonstrated favorable docking interactions at key ESR1 residues, including GLU353, ALA350, LEU387, ARG394, LYS449, and PRO324. Molecular dynamics simulations up to 300 ns revealed that the native ligand complex maintained the greatest stability, 5,7,3′,4′-tetrahydroxyflavone achieved persistent receptor engagement with moderate flexibility, and hesperetin showed higher deviations yet equilibrated with selective residue interactions. MM-PBSA binding free energy calculations confirmed that 5,7,3′,4′-tetrahydroxyflavone possessed the strongest affinity (−79.695 ​kJ/mol), followed by the native ligand (−72.521 ​kJ/mol), while hesperetin displayed weaker but still favorable binding (−55.737 ​kJ/mol). These energetic contributions were largely driven by van der Waals and electrostatic forces, partially offset by polar solvation. The integrated computational findings highlight 5,7,3′,4′-tetrahydroxyflavone as the most promising ESR1 binder from <em>Citrus aurantium</em>, while hesperetin remains a viable candidate through its selective pharmacophoric interactions. Collectively, these findings establish a solid foundation for subsequent experimental validation and support the development of <em>Citrus aurantium</em>-derived compounds as potential therapies for estrogen receptor-positive breast cancer.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"4 1","pages":"Pages 67-87"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147413040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in predicting personalized nanocarrier formulations for herbal drugs: Bridging phytomedicine and precision nanotechnology","authors":"Duraisamy Sridhar,&nbsp;Ramalingam Manikandan,&nbsp;Yogananthan Dhanapal,&nbsp;Sulekha Khute,&nbsp;Paranthaman Subash","doi":"10.1016/j.ipha.2025.08.001","DOIUrl":"10.1016/j.ipha.2025.08.001","url":null,"abstract":"<div><div>Herbal drugs offer significant therapeutic benefits but face challenges such as poor bioavailability, low stability, and variable patient responses. Nanocarrier-based delivery systems can overcome these limitations. However, their development often relies on time-consuming trial-and-error methods, which lack personalization. This review explores how advanced artificial intelligence (AI) models enhance nanocarrier formulation for phytoconstituents. These models include machine learning, deep learning, transformer-based models, and graph neural networks. They optimize nanocarrier selection, drug–carrier compatibility, and release profiles while incorporating patient-specific omics data. These computational tools accurately predict key parameters, such as particle size, drug loading, and release kinetics, for phytoactives like curcumin, quercetin, and genistein. Emerging applications include optimizing herbal nanocarriers for antimicrobial efficacy in infectious disease treatment. By integrating genomics, microbiome, and metabolomics data, these models enable tailored herbal nanomedicines. This data-driven approach, combining nanotechnology with phytomedicine, accelerates formulation, improves therapeutic efficacy, and advances precision herbal therapeutics. It also shows promise for infectious disease applications. Progress in explainable AI and supportive regulatory frameworks is essential for clinical translation.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"4 1","pages":"Pages 1-11"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147413038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and application of a digital intelligent platform for clinical trial management","authors":"Pu Li,&nbsp;Hao Lei,&nbsp;Cheng Zhang,&nbsp;Xi Rao,&nbsp;Shibo Huang,&nbsp;Duanwen Cao,&nbsp;Jiangling Zhou,&nbsp;Jinhua Wen","doi":"10.1016/j.ipha.2025.09.002","DOIUrl":"10.1016/j.ipha.2025.09.002","url":null,"abstract":"<div><div>This study leverages the existing hospital-based developed Clinical Trial Management System (CTMS) information management platform to create an integrated and targeted platform for the digital and intelligent management of clinical trials in response to emerging challenges. By overcoming barriers between clinical diagnosis and treatment data and the CTMS, this platform facilitates the seamless integration of clinical trial management data (such as project initiation applications, ethical reviews, protocol execution, subject visit management, investigational drug management, financial oversight, adverse reactions, and more.) with subject diagnosis and treatment data (such as Hospital Information System (HIS), Laboratory Information System (LIS), Picture Archiving and Communication System (PACS), and others). Adopting a problem-oriented approach to quality management, we developed a subject recruitment and screening system along with a remote monitoring subsystem. This initiative strengthens the role of the digital intelligent model in subject recruitment and remote monitoring. Additionally, research has been conducted on methods and technologies for segregating inpatient subjects' medical insurance expenses from clinical trial costs. We have established an independent comprehensive database for clinical trials, achieving closed-loop information management for clinical trials. The digital intelligent platform for clinical trials has undergone rigorous internal and external validation, and its application has been widely promoted, contributing to the high-quality development of new drug research and development in China.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"4 1","pages":"Pages 12-19"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147413039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A theoretical study of 8-hydroxy quinoline with fullerenes and carbon nanotubes as nano vectors for drug delivery","authors":"Wesam R. Kadhum ,&nbsp;Amar Yasser Jassim ,&nbsp;Ehsan kianfar","doi":"10.1016/j.ipha.2025.09.003","DOIUrl":"10.1016/j.ipha.2025.09.003","url":null,"abstract":"<div><div>In recent years, the use of nanotubes as drug delivery nanocarriers has been investigated and studied. In this study, C60 fullerene and nine different nanotubes were used as carriers of the drug molecule 8-hydroxyquinoline. First, the structure of the drug molecule HQ-8 and the nanotubes was drawn using Nanotube Modeler and Gauss View software and then optimized using Gaussian09 software using DFT/B3LYP-31G∗ method. After that, the HQ-8 molecule was placed on the surface of different nanotubes from both sides of its heteroatoms, namely the pyridine nitrogen atom and the hydroxyl group, and their structures were optimized using the aforementioned method. including information related to binding energy, dipole moment, atomic charges, bond angles and lengths, fundamental properties (ionization potential, electronegativity, chemical potential, hardness and softness) and HOMO–LUMO energy gap, were calculated and evaluated. In terms of binding energy and absorption rate, the CNT (9,0) nanotube had the best interaction with the drug molecule 8-HQ from the side of the pyridine nitrogen atom. Also, in terms of dipole moment, the BNNTdopedGe nanotube showed the highest dipole moment with the 8-HQ molecule. The structure of this nanotube with the 8-HQ molecule (especially from the N atom side) showed higher polarizability and charge transfer than other structures.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"4 1","pages":"Pages 20-44"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147413020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trophoblast-derived CD81 decreased eNOS expression in endothelial cell during preeclampsia development","authors":"Li Shen,&nbsp;Yujing Li,&nbsp;Dan Liu,&nbsp;Qiang Li","doi":"10.1016/j.ipha.2025.12.001","DOIUrl":"10.1016/j.ipha.2025.12.001","url":null,"abstract":"<div><div>Trophoblast shallow invasion and maternal vascular endothelial cell damage are recognized as key pathophysiological changes associated with preeclampsia (PE). Our previous study demonstrated that CD81 inhibits trophoblast cell invasion and induces endothelial cell dysfunction. In present study, we investigated whether trophoblast-derived CD81 influences endothelial cell permeability and the expression of endothelial nitric oxide synthase (eNOS) in endothelial cell, also we explored the related molecular mechanisms. Firstly, we observed that eNOS protein level was decreased in the placenta of patients with severe preeclampsia (sPE) by western blotting (WB) analysis. Immunohistochemical results indicated that eNOS expression was down-regulated in the villous of sPE patients. Secondly, we found that primary human umbilical vein endothelial cells (HUVECs) treated with Ad-CD8-media derived from HTR-8/SV neo cells exhibited increased cell permeability compared to those treated with Ad-CTL-media. Conversely, the cell permeability of siCD81-media treated primary HUVECs was inhibited. The expressions of eNOS, Akt S473 and PDK1 in HUVECs exposed to Ad-CD81-media from trophoblasts were reduced; while treated with siCD81-media, the expressions of these moleculars were elevated in HUVECs. Furthermore, using the inhibitor of PI3K/Akt, we discovered that LY294002 could significantly reverse the effects of CD81-related media on the expressions of eNOS, Akt S473 and PDK1 in the HUVECs. Finally, we found the expressions of Akt S473 and PDK1 were reduced in placenta of sPE patients by WB analysis. In conclusion, the present study indicated that trophoblast-derived CD81 decreased eNOS expression in endothelial cell and increased the ability of endothelial cell permeability. Furthermore, we revealed that downregulation of eNOS expression by trophoblast-derived CD81 may occur partly through the PI3K/PDK1/Akt pathway.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"4 1","pages":"Pages 96-102"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147413022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DDI-LLM: Predicting unseen Drug–Drug Interactions using Large Language Models and molecular graphs","authors":"Mohammad Tanhaei","doi":"10.1016/j.ipha.2025.11.003","DOIUrl":"10.1016/j.ipha.2025.11.003","url":null,"abstract":"<div><div>This article introduces DDI-LLM, a hybrid pipeline that combines open-source Large Language Models (MedGemma) with molecular graph representations for the prediction of Drug–Drug Interactions (DDIs) of new compounds. The method integrates SMILES-based structural embeddings with literature-derived semantic embeddings, allowing the model to use molecular as well as textual information for DDI prediction. Moreover, a cross-attention interaction module is implemented to integrate these two types of embeddings efficiently. The experiments based on DrugBank v5.1.10 and PubChem BioAssay show that the model has better generalization and achieves higher (Precision, Recall, F1-score, and AUC) than DeepDDI, MolTrans, and BioBERT-DDI which are taken as baselines. Additionally, we evaluate DDI-LLM using a novel Cold-Start Precision metric, with a precision of 0.80 for novel drug pairs, this being a limiting factor in drug safety in the real world. This study advances DDI prediction methodologies and has significant clinical implications for drug safety and therapy optimization.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"4 1","pages":"Pages 88-95"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147413021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative practices and reflections on the development of hospital-led co-constructed state key laboratories","authors":"Hu Jin ,&nbsp;Bai Hua ,&nbsp;Wang Chengcheng ,&nbsp;Huang Hui ,&nbsp;Song Yimin","doi":"10.1016/j.ipha.2025.12.002","DOIUrl":"10.1016/j.ipha.2025.12.002","url":null,"abstract":"<div><div>Focusing on the development of the State Key Laboratory for Complex, Severe, and Rare Diseases since its restructuring, this paper explores its practices in fully integrating the advantages of multi-party resources, constructing a medical engineering integrated scientific research system, and innovating management mechanisms. Since the restructuring, the laboratory has focused on three core research directions (complex diseases, severe diseases, and rare diseases), and achieved remarkable results in research projects, academic papers, patent authorizations, and achievement transformation. Its recent representative achievements cover state strategic medical research fields such as pancreatic cancer mechanism research, therapeutic strategies for immunodeficiency-related sepsis, and large models for rare diseases. In the future, the laboratory will continue to benchmark against international advanced standards, strengthen open cooperation and talent echelon construction, accelerate the clinical transformation of scientific research achievements, and help enhance the hospital's original innovation capability and international influence in the field of Complex, Severe, and Rare Diseases.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"4 1","pages":"Pages 103-108"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147413023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of the latest progress of AI and machine learning methods in the free energy kinetics estimation and synthesis analysis for organic chemistry applications","authors":"Chengze Yang","doi":"10.1016/j.ipha.2025.10.001","DOIUrl":"10.1016/j.ipha.2025.10.001","url":null,"abstract":"<div><div>The accurate prediction of free energy, kinetics, and reaction outcomes in organic chemistry has long presented significant computational challenges, which are often limited by the prohibitive cost of high-precision ab initio methods for complex systems. ^1-2 Artificial intelligence and machine learning have transformed computational chemistry through a data-driven approach in property prediction, kinetics, and synthetic design, offering an alternative option for addressing this challenge. This review examines the latest advancements and the potential of artificial intelligence (AI) and machine learning (ML) to overcome these limitations. We highlight groundbreaking progress across several key domains: Dataset-based machine learning and hybrid quantum mechanical/machine learning models that achieve superior accuracy in free energy and kinetics predictions with reduced computational costs. Machine learning models that enable rapid <span><math><mrow><msub><mtext>pK</mtext><mi>a</mi></msub></mrow></math></span> predictions across a wide range of diverse solvents. The integration of thermodynamic principles into a machine learning model for accurate and consistent macro-micro <span><math><mrow><msub><mtext>pK</mtext><mi>a</mi></msub></mrow></math></span> prediction. Graph-convolutional neural networks that demonstrate high accuracy in reaction outcome prediction with interpretable mechanisms. Neural-symbolic frameworks and Monte Carlo Tree Search (MCTS) integrated with deep neural networks that revolutionize retrosynthetic planning, generating expert-quality routes at unprecedented speeds. A machine learning model based on molecular orbital reaction theory for organic reaction outcome prediction with remarkable accuracy and generalizability. Finally, hierarchical neural networks that predict comprehensive reaction conditions interdependently with exceptional speed. These advancements in a data-driven approach enhance precision, efficiency, and scalability, but challenges such as data quality, stereochemical prediction, and explicit mechanistic incorporation persist. The convergence of these AI/ML capabilities is paving the way for fully automated chemical discovery, addressing critical global challenges in medicine, materials, and energy.^3,4</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"4 1","pages":"Pages 45-66"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147413041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}