Expert Opinion on Drug Discovery最新文献

Tumor spheroids versus organoids: defining the more suitable three-dimensional model for cancer research. 肿瘤球体与类器官：定义更适合癌症研究的三维模型。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-09-04 DOI: 10.1080/17460441.2026.2726313

Hiral Tanna, Shrey Shah

{"title":"Tumor spheroids versus organoids: defining the more suitable three-dimensional model for cancer research.","authors":"Hiral Tanna, Shrey Shah","doi":"10.1080/17460441.2026.2726313","DOIUrl":"https://doi.org/10.1080/17460441.2026.2726313","url":null,"abstract":"Introduction: Three-dimensional cell culture systems, such as spheroids and organoids, have emerged as tools for modeling biological processes beyond conventional two-dimensional cultures. Spheroids, formed by self-aggregation of cell lines or primary cells, are simple multicellular structures ideal for drug screening due to their reproducibility and compatibility with high-throughput platforms. Organoids, typically derived from stem cells or patient tissues, develop structures that mimic tissue architecture and genetic features, including tumor heterogeneity.Areas covered: This review examines the fundamental differences between spheroid and organoid models, focusing on their cellular origin, structural features, methods of generation, and applications in tumor research. The authors discuss the advantages and limitations of each system and highlight how experimental goals, scalability requirements, and biological complexity influence model selection. This review is based on structured literature searches of PubMed/MEDLINE, Web of Science, and Google Scholar for articles published between January 1988 and July 2026.Expert opinion: Tumor spheroid models offer practical and scalable solutions to screening and mechanistic studies, while organoids offer biological complexity and better representation of patient-derived disease characteristics. These models improve the physiological relevance of in-vitro studies, help bridge the gap between simplified cell culture systems and in-vivo testing, and reduce dependence on animal model testing.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"1-17"},"PeriodicalIF":7.3,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148891464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in the emerging field of epi-informatics in drug discovery: roads taken and future prospects. 药物发现中外源性信息学新兴领域的进展：已采取的道路和未来展望。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-09-02 DOI: 10.1080/17460441.2026.2727574

Aylin Del Moral-Morales, Erik D Díaz-Dionisio, José L Medina-Franco

{"title":"Advances in the emerging field of epi-informatics in drug discovery: roads taken and future prospects.","authors":"Aylin Del Moral-Morales, Erik D Díaz-Dionisio, José L Medina-Franco","doi":"10.1080/17460441.2026.2727574","DOIUrl":"10.1080/17460441.2026.2727574","url":null,"abstract":"Introduction: Epigenetic drug discovery remains a promising drug discovery strategy that has long been driven by advances in computational approaches. The subfield of epi-informatics, established more than a decade ago, continues to evolve rapidly as emerging machine learning methodologies reshape and expand its applications.Areas covered: The authors provide an updated overview of bioinformatics, chemoinformatics, and machine learning methodologies used to identify, design, and optimize compounds, primarily small-molecules, that modulate epigenetic processes with therapeutic potential. The discussion is based on a comprehensive literature analysis of peer-reviewed literature, encompassing 7,185 unique research articles published between 2000 up to 2025. The article also examines the epigenetic drug discovery landscape by analyzing the most extensively investigated epigenetic targets and emerging research trends.Expert opinion: Epi-informatics has evolved into a distinct interdisciplinary field integrating bioinformatics, chemoinformatics, and artificial intelligence to advance epigenetic drug discovery. Although rapid progress in multi-omics integration, molecular modeling, and generative artificial intelligence is accelerating the identification of drug candidates, future success will depend on high-quality, standardized data, interpretable machine learning models, and rigorous experimental validation that ensure reproducibility. Addressing these challenges will further advance epi-informatics in oncology research and an expanding range of complex diseases.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"1-15"},"PeriodicalIF":7.3,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148864447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Limitations of current drug hybridization strategies and the challenges ahead. 当前药物杂交策略的局限性和未来的挑战。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-09-01 DOI: 10.1080/17460441.2026.2726308

Joana Gil, Tânia S Morais

引用次数: 0

Trustworthy and explainable AI for drug discovery. 可信赖且可解释的药物发现人工智能。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-09-01 Epub Date: 2026-06-24 DOI: 10.1080/17460441.2026.2691832

Stephan Steigele

引用次数: 0

Foundation models for low-data drug discovery: mining heterogeneous public and proprietary data in neglected indications. 低数据药物发现的基础模型：在被忽视的适应症中挖掘异构公共和专有数据。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-09-01 Epub Date: 2026-08-07 DOI: 10.1080/17460441.2026.2712551

Michail Papadourakis, Evangelia Efraimia Amaxopoulou, Minos-Timotheos Matsoukas

{"title":"Foundation models for low-data drug discovery: mining heterogeneous public and proprietary data in neglected indications.","authors":"Michail Papadourakis, Evangelia Efraimia Amaxopoulou, Minos-Timotheos Matsoukas","doi":"10.1080/17460441.2026.2712551","DOIUrl":"10.1080/17460441.2026.2712551","url":null,"abstract":"Introduction: This review examines how Foundation Models can address critical limitations of data scarcity in drug discovery, particularly for neglected diseases where traditional approaches are ineffective. It highlights the need for new methodologies that integrate heterogeneous data sources to enable more equitable and efficient therapeutic development.Areas covered: This review synthesizes recent advances in Foundation Models and related machine learning approaches for low-data drug discovery, with a focus on applications in neglected diseases. The authors searched PubMed, Scopus, and Web of Science for available literature using terms related to foundation models, machine learning, deep learning, AI with neglected diseases, drug discovery, low-data settings, transfer learning, and related methodological and disease-specific terms. Reference lists of included reviews were additionally screened for relevant primary literature.Expert opinion: Foundation Models are poised to play a central role in drug discovery. Nevertheless, their effectiveness for low-data and neglected diseases will depend on strong collaboration, responsible and ethical use, and continued technical innovation.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"983-1004"},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148684009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mössbauer spectroscopy in drug discovery: revealing Fe- and Fe-S cluster dependent targets. Mössbauer光谱在药物发现：揭示铁和铁s簇依赖的目标。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-09-01 Epub Date: 2026-08-05 DOI: 10.1080/17460441.2026.2712554

Jiahua Chen, Trent Quist, Ronabelle Fang, Henry Thanh Nguyen, Maria-Eirini Pandelia

{"title":"Mössbauer spectroscopy in drug discovery: revealing Fe- and Fe-S cluster dependent targets.","authors":"Jiahua Chen, Trent Quist, Ronabelle Fang, Henry Thanh Nguyen, Maria-Eirini Pandelia","doi":"10.1080/17460441.2026.2712554","DOIUrl":"10.1080/17460441.2026.2712554","url":null,"abstract":"Introduction: Iron- and iron-sulfur cluster (Fe-S)-containing proteins are essential for diverse biological processes, including electron transfer, genome maintenance, metabolism, cellular signaling, and host-pathogen interactions. Despite their broad biological importance and growing links to human disease, Fe-S cluster-dependent proteins remain underexplored as therapeutic targets, largely because it is difficult to define their metal-dependent chemistry using conventional biochemical, spectroscopic, and structural approaches.Areas covered: This review examines how Mössbauer spectroscopy can be integrated into workflows for metalloprotein characterization, target validation, and drug discovery. Using representative Fe-S cluster-containing proteins, the practical considerations for implementing Mössbauer spectroscopy are outlined, including 57Fe-enriched expression, sample preparation, and spectroscopic analysis. Two case studies of experimentally challenging viral Fe-S cluster proteins are then highlighted, the Hepatitis B virus X protein and the Porcine Reproductive and Respiratory Syndrome Virus Nsp1α protease, which demonstrate how direct characterization of metal cofactors can reveal previously unrecognized therapeutic avenues. Relevant literature published through March 2026 was identified using PubMed and Google Scholar with keywords related to Mössbauer spectroscopy, iron-sulfur proteins, viral metalloproteins, and drug discovery.Expert opinion: As drug discovery increasingly seeks to exploit metal-dependent biology, Mössbauer spectroscopy will play an important role in identifying cryptic metalloproteins, defining their native states, and uncovering Fe- and Fe-S cluster-dependent targets. Mössbauer spectroscopy can also be complementary, and integrated with structural and AI-driven approaches to answer emerging challenges in medicinal chemistry.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"953-963"},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148677744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing polypharmacology-based drug discovery using deep generative models. 使用深度生成模型推进基于多药理学的药物发现。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-09-01 Epub Date: 2026-07-21 DOI: 10.1080/17460441.2026.2707118

Jürgen Bajorath

引用次数: 0

Should C. elegans play a larger role in drug discovery for neurodegenerative diseases? 秀丽隐杆线虫是否应该在神经退行性疾病的药物发现中发挥更大的作用？

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-09-01 Epub Date: 2026-08-05 DOI: 10.1080/17460441.2026.2712548

Andrea Hinas, Christian G Riedel

引用次数: 0

Fatty acid amide hydrolase (FAAH) inhibitor design and preclinical studies: lessons learned from BIA 10-2474 and the challenges ahead. 脂肪酸酰胺水解酶（FAAH）抑制剂设计和临床前研究：BIA 10-2474的经验教训和未来的挑战

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-09-01 Epub Date: 2026-08-19 DOI: 10.1080/17460441.2026.2716127

Alessia Catalano, Filomena Fezza, Andrea Duranti, Maria Maddalena Cavalluzzi, Giovanni Lentini, Giuseppe Felice Mangiatordi, Marco Bruno Luigi Rocchi, Mauro Maccarrone

{"title":"Fatty acid amide hydrolase (FAAH) inhibitor design and preclinical studies: lessons learned from BIA 10-2474 and the challenges ahead.","authors":"Alessia Catalano, Filomena Fezza, Andrea Duranti, Maria Maddalena Cavalluzzi, Giovanni Lentini, Giuseppe Felice Mangiatordi, Marco Bruno Luigi Rocchi, Mauro Maccarrone","doi":"10.1080/17460441.2026.2716127","DOIUrl":"10.1080/17460441.2026.2716127","url":null,"abstract":"Introduction: BIA 10-2474 is a fatty acid amide hydrolase (FAAH) inhibitor that was developed as a treatment for anxiety and pain, due to its ability to increase the endocannabinoid tone. It was released for clinical trials and subsequently withdrawn due to its neurological adverse reactions Several hypotheses were suggested to justify the toxicity of the drug, but the exact responsible mechanism has not been fully established.Areas covered: This review paper summarises the concerns arising from the development of BIA 10-2474. The authors highlight the lessons learnt from the development of BIA 10-2474 and discuss how advanced methodologies can assist scientists to face the challenges ahead. This article is primarily based on literature derived from the Reaxys® (queries: 'FAAH inhibitors,' 'fatty acid amidase inhibitors,' and 'fatty acid amide hydrolase inhibitors') focusing on the past decade. Cross references were retrieved from the early papers obtained.Expert opinion: Rodent and human FAAH have different 3D structures and interact differently with exogenous molecules. Thus, any candidate drug should be tested also on human FAAH and/or on human specimens. Safety measures to prevent untoward outcomes in first-in-human phase I clinical trials have been suggested, and further studies are urgently needed to address these dangerous circumstances.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"1019-1036"},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148790338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can Artificial intelligence meaningfully shorten drug discovery timelines? Current evidence, roadblocks and future directions. 人工智能能有效缩短药物研发时间吗？目前的证据，障碍和未来的方向。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-09-01 Epub Date: 2026-07-30 DOI: 10.1080/17460441.2026.2711722

Juan Luis Fernández-Martínez

{"title":"Can Artificial intelligence meaningfully shorten drug discovery timelines? Current evidence, roadblocks and future directions.","authors":"Juan Luis Fernández-Martínez","doi":"10.1080/17460441.2026.2711722","DOIUrl":"10.1080/17460441.2026.2711722","url":null,"abstract":"Introduction: Artificial intelligence (AI) is increasingly proposed as a means of shortening drug-discovery timelines, although its practical impact varies across the discovery and development process. A critical review is needed to distinguish the stages in which AI can improve search, prioritization, and decision-making from those that remain limited by experimental validation, safety assessment, and clinical evidence generation.Areas covered: This review discusses the use of AI in target and pathway prioritization, variant and protein-structure interpretation, drug repurposing, virtual screening, molecular design, ADMET prediction, biomarker discovery, and patient stratification. The review was informed by iterative searches of PubMed and Google Scholar, supplemented by ResearchGate, covering literature from database inception to 15 July 2026, together with reference lists, clinical-trial registries, regulatory disclosures, company reports, and publicly available pipeline updates.Expert opinion: AI is most likely to shorten drug discovery when it improves the quality and sequence of decisions, reduces the number of unnecessary experiments, and identifies failure earlier. Its greatest value will arise when it is integrated with high-quality data, disease-relevant experimental models, expert supervision, and prospective clinical validation.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"965-981"},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148619105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0