Expert Opinion on Drug Discovery最新文献_第3页

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

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

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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13555694/pdf/","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":"OA","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

A network-driven antiviral discovery pipeline for emerging RNA viruses: a critical perspective from the Red Mexicana de Desarrollo de Antivirales (RMDA). 新出现的RNA病毒的网络驱动的抗病毒发现管道：来自红色墨西哥抗病毒病毒研究（RMDA）的关键观点。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-09-01 Epub Date: 2026-08-14 DOI: 10.1080/17460441.2026.2716137

Luis Adrián De Jesús-González, Alan Orlando Santos-Mena, Carlos Noé Farfán-Morales, Moisés León-Juárez, José Manuel Reyes-Ruiz, Juan Fidel Osuna-Ramos, Carlos Daniel Cordero-Rivera, Selvin Noé Palacios-Rápalo, Flor Itzel Lira-Hernández, Rosa María Del Ángel

{"title":"A network-driven antiviral discovery pipeline for emerging RNA viruses: a critical perspective from the Red Mexicana de Desarrollo de Antivirales (RMDA).","authors":"Luis Adrián De Jesús-González, Alan Orlando Santos-Mena, Carlos Noé Farfán-Morales, Moisés León-Juárez, José Manuel Reyes-Ruiz, Juan Fidel Osuna-Ramos, Carlos Daniel Cordero-Rivera, Selvin Noé Palacios-Rápalo, Flor Itzel Lira-Hernández, Rosa María Del Ángel","doi":"10.1080/17460441.2026.2716137","DOIUrl":"10.1080/17460441.2026.2716137","url":null,"abstract":"Introduction: RNA viruses impose a disproportionate burden of emerging and reemerging infectious diseases in Latin America, yet antiviral discovery in the region remains fragmented, target-limited, and frequently disconnected from translational pathways. This perspective discusses the Red Mexicana de Desarrollo de Antivirales (RMDA) as a network-driven model to organize antiviral discovery against clinically relevant RNA viruses.Areas covered: The authors describe the conceptual architecture of RMDA, a multi-institutional Mexican initiative that integrates computational prioritization, cell-based antiviral evaluation under BSL-2/BSL-3 conditions, mechanistic virology, assessment of pharmacological synergy, in vivo validation, and clinical linkage through healthcare systems such as the Instituto Mexicano del Seguro Social. The framework is supported by prior work from participating groups on host-directed and virus-directed candidates, including metabolic modulators, lipid-lowering agents, nuclear transport inhibitors, natural products, polyphenols, melatonin, and rational drug combinations evaluated against dengue virus, Zika virus, SARS-CoV-2, respiratory syncytial virus, and related RNA viruses.Expert opinion: RMDA provides a scalable and regionally adaptable model for antiviral drug discovery in low- and middle-income settings. By aligning distributed expertise, standardized workflows, and translational decision points, this framework may accelerate candidate prioritization, reduce duplication, and strengthen preparedness against RNA virus threats in Mexico and Latin America.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"931-951"},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148758268","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

An overview of the current animal models of Marburg virus disease for countermeasure development. 马尔堡病毒病动物模型现状综述及对策研究

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-09-01 Epub Date: 2026-08-03 DOI: 10.1080/17460441.2026.2711720

Takeshi Saito, Junki Maruyama, Ayato Takada

{"title":"An overview of the current animal models of Marburg virus disease for countermeasure development.","authors":"Takeshi Saito, Junki Maruyama, Ayato Takada","doi":"10.1080/17460441.2026.2711720","DOIUrl":"10.1080/17460441.2026.2711720","url":null,"abstract":"Introduction: Marburg virus disease (MVD) occurs in sporadic and unpredictable outbreaks, making conventional human efficacy studies of vaccines and therapeutics impractical. This review summarizes animal models of MVD and their utility for pathogenesis research and evaluation of vaccines and therapeutics.Areas covered: Herein, the authors review rodent, ferret, and nonhuman primate models of MVD infection, focusing on how each reflects human disease and supports different stages of countermeasure development. Furthermore, the authors discuss their virological and pathological features and major strengths and limitations, with emphasis on the influences of the host background, viral adaptation, inoculation route, and study design on translational relevance. A literature search was conducted using PubMed, ScienceDirect, Web of Science, and Google Scholar.Expert opinion: Since no single model fully captures human MVD, model choice should be guided by specific scientific or translational questions rather than human relevance alone. Stepwise evaluation is required, with mice supporting mechanistic and early screening, other rodents and ferrets enabling candidate refinement, and nonhuman primates providing definitive efficacy evaluation. Although some models with host-adapted viruses impose limitations, they remain useful when their biological consequences are defined. Animal models are indispensable for MVD drug development, and further harmonization of strains, endpoints, and reporting standards will improve their predictive value.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"1005-1018"},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148668905","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

Radiolysis in drug discovery: molecular modification, repurposing, and mechanistic insights. 放射性溶解在药物发现中的应用：分子修饰、再利用和机理。

IF 7.3 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-08-01 Epub Date: 2026-07-08 DOI: 10.1080/17460441.2026.2697792

Ha-Yeon Song

{"title":"Radiolysis in drug discovery: molecular modification, repurposing, and mechanistic insights.","authors":"Ha-Yeon Song","doi":"10.1080/17460441.2026.2697792","DOIUrl":"10.1080/17460441.2026.2697792","url":null,"abstract":"Introduction: In drug discovery, exploring novel chemical space and elucidating precise molecular mechanisms are essential for successful lead identification. Radiation chemistry offers a highly versatile approach that spans both structural modification and mechanistic analysis.Areas covered: This review examines gamma radiolysis as a robust engine for diversity-oriented molecular modification and drug repurposing, facilitating the structural diversification of natural scaffolds and the optimization of existing therapeutics. Conversely, pulse radiolysis is discussed as a high-precision analytical technique capturing the real-time kinetics of transient radical intermediates to establish quantitative structure-activity relationships (QSAR). The literature search encompasses radiolytic transformations of natural products and mechanistic studies employing pulse radiolysis. The literature search (up to June 2026) was conducted via PubMed and Google Scholar using keywords such as 'radiolysis' and 'structural modification.Expert opinion: Radiolytic technologies provide crucial mechanistic insights to support lead optimization and rapidly generate novel scaffolds. Future integration of these tools with high-throughput screening and predictive computational modeling (e.g. DFT and machine learning) will overcome traditional purification bottlenecks, transforming radiation chemistry into an automated, 'on-demand' discovery workflow that significantly accelerates the drug discovery pipeline.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"901-915"},"PeriodicalIF":7.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148410888","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