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

The impact of antisense oligonucleotide (ASO) therapeutics on the future of rare disease drug discovery. 反义寡核苷酸（ASO）疗法对未来罕见病药物发现的影响。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-04-01 Epub Date: 2026-03-30 DOI: 10.1080/17460441.2026.2652440

Ruchi Ruchi, Kriti Gupta, Rajkumar Verma, José Manautou, Xiao-Bo Zhong, Raman Bahal

引用次数: 0

Why is it so difficult to discover drugs for amyotrophic lateral sclerosis? A protein intrinsic disorder perspective. 为什么发现治疗肌萎缩性侧索硬化症的药物如此困难？蛋白质内在失调的观点。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-03-24 DOI: 10.1080/17460441.2026.2648612

Vladimir N Uversky

{"title":"Why is it so difficult to discover drugs for amyotrophic lateral sclerosis? A protein intrinsic disorder perspective.","authors":"Vladimir N Uversky","doi":"10.1080/17460441.2026.2648612","DOIUrl":"https://doi.org/10.1080/17460441.2026.2648612","url":null,"abstract":"Introduction: Amyotrophic Lateral Sclerosis (ALS) is the most common adult motor neuron disease, now viewed as a spectrum disorder rather than a single entity. Because of the significant person-to-person variability in the disease's biology, driven by both genetic and environmental interactions, finding a single \"magic bullet\" drug is unlikely. Despite decades of research, only a few ALS drugs have being developed. Drug discovery has a 95% failure rate due to genetic complexity, lack of sensitive biomarkers, diagnostic delays, inadequate animal models, and poor clinical trial design.Areas covered: This article considers several aspects related to the prevalence of intrinsic disorder in ALS-related proteins and highlights how these features might hinder rational structure-based drug discovery.Expert opinion: There is a common oversight in current drug discovery methodologies, which is the neglect of intrinsically disordered proteins (IDPs) playing several crucial roles in the pathology of neurodegeneration in general and ALS in particular. Therefore, it seems that the 'one-size-fits-all' approach to ALS is hitting a wall because these 'shapeshifters' of the cellular world are ignored. Consequently, to be more successful in finding drugs treating ALS, gears should be shifted from rational structure-based models to intrinsic disorder-centric approaches.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"1-21"},"PeriodicalIF":4.9,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147510939","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

Integrated in silico and in vitro screening approaches for the discovery of small-molecule inhibitors of β-carbonic anhydrases. 集成在硅和体外筛选方法发现β-碳酸酐酶的小分子抑制剂。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-03-01 Epub Date: 2026-02-23 DOI: 10.1080/17460441.2026.2636772

Ratul Bhowmik, Ilaria D'Agostino, Andrea Angeli, Ashok Aspatwar

{"title":"Integrated in silico and in vitro screening approaches for the discovery of small-molecule inhibitors of β-carbonic anhydrases.","authors":"Ratul Bhowmik, Ilaria D'Agostino, Andrea Angeli, Ashok Aspatwar","doi":"10.1080/17460441.2026.2636772","DOIUrl":"10.1080/17460441.2026.2636772","url":null,"abstract":"Introduction: β-Carbonic anhydrases (β-CAs) are zinc-dependent metalloenzymes that catalyze the reversible hydration of carbon dioxide to bicarbonate and protons. They are widely distributed in bacteria, where they support pH regulation, inorganic carbon homeostasis, and central metabolism. Unlike humans, which express only α-class carbonic anhydrases, many bacterial pathogens encode β-CAs, highlighting these enzymes as attractive antibacterial targets with reduced risk of host cross-reactivity.Areas covered: This review discusses integrated in silico and in vitro strategies for the discovery and validation of small-molecule inhibitors targeting bacterial β-CAs. Computational approaches - including pharmacophore modeling, molecular docking, molecular dynamics simulations, and machine learning - are increasingly used to prioritize and optimize candidate inhibitors. Experimental validation employs enzymatic activity assays, biophysical binding techniques, and whole-cell assays to assess target engagement and antibacterial effects. Current inhibitor classes include sulfonamides, coumarins, dithiocarbamates, phenolic compounds, and natural products, with selected chemotypes demonstrating antibacterial or antivirulence activity in specific models. Relevant literature was identified through searches of PubMed, Web of Science, and Scopus, focusing on studies published between approximately 2000 and 2025.Expert opinion: β-CAs represent a tractable yet underexploited antibacterial target class. Successful translation will depend on improving bacterial penetration, pharmacokinetics, and target engagement. When strategically positioned as adjunctive or context-dependent therapies, β-CA inhibitors may contribute to the treatment of drug-resistant bacterial infections, including tuberculosis.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"319-337"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146257940","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

Deep generative molecular design and its value in modern drug discovery. 深层生成分子设计及其在现代药物发现中的价值。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-03-01 Epub Date: 2026-03-04 DOI: 10.1080/17460441.2026.2636192

E Sila Ozdemir, Hyunbum Jang, Ozlem Keskin, Attila Gursoy, Ruth Nussinov

{"title":"Deep generative molecular design and its value in modern drug discovery.","authors":"E Sila Ozdemir, Hyunbum Jang, Ozlem Keskin, Attila Gursoy, Ruth Nussinov","doi":"10.1080/17460441.2026.2636192","DOIUrl":"10.1080/17460441.2026.2636192","url":null,"abstract":"Introduction: Deep generative models are reshaping de novo drug design by enabling creation of novel, property-optimized molecules beyond traditional chemical libraries. Advances in deep learning, molecular representation learning, and structure-aware modeling now enable algorithms to propose molecules that satisfy complex pharmacological constraints, accelerating hit identification.Areas covered: This review outlines recent advances in generative molecular design, including neural network-based frameworks, reinforcement learning systems, diffusion models, and language model-based transformers. The authors outline how each class generates and optimizes molecular structures and review generative AI's practical applications in drug discovery, illustrating translational progress. Current bottlenecks are critically analyzed alongside emerging solutions. This review is based on a systematic literature search conducted in Google Scholar and PubMed, covering studies published up to December 2025.Expert opinion: Generative AI's greatest promise lies not in generating more molecules, but in generating better hypotheses, structures that are synthetically accessible, biologically plausible, optimized across potency, selectivity, and pharmacokinetics. The next phase will be led by multimodal foundation models capable of reasoning jointly about chemistry, protein structure, and cellular response, supported by automated synthesis and high-throughput experimentation. As these components are integrated, generative molecular design will guide lead optimization and reshape how new therapies are discovered and developed.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"273-287"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354402","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 label-free screening technologies to enhance drug discovery efficiency. 推进无标签筛选技术，提高药物发现效率。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1080/17460441.2026.2622372

Ronghai Cheng, Chang Liu

{"title":"Advancing label-free screening technologies to enhance drug discovery efficiency.","authors":"Ronghai Cheng, Chang Liu","doi":"10.1080/17460441.2026.2622372","DOIUrl":"10.1080/17460441.2026.2622372","url":null,"abstract":"Introduction: Hit identification is a pivotal yet resource-intensive stage of early drug discovery, where large chemical libraries are screened to uncover compounds with target-specific activity. Traditional fluorescence- and luminescence-based high-throughput assays, while fast and automation-friendly, suffer from label-associated artifacts, limited biological relevance, and signal interference that can compromise data fidelity. These challenges, coupled with the growing scale of screening campaigns, have intensified the need for more robust and physiologically relevant label-free screening strategies.Areas covered: This review highlights the emergence of label-free detection technologies as powerful alternatives for hit identification. By enabling direct measurement of biomolecular interactions or cellular responses without secondary reporters, these modalities reduce false positives, improve assay reliability, and enhance mechanistic insight. The authors also summarize their operating principles, recent applications, and practical considerations, emphasizing how label-free approaches can strengthen screening accuracy and accelerate early drug discovery.Expert opinion: Label-free assays have rapidly advanced, offering real-time measurements, improved physiological relevance, and expanding throughput for early drug discovery. While these methods reduce artifacts and broaden target compatibility, challenges remain in validating biological relevance and managing complex kinetic data. Recent software innovations, including automated kinetic modeling and high-throughput data pipelines, are accelerating analysis and enhancing scalability.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"289-302"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146085125","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

Learning from the successes and failures of early artificial intelligence (AI) adoption for drug discovery in Big BioPharma. 从大型生物制药公司早期采用人工智能（AI）进行药物发现的成功和失败中吸取教训。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-03-01 Epub Date: 2026-01-30 DOI: 10.1080/17460441.2026.2624023

Martin Braddock, Krzysztof Jeziorski

{"title":"Learning from the successes and failures of early artificial intelligence (AI) adoption for drug discovery in Big BioPharma.","authors":"Martin Braddock, Krzysztof Jeziorski","doi":"10.1080/17460441.2026.2624023","DOIUrl":"10.1080/17460441.2026.2624023","url":null,"abstract":"Introduction: AI has tremendous potential to reduce time and costs taken to discover and develop new medical entities. As technology evolves, it is essential to learn from successes and failures to realign expectations for scientists, stakeholders and investors.Areas covered: The authors discuss the challenges associated with the traditional reductionist approach to drug discovery which relies on incomplete data for target validation and, specifically for small molecules, the expanse of chemical space providing potential candidates. The promise of AI is illustrated by both early success and failure stories. Lessons learned are provided at levels of realism, adoption and integration of AI within current Research and Development (R&D) organizational structures.Expert opinion: The first decade of AI adoption in Big BioPharma has been characterized by genuine breakthroughs and sobering realities. While AI has delivered notable accelerations in hit identification and early-stage design, it has yet to fundamentally alter the success rates of late-stage clinical trials. The industry has learned that AI is neither a silver bullet nor a passing fad, though a critical and evolving component of modern R&D. By consolidating lessons from early adoption, the next decade may see AI truly shift the innovation frontier in global pharmaceutical discovery.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"255-272"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146085087","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

Emerging strategies in drug repurposing for decreasing the risk of age-related macular degeneration. 降低老年性黄斑变性风险的药物再利用新策略。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-03-01 Epub Date: 2026-03-10 DOI: 10.1080/17460441.2026.2635492

Beryl Zhou, Nikita Mokhashi, Dimitra Skondra

{"title":"Emerging strategies in drug repurposing for decreasing the risk of age-related macular degeneration.","authors":"Beryl Zhou, Nikita Mokhashi, Dimitra Skondra","doi":"10.1080/17460441.2026.2635492","DOIUrl":"10.1080/17460441.2026.2635492","url":null,"abstract":"Introduction: Vision loss in older adults is largely driven by age-related macular degeneration (AMD), characterized by progressive central visual field damage and functional decline. While current options for wet and dry AMD are limited and expensive, drug repurposing represents a promising strategy to accelerate the discovery of effective, accessible treatment by leveraging medications with established safety profiles. Notably, anti-diabetic agents including metformin, sulfonylureas, glucagon-like peptide-1 receptor agonists (GLP-1RAs), and insulin have emerged as modulators of the retinal pigment epithelium (RPE) function, photoreceptors, and retinal vascular integrity.Areas covered: This review highlights the roles of oxidative stress, inflammation, and complement-mediated immune dysregulation in AMD pathogenesis, alongside preclinical data demonstrating metformin's protective effects via AMP-activated protein kinase (AMPK) activation. Population-based studies and meta-analyses further suggest a modest reduction in AMD risk associated with metformin use in both diabetic and non-diabetic cohorts. Additional pharmacological agents include statins, glyburide, L-DOPA, fluoxetine, dimethyl fumarate, and nutraceuticals such as curcumin, melatonin, and N-acetylcysteine.Expert opinion: Early AMD prevention through repurposed therapeutics, guided by AI-driven design and systems biology, may enable personalized care via multimodal risk stratification incorporating genetic, metabolomic, and microbiome data. Rigorous, stratified clinical trials integrating bioinformatics and precision medicine are essential to validate the most effective candidates.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"339-354"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147389475","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

Realising the full potential of biocides in the fight against antimicrobial resistance. 充分发挥杀菌剂在抗击抗菌素耐药性方面的潜力。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-03-01 Epub Date: 2026-02-23 DOI: 10.1080/17460441.2026.2633395

Yingyu Wang, Shilin Deng, Shenghui Tu, Ke Ding, Kate Alston, Kangcheng Shen, Mingrui Liao, Jordan Petkov, Andrew J McBain, Jian R Lu

引用次数: 0

Computational insights and impact of combinatorial peptidomimetics on immune escape SARS-CoV-2 variants. 组合拟肽物对免疫逃逸SARS-CoV-2变异的计算见解和影响。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-03-01 Epub Date: 2026-02-23 DOI: 10.1080/17460441.2026.2625825

Stanly Paul Matam Losery, Kapil Laddha, Tamás A Martinek, M Elizabeth Sobhia

{"title":"Computational insights and impact of combinatorial peptidomimetics on immune escape SARS-CoV-2 variants.","authors":"Stanly Paul Matam Losery, Kapil Laddha, Tamás A Martinek, M Elizabeth Sobhia","doi":"10.1080/17460441.2026.2625825","DOIUrl":"10.1080/17460441.2026.2625825","url":null,"abstract":"Background: The SARS-CoV-2 receptor-binding domain (RBD) undergoes frequent mutations, weakening current therapeutics. Despite its importance in viral entry, most inhibitors lack resilience to antigenic drift, emphasizing the need for broad-spectrum agents that maintain binding across variants while preserving host specificity.Research design and methods: The authors employed computational mutagenesis to design h-ACE2-derived peptidomimetics incorporating nonstandard amino acids (NSAAs), targeting conserved RBD residues in SARS-CoV-2, Omicron, and JN.1. Approximately 10,000 NSAAs were screened at six hotspot positions, followed by molecular dynamics simulations and density functional theory (DFT) analyses to assess binding energy, electronic compatibility, and structural stability.Results: NSAA substitutions enhanced peptidomimetic-RBD affinity through non-canonical interactions: bromothiophenyl residues engaged in halogen bonding (distance: 3.1-3.4 Å), triazole rings formed π-stacking networks, and sulfonamide linkers stabilized hydrogen bonds (-104.5 to -113.1 kcal/mol binding free energy). Multi-mutated peptides retained native-like electrostatic profiles (<±2 kcal/mol deviation) while demonstrating structural stabilization of variant RBDs (RMSD ≤1.2 Å). Hydrogen bond profiling revealed triazole-mediated interactions as critical for cross-variant recognition, with 90% persistence across simulations.Conclusion: NSAA-engineered peptidomimetics act as pan-variant RBD inhibitors, resisting antigenic drift while preserving host-binding specificity. This computational-quantum hybrid strategy offers a blueprint for resilient antiviral therapeutics against evolving pathogens.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"355-368"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147276171","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

The impact of free radicals on opioid receptors in tissue injury and inflammation: implications for drug design. 自由基对组织损伤和炎症中阿片受体的影响：对药物设计的影响。

IF 4.9 2区医学

Expert Opinion on Drug Discovery Pub Date : 2026-03-01 Epub Date: 2026-02-16 DOI: 10.1080/17460441.2026.2625824

Sophia Detzner, Christoph Stein

{"title":"The impact of free radicals on opioid receptors in tissue injury and inflammation: implications for drug design.","authors":"Sophia Detzner, Christoph Stein","doi":"10.1080/17460441.2026.2625824","DOIUrl":"10.1080/17460441.2026.2625824","url":null,"abstract":"Introduction: The opioid crisis burdens the health care system and poses major research challenges. One approach toward safer analgesics involves targeting opioid receptors in areas of tissue injury/inflammation. Increased proton concentrations have been successfully exploited for developing novel ligands with limited side effects. This review investigates the impact of other inflammatory components (free radicals) on opioid receptors, focusing on redox-sensitive thiols and disulfides.Areas covered: This narrative review is based on a systematic literature search up to 3 March 2025. Of the identified 938 articles, 29 articles met the authors' inclusion criteria. Risk of bias was assessed according to NINDS recommendations, and the review followed PRISMA guidelines. As the included studies indicate that disulfides are an essential structural component, it is conceivable that free radicals affect opioid receptor activity.Expert opinion: Current drug design has largely overlooked that function of G-protein coupled receptors (GPCRs) can differ between healthy and pathological microenvironments. The success of pH-dependent opioid ligands illustrates the therapeutic potential of exploiting such conditions. Redox changes may represent another regulatory component, but functional and in vivo evidence is still lacking. The consideration of microenvironmental factors may enable the development of safer, peripherally acting analgesics and refine GPCR-targeted drug design.","PeriodicalId":12267,"journal":{"name":"Expert Opinion on Drug Discovery","volume":" ","pages":"303-318"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146200607","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