Expert Opinion on Drug Discovery药物发现专家意见
Expert Opinion on Drug Discovery(英文缩写 EXPERT OPIN DRUG DIS),ISSN 1746-0441,eISSN 1746-045X,中文译名:药物发现专家意见 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 7.050 | Q1 |
| 2022 | 6.300 | Q1 |
| 2023 | 6.000 | Q1 |
| 2024 | 4.900 | Q1 |
| 2025 | 7.300 | Q1 |
Expert Opinion on Drug Discovery 最新收录文献
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1. Organoids in drug discovery and development: emerging technologies to transition beyond animal models.
1. 药物发现与开发中的类器官：超越动物模型的新兴技术PMID:日期:2026-10-05High translational failure rates and ethical concerns surrounding animal models are driving the development of technologies to improve the drug development process. Organoids, three-dimensional self-organizing structures derived from stem cells, provide scalable, human-relevant platforms that recapitulate aspects of native tissue architecture and physiology with the potential to enhance predictive assessment of drug efficacy and toxicity. This review evaluates the potential for organoids to complement and, where scientifically feasible, reduce or eliminate animal testing. It highlights advancements and enabling technologies, including bioprinting, organ-on-chip, bioreactors, and machine learning, that support increasing organoid complexity and physiological relevance. Furthermore, it explores evolving regulatory frameworks and industry adoption while discussing practical challenges and ethical complexities to inform future development. The literature was surveyed up to August 2026 through PubMed and ClinicalTrials.gov, with industry reports retrieved through Google search. The future of drug discovery lies in a strategic transition toward human-relevant systems. Organoid technologies provide value across stages of drug development, from high-throughput screening and mechanistic studies to predicting patient-specific treatment response, while reducing reliance on animal testing and supporting increased patient safety and efficiency. However, challenges in standardization, morphological complexity, heterogeneity, and cellular maturation currently limit their broad application.
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2. Using molecular modeling and QSAR-based design strategies for advancing the development of therapeutic solute carrier 6 (SLC6) transporter ligands.
PMID:日期:2026-10-04The SLC6 transporter family serves as an important source of therapeutic targets, particularly in CNS disorders. Although advances in structural biology have rapidly expanded our knowledge of these transporters, rational drug design remains challenging due to their dynamic nature and a highly conserved orthosteric binding pocket. Consequently, in silico methods have become irreplaceable tools in the design of SLC6 inhibitors. This review summarizes computational methods applied to the discovery of SLC6 transporter inhibitors between January 2020 and August 2026. Particular attention is paid to approaches that have enabled the identification of novel chemotypes or helped address key challenges in the rational design of SLC6-targeting drugs, including ligand selectivity, transporter conformational dynamics, or allosteric modulation. The authors identified literature using the search tools PubMed, Embase, and Web of Science. Future SLC6 drug discovery will likely rely on integrated computational workflows combining LBDD, SBDD, and AI to enable the design of selective and conformation-specific inhibitors. These advances may accelerate early-stage drug discovery by improving hit identification, hopefully facilitating the development of safer and more effective SLC6 inhibitors.
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3. Design considerations for in silico drug discovery platform that can accelerate targeted drug repurposing: a focus on rare, neglected, and emerging diseases.
PMID:日期:2026-09-25Over the past decades, considerable debate has arisen regarding the decline in productivity within the pharmaceutical industry. In this context, drug repurposing has become an increasingly important strategy. Because of its cost- and time-efficient nature, it offers a particularly attractive avenue for identifying therapeutic solutions for rare, neglected, and emerging diseases. However, the implementation of rational drug repurposing strategies in these fields is hindered by data scarcity. The author focuses on the challenge of data availability in the context of rare, emerging, and neglected diseases. Strategies that can be implemented to mitigate these limitations are discussed: open data initiatives, federated learning, data augmentation, data synthesis, and data-centric strategies for AI-guided drug repurposing. Scopus was searched for relevant literature published in the last 10 years. Drug repurposing increasingly relies on knowledge-based strategies to identify new therapeutic opportunities. The implementation of these approaches depends on the availability of high-quality data. The consolidation of data augmentation and data synthesis technologies as well as open-knowledge and federated learning paradigms could help mitigate some of the data limitations that currently constrain computational drug repurposing. Their impact remains to be established through real-world implementation.
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4. The rapid emergence of small molecule RNA modulator hit discovery: unlocking the potential.
PMID:日期:2026-09-22RNA is now recognized as an active regulatory target rather than a passive intermediary, expanding therapeutic opportunities beyond protein-centric drug discovery. Small-molecule RNA modulators offer access to novel target space and new mechanisms to modulate protein targets that are difficult to drug directly, but clinical translation remains limited. This review examines the principal challenges and opportunities in small-molecule RNA-targeted hit discovery: target prioritization based on genetics, structure, and function; mechanisms of modulation beyond splicing, including translation interference and targeted RNA degradation; the persistent gap between RNA structural engagement and functional outcome; hit-finding strategies; and hit-to-lead optimization. Here, the authors discuss how limitations do not lie solely in identifying RNA binders, but in predicting prospectively whether binding to a defined RNA structure will produce a functional consequence. Progress requires closing the structure-function-druggability loop through in-cell validation and RNA-specific druggability models, deploying targeted RNA degradation to convert silent binders into functional molecules, establishing standardized validation cascades and translational PK/PD frameworks, building RNA-native chemical infrastructure, and training AI on RNA-specific datasets. Pre-competitive infrastructure to generate shared structural and functional data will accelerate the field.
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5. Epidermal growth factor receptor (EGFR)-directed PROTACs: mechanistic validation, mutant selectivity, and translational challenges.
5. 表皮生长因子受体（EGFR）导向的PROTACs：机制验证、突变选择性和翻译挑战PMID:日期:2026-09-20该文献暂无摘要。
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6. Incentivizing challenging drug discovery: from reducing failure to quantifying uncertainty.
PMID:日期:2026-09-17Drug discovery's central difficulty is less the elimination of failure than the measurement of uncertainty. This editorial argues that artificial intelligence (AI) helps mainly by converting unquantifiable uncertainty into measurable, priceable confidence, as one evidence stream among several. It discusses the risk-uncertainty distinction, AI as a new layer in the computational drug-discovery stack, the cumulative nature of confidence (illustrated by KRAS, PCSK9 and the AI-discovered TNIK inhibitor rentosertib), discordant evidence, AI's limitations, and incentive design. Literature was identified from PubMed/MEDLINE, Scopus and Google Scholar to August 2026, with primary sources and trial registries; as an invited editorial, the search was narrative, not systematic. AI's deepest contribution is epistemic rather than algorithmic: unlikely to lower failure rates markedly, but by making uncertainty measurable it can improve portfolio decisions and render ambitious biology attemptable - given calibration, prospective validation, open negative data, and incentives that reward reducing uncertainty.
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7. Tumor spheroids versus organoids: defining the more suitable three-dimensional model for cancer research.
7. 肿瘤球状体与类器官：定义更适合癌症研究的三维模型PMID:日期:2026-09-04Three-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. 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. 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.
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8. Advances in the emerging field of epi-informatics in drug discovery: roads taken and future prospects.
8. 药物发现表观信息学新兴领域的进展：所走的道路和未来前景PMID:日期:2026-09-02Epigenetic 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. 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. 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.
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9. Limitations of current drug hybridization strategies and the challenges ahead.
9. 当前药物杂交策略的局限性和未来的挑战PMID:日期:2026-09-01该文献暂无摘要。
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10. Challenges in modeling interstitial cystitis: implications for drug discovery efforts.
10. 间质性膀胱炎建模的挑战：对药物研发工作的启示PMID:日期:2026-08-31Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic condition characterized by bladder pain and lower urinary tract symptoms. Despite extensive investigation and numerous therapeutic candidates, few disease-modifying treatments have translated into clinical practice. Evidence indicates that IC/BPS comprises biologically distinct phenotypes, including inflammatory bladder-centric and systemic pain-associated phenotypes, creating challenges for experimental modeling and drug development. Using literature from a structured search of PubMed, Scopus, and Web of Science, this review examines current IC/BPS model systems from a drug discovery perspective, including preclinical, human translational, and computational approaches. The authors evaluate the biological domains captured by existing models, their strengths and limitations, and their influence on therapeutic development. Attention is given to four translational challenges: inadequate representation of disease heterogeneity, reliance on acute injury paradigms for a chronic pain condition, bladder-centric modeling of a multisystem disorder, and limited integration of computational phenotyping. Finally, the authors propose a model-informed framework linking experimental systems with therapeutic mechanisms, patient phenotypes, and clinically relevant endpoints. Progress in IC/BPS drug discovery will depend on mechanism-driven use of complementary models aligned with defined patient subgroups. Integrating patient phenotyping, biomarkers, computational methods, and back-translation from clinical trial outcomes may improve translational predictability and therapeutic success.