DRUG DISCOVERY TODAY药物发现今日
DRUG DISCOVERY TODAY(英文缩写 DRUG DISCOV TODAY),ISSN 1359-6446,eISSN 1878-5832,中文译名:药物发现今日 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：2025-09-27 至 2026-09-27，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 | 8.369 | Q1 |
| 2022 | 7.400 | Q1 |
| 2023 | 6.500 | Q1 |
| 2024 | 7.500 | Q1 |
| 2025 | 8.700 | Q1 |
DRUG DISCOVERY TODAY 最新收录文献
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1. {"_":"5-HT receptor as a potential therapeutic target in irritable bowel syndrome: Current insights and future perspectives.","sub":["7"]}
PMID:日期:2026-09-24Irritable bowel syndrome (IBS) affects up to 9% of the population worldwide, but current therapies benefit only 25-30% of patients. Disrupted serotonin (5-HT) signaling alters gut microbiota, permeability and immunity, driving IBS pathogenesis. The serotonin type 7 receptor (5-HTR), which is widely expressed in both central and peripheral tissues, has emerged as a promising therapeutic target. 5-HTR regulates peristalsis, modulates dendritic cell morphology and drives anti-inflammatory responses. Furthermore, 5-HTR antagonists induce analgesia and reduce mucosal innervation. This review summarizes the crucial role of 5-HTR in the gastrointestinal tract and highlights its potential for future IBS therapy.
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2. Challenges and progress in access to rare cancer drugs in China: Insights from clinical trials, availability and affordability.
PMID:日期:2026-09-23This study evaluated the development and accessibility of targeted therapies for 23 rare cancers in China. Clinical trials increased between 2013 and 2025 but have recently plateaued, with 12% terminated or suspended. Domestic enterprises now dominate trial activity, while trials of imported drugs have declined. As of 31 December 2025, 63 targeted-therapy indications had been approved globally, compared with 40 indications for 15 rare cancers in China, and eight rare cancers still lacked targeted treatments. The median monthly treatment cost was US$2165, substantially exceeding monthly disposable income per capita. Costs were significantly lower for drugs included in the National Reimbursement Drug List or those with generic alternatives. Despite progress, important gaps remain in R&D incentives, treatment availability and affordability.
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3. Drug discovery strategies targeting B7-H3 (CD276) in lung and gastric cancers: From biological validation to next-generation small molecules, biologics and cell-based immunotherapies.
PMID:日期:2026-09-23B7 homolog 3 protein (B7-H3, also known as CD276) has emerged as a promising immune checkpoint target because of its high expression in solid tumors, limited presence in normal tissues and roles in immune evasion and tumor progression. This review focuses on the biological and therapeutic relevance of B7-H3 in lung and gastric cancers, where it contributes to oncogenic signaling, epithelial-mesenchymal transition, metabolic reprogramming and therapy resistance. We highlight its structural biology, target validation and molecular mechanisms while critically assessing therapeutic strategies, including monoclonal antibodies, antibody-drug conjugates, bispecific antibodies, CAR-based therapies and emerging small-molecule inhibitors. We also discuss biomarkers, medicinal chemistry and future directions for precision cancer therapy.
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5. The future of peptide ADMET prediction: Leveraging AI to unlock new possibilities.
PMID:日期:2026-09-21Peptide-based therapeutics hold substantial promise for treating diverse diseases, yet poor stability, limited permeability, rapid clearance and context-dependent behavior continue to hinder delivery and clinical translation. Today, artificial intelligence (AI) is increasingly used to support early property evaluation and enable rapid screening and prioritization of candidate peptides. However, given these peptide-specific pharmacokinetic challenges, accurate prediction of peptide absorption, distribution, metabolism, excretion and toxicity (ADMET) remains a key bottleneck, posing considerably greater challenges than conventional small-molecule ADMET prediction and therefore warranting a focused synthesis of recent advances. This review examines AI-driven peptide ADMET prediction, synthesizing methodological advances and limitations, including peptide-specific data scarcity, limited transferability and inadequate representations of modified peptides. It further outlines emerging directions, including interrelated ADMET modeling, class-specific peptide predictors and integration of AI with physiologically based pharmacokinetic models.
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6. Bridging the translational gap in drug repurposing for rare diseases: clinical success versus regulatory conversion.
PMID:日期:2026-09-18Repurposing of drugs to treat rare diseases (RDs) is vital, but the translational gap between clinical evidence and regulatory approval remains unquantified. Analysis of 47 clinical trials showed that 66% of completed studies met their primary or key secondary endpoints. Nevertheless, only 39% achieved marketing authorization (MA) by at least one major regulatory agency (32% by the European Medicines Agency [EMA], 32% by the US Food and Drug Administration [FDA], and 24% by both agencies). A marked 'Sponsor gap' exists: academics led 64% of trials, yet industry drove 75% (EMA) and 67% (FDA) of regulatory successes. When looking at Marketing Authorization Holders (MAHs), industry sponsors held 100% of EMA authorizations in this cohort. To bridge this translational divide, we propose a framework combining AI-driven screening and N-of-1 trials to optimize academic-industrial handoffs and harmonize regulatory pathways, accelerating approval for underserved patient populations.
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7. Dark proteome and emerging technologies for functional characterization and drug discovery.
PMID:日期:2026-09-17Despite major advances in genomics and proteomics, a substantial portion of the human proteome remains poorly characterized and is commonly known as the 'dark proteome'. These understudied proteins often lack detailed structural, functional, and biochemical information, limiting our understanding of their biological roles and therapeutic potential. Recent developments in artificial intelligence (AI)-based structure prediction, cryo-electron microscopy (cryo-EM), functional genomics, and multi-omics technologies have provided new opportunities to explore this hidden region of the proteome. Characterizing the dark proteome could reveal novel therapeutic targets for cancer, neurodegenerative, metabolic, and rare genetic diseases. In this review, we summarize current knowledge of the dark proteome, highlight emerging experimental and computational approaches, discuss key challenges, and outline prospects for translating dark proteome discoveries into therapeutic applications.
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8. Pathway-driven target prioritisation in drug discovery.
PMID:日期:2026-09-16Genome-scale association studies and functional screens routinely implicate hundreds of candidate genes per disease, yet only a few will be clinically validated as drug targets. Choosing which to pursue is a central drug-discovery decision that depends on interpreting each candidate in its biological context. Curated pathway databases provide this context, while enrichment analysis applies it at scale, turning gene-level signals from genome-wide association, transcriptomic, proteomic and CRISPR studies into mechanistic hypotheses for prioritisation. This review examines how pathway-based methods inform target prioritisation, the databases and tools available for this purpose, and why pathway co-membership should be viewed as a starting point for validation rather than as evidence of causal involvement.
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9. From implicit prioritization to auditable decisions in natural product drug discovery.
PMID:日期:2026-09-14Natural-product discovery increasingly benefits from metabolomics, high-resolution mass spectrometry, molecular networking, cheminformatics and artificial intelligence; but analytical capacity does not ensure explicit, reproducible decisions. We propose a framework for prospectively formalizing how evidence is translated into auditable experimental actions. Three recurring gaps are addressed: incomplete integration of sample metadata, conflation of analytical detection with molecular novelty and lack of explicit decision thresholds. A four-tier architecture links source authentication, reproducible chemical fingerprinting, orthogonal prioritization and definitive characterization through documented decision gates. An 'evidence ladder' separates molecular-identification confidence from chemical novelty, biological novelty and translational relevance. The framework is intended to make an existing, largely tacit decision process more transparent, comparable and testable across laboratories.
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10. Synthetic lethality and context-selective DNA-damage response dependencies: from genetic vulnerability to pharmacological translation.
PMID:日期:2026-09-11Synthetic lethality has established DNA-damage response (DDR) dependencies as a major strategy for precision cancer therapy, exemplified by targeting BRCA1/2 and poly(ADP-ribose) polymerase (PARP). However, genetic dependency alone does not ensure pharmacological tractability or clinical utility. Emerging targets, including WRN, POLQ, USP1 and PARG, impose distinct requirements for ligandability, selectivity, target engagement, patient selection and the therapeutic window. A comparison of their structural biology, inhibitor chemotypes, SAR, pharmacodynamic strategies, resistance mechanisms and clinical development shows that successful translation requires matching a tumor-selective dependency to an experimentally validated, exposure-controllable pharmacological mechanism. This framework supports the rational prioritization of context-selective DDR dependencies beyond BRCA-PARP.