Peptides are versatile molecules with a growing relevance in addressing previously untreatable and complex diseases and targets. Computational methods offer powerful strategies to streamline peptide drug discovery by accelerating design–test cycles and guiding efforts toward promising candidates. The application of such tools requires specialized knowledge in informatics, programming, and statistics, with a growing number of computational tools and frameworks becoming available. In this review, we provide an overview of current computational approaches in peptide research, covering different phases of the computational pipeline, such as representation, similarity assessments, machine/deep learning (ML/DL) models, and peptide design. We further highlight available peptide cheminformatics tools based on their key features to facilitate their integration into peptide drug discovery pipelines.
{"title":"Peptide cheminformatics tools: making computational tasks accessible in peptide drug discovery","authors":"Vanessa Erckes , Massina Abderrahmane , Maud Jusot , Christian Steuer , Rodrigo Ochoa","doi":"10.1016/j.drudis.2026.104612","DOIUrl":"10.1016/j.drudis.2026.104612","url":null,"abstract":"<div><div>Peptides are versatile molecules with a growing relevance in addressing previously untreatable and complex diseases and targets. Computational methods offer powerful strategies to streamline peptide drug discovery by accelerating design–test cycles and guiding efforts toward promising candidates. The application of such tools requires specialized knowledge in informatics, programming, and statistics, with a growing number of computational tools and frameworks becoming available. In this review, we provide an overview of current computational approaches in peptide research, covering different phases of the computational pipeline, such as representation, similarity assessments, machine/deep learning (ML/DL) models, and peptide design. We further highlight available peptide cheminformatics tools based on their key features to facilitate their integration into peptide drug discovery pipelines.</div></div>","PeriodicalId":301,"journal":{"name":"Drug Discovery Today","volume":"31 2","pages":"Article 104612"},"PeriodicalIF":7.5,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040022","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}
Pub Date : 2026-01-21DOI: 10.1016/j.drudis.2026.104609
Hanxue Zhou, Jiaye Lu, Bei Yin, Quangang Zhu, Zhongjian Chen
Psoriasis is a chronic immune-mediated inflammatory disorder characterized by immune imbalance and keratinocyte hyperproliferation. Highly effective immunomodulators, with well-established safety profiles, have substantially transformed disease management, achieving durable remission in many patients and reshaping current therapeutic paradigms. However, several unmet clinical needs have driven increasing interest in next-generation nucleic-acid-based therapies, including siRNA, miRNA and therapeutic oligonucleotides, which enable precise and modular gene-level modulation. In this review, their therapeutic potential is summarized and key challenges - such as immunogenicity, off-target effects, delivery barriers and manufacturing scalability - are critically examined. Recent advances in biomaterials that facilitate safer and more-durable gene modulation are further summarized; and potential opportunities for future personalized strategies that complement existing biologic therapies are discussed.
{"title":"Achieving durable psoriasis remission with nucleic acid therapeutics.","authors":"Hanxue Zhou, Jiaye Lu, Bei Yin, Quangang Zhu, Zhongjian Chen","doi":"10.1016/j.drudis.2026.104609","DOIUrl":"10.1016/j.drudis.2026.104609","url":null,"abstract":"<p><p>Psoriasis is a chronic immune-mediated inflammatory disorder characterized by immune imbalance and keratinocyte hyperproliferation. Highly effective immunomodulators, with well-established safety profiles, have substantially transformed disease management, achieving durable remission in many patients and reshaping current therapeutic paradigms. However, several unmet clinical needs have driven increasing interest in next-generation nucleic-acid-based therapies, including siRNA, miRNA and therapeutic oligonucleotides, which enable precise and modular gene-level modulation. In this review, their therapeutic potential is summarized and key challenges - such as immunogenicity, off-target effects, delivery barriers and manufacturing scalability - are critically examined. Recent advances in biomaterials that facilitate safer and more-durable gene modulation are further summarized; and potential opportunities for future personalized strategies that complement existing biologic therapies are discussed.</p>","PeriodicalId":301,"journal":{"name":"Drug Discovery Today","volume":" ","pages":"104609"},"PeriodicalIF":7.5,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040109","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}
Pub Date : 2026-01-19DOI: 10.1016/j.drudis.2026.104606
Árpád Furka
Since the introduction of split and pool synthesis, making it possible to create even billion-member combinatorial libraries, two deconvolution possibilities have been extensively used: the one-bead one-compound (OBOC) and the DNA-encoded methods. The originally available back search screening and the possibilities offered by the later-developed omission peptide libraries and the positional scanning have long been forgotten. Here, I compared the advantages and disadvantages of the above-mentioned methods, emphasizing the advantage of screening free dissolved molecules in the back search method.
自从引入分裂和池合成（split and pool synthesis），使创建甚至十亿成员的组合文库成为可能以来，两种反卷积方法被广泛使用：一头一化合物（one- head one-compound， OBOC）和dna编码方法。最初可用的反向搜索筛选和后来开发的遗漏肽库和位置扫描提供的可能性早已被遗忘。在这里，我比较了上述方法的优缺点，强调了反向搜索法在筛选游离溶解分子方面的优势。
{"title":"Unfairly forgotten deconvolution methods of combinatorial libraries","authors":"Árpád Furka","doi":"10.1016/j.drudis.2026.104606","DOIUrl":"10.1016/j.drudis.2026.104606","url":null,"abstract":"<div><div>Since the introduction of split and pool synthesis, making it possible to create even billion-member combinatorial libraries, two deconvolution possibilities have been extensively used: the one-bead one-compound (OBOC) and the DNA-encoded methods. The originally available back search screening and the possibilities offered by the later-developed omission peptide libraries and the positional scanning have long been forgotten. Here, I compared the advantages and disadvantages of the above-mentioned methods, emphasizing the advantage of screening free dissolved molecules in the back search method.</div></div>","PeriodicalId":301,"journal":{"name":"Drug Discovery Today","volume":"31 2","pages":"Article 104606"},"PeriodicalIF":7.5,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146016829","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}
Pub Date : 2026-01-19DOI: 10.1016/j.drudis.2026.104607
Daniel O'Keeffe
The ambitions of Patient Safety and ongoing Pharmacovigilance are quite clear - making drugs and therapies as safe as they can be, so that they deliver ever better outcomes. And yet this entire endeavour is threatened by the enormous complexity in capturing meaningful, high-quality insights and acting on them swiftly. Today, tech vendors are promising the world with AI. Leave the chaos as it is, and let AI make sense of it, they say. But it is not that simple. Until regulators insist that better data is captured at source, and until the entire industry understands that AI is not a simple fix to the complexity problem, Safety functions and their patients will be no better off. In this hard-hitting article, illustrated by clear examples of what's still going wrong, Qinecsa's Daniel O'Keeffe explains why it's time for a major shift in approach to the problem of fragmented patient safety data. Drawing on the persisting challenges affecting pharma companies today, he will explain.
{"title":"Regulators must intervene to drive up the quality and impact of patient safety reporting.","authors":"Daniel O'Keeffe","doi":"10.1016/j.drudis.2026.104607","DOIUrl":"10.1016/j.drudis.2026.104607","url":null,"abstract":"<p><p>The ambitions of Patient Safety and ongoing Pharmacovigilance are quite clear - making drugs and therapies as safe as they can be, so that they deliver ever better outcomes. And yet this entire endeavour is threatened by the enormous complexity in capturing meaningful, high-quality insights and acting on them swiftly. Today, tech vendors are promising the world with AI. Leave the chaos as it is, and let AI make sense of it, they say. But it is not that simple. Until regulators insist that better data is captured at source, and until the entire industry understands that AI is not a simple fix to the complexity problem, Safety functions and their patients will be no better off. In this hard-hitting article, illustrated by clear examples of what's still going wrong, Qinecsa's Daniel O'Keeffe explains why it's time for a major shift in approach to the problem of fragmented patient safety data. Drawing on the persisting challenges affecting pharma companies today, he will explain.</p>","PeriodicalId":301,"journal":{"name":"Drug Discovery Today","volume":" ","pages":"104607"},"PeriodicalIF":7.5,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146016859","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}
Pub Date : 2026-01-16DOI: 10.1016/j.drudis.2026.104605
Jiazhen He , Helen Lai , Lakshidaa Saigiridharan , Gian Marco Ghiandoni , Kinga Jenei , Umur Gokalp , Ajša Nuković , Ola Engkvist , Jon Paul Janet , Samuel Genheden
Agentic systems that are based on large language models (LLMs) have emerged as promising tools in the chemistry domain over the past few years. Early examples included work on CoScientist, Chemcrow, and LLM-RDF, which showcased the potential of agentic systems to assist in chemical research, in the orchestration of cheminformatics tools, and in synthetic reaction development. Despite this, the current literature lacks examples of the real-world adoption of such systems in drug discovery. We present such an example by describing our work on an agentic system called ChatInvent, which has been integrated into the discovery pipeline at AstraZeneca to aid in molecular design and synthesis planning. We discuss how the system evolved from a proof-of-concept single agent into an extensible, robust, and scalable multi-agent architecture with a graphical user interface. We emphasize the lessons learnt and the challenges that persist as we continue to work on this project, and share our perspectives on the future of agentic systems in our domain.
{"title":"Democratising real-world drug discovery through agentic AI","authors":"Jiazhen He , Helen Lai , Lakshidaa Saigiridharan , Gian Marco Ghiandoni , Kinga Jenei , Umur Gokalp , Ajša Nuković , Ola Engkvist , Jon Paul Janet , Samuel Genheden","doi":"10.1016/j.drudis.2026.104605","DOIUrl":"10.1016/j.drudis.2026.104605","url":null,"abstract":"<div><div>Agentic systems that are based on large language models (LLMs) have emerged as promising tools in the chemistry domain over the past few years. Early examples included work on CoScientist, Chemcrow, and LLM-RDF, which showcased the potential of agentic systems to assist in chemical research, in the orchestration of cheminformatics tools, and in synthetic reaction development. Despite this, the current literature lacks examples of the real-world adoption of such systems in drug discovery. We present such an example by describing our work on an agentic system called ChatInvent, which has been integrated into the discovery pipeline at AstraZeneca to aid in molecular design and synthesis planning. We discuss how the system evolved from a proof-of-concept single agent into an extensible, robust, and scalable multi-agent architecture with a graphical user interface. We emphasize the lessons learnt and the challenges that persist as we continue to work on this project, and share our perspectives on the future of agentic systems in our domain.</div></div>","PeriodicalId":301,"journal":{"name":"Drug Discovery Today","volume":"31 2","pages":"Article 104605"},"PeriodicalIF":7.5,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145996841","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}
Pub Date : 2026-01-14DOI: 10.1016/j.drudis.2026.104604
Nanziba Sharmin Hossain , Nishat Tasnim , Jannatul Ferdoush , Ayan Roy , Suvash C. Saha , Ayan Saha
Lung-cancer-on-a-chip (LCOC) technologies have advanced rapidly, yet most models evaluate mechanical strain, patient-derived tumors, multi-organ interactions, artificial intelligence (AI) analytics, and clustered regularly interspaced short palindromic repeats (CRISPR) editing in isolation. In this review, we uniquely integrate these emerging components into a unified framework centered on the breathing LCOC. We highlight how embedding patient-derived lung tumor fragments into cyclically stretched microenvironments, then linking them to downstream organ compartments, enables patient-specific mapping of metastatic routes under physiologically relevant mechanics. We further describe how continuous high-resolution imaging from these platforms can feed AI pipelines for automated drug–response prediction and metastatic trajectory simulation, and how on-chip CRISPR editing enables accurate investigation of metastatic drivers within dynamic, strain-modulated microenvironments. By synthesizing these technologies, we outline a next-generation, personalized multi-organ-on-chip architecture capable of predicting individual disease progression without direct patient risk. We also address practical barriers, including tumor fragility under strain, imaging domain shift, and gene-editing delivery challenges, and how to overcome such barriers.
{"title":"Next-generation lung-cancer-on-a-chip: Toward personalized therapy, AI, and CRISPR-driven models","authors":"Nanziba Sharmin Hossain , Nishat Tasnim , Jannatul Ferdoush , Ayan Roy , Suvash C. Saha , Ayan Saha","doi":"10.1016/j.drudis.2026.104604","DOIUrl":"10.1016/j.drudis.2026.104604","url":null,"abstract":"<div><div>Lung-cancer-on-a-chip (LCOC) technologies have advanced rapidly, yet most models evaluate mechanical strain, patient-derived tumors, multi-organ interactions, artificial intelligence (AI) analytics, and clustered regularly interspaced short palindromic repeats (CRISPR) editing in isolation. In this review, we uniquely integrate these emerging components into a unified framework centered on the breathing LCOC. We highlight how embedding patient-derived lung tumor fragments into cyclically stretched microenvironments, then linking them to downstream organ compartments, enables patient-specific mapping of metastatic routes under physiologically relevant mechanics. We further describe how continuous high-resolution imaging from these platforms can feed AI pipelines for automated drug–response prediction and metastatic trajectory simulation, and how on-chip CRISPR editing enables accurate investigation of metastatic drivers within dynamic, strain-modulated microenvironments. By synthesizing these technologies, we outline a next-generation, personalized multi-organ-on-chip architecture capable of predicting individual disease progression without direct patient risk. We also address practical barriers, including tumor fragility under strain, imaging domain shift, and gene-editing delivery challenges, and how to overcome such barriers.</div></div>","PeriodicalId":301,"journal":{"name":"Drug Discovery Today","volume":"31 2","pages":"Article 104604"},"PeriodicalIF":7.5,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145987664","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}
Pub Date : 2026-01-14DOI: 10.1016/j.drudis.2026.104603
Nilakshi Deka , Niharika Nag , Timir Tripathi
Intrinsically disordered proteins (IDPs) and their involvement in liquid–liquid phase separation (LLPS) have reshaped our understanding of how cells organize biochemical reactions in space and time. Unlike structured proteins, IDPs populate highly dynamic conformational ensembles that promote weak, multivalent interactions and enable the formation of biomolecular condensates (BCs). When these phase-separation processes become dysregulated, they often contribute to diseases, including cancer, neurodegeneration, viral infection, and certain metabolic disorders. This emerging biology presents both conceptual challenges and promising opportunities for drug discovery. Therapeutic strategies now extend beyond classical small molecules to include physicochemical modulators, post-translational modification mimetics, peptides, nanobodies, and targeted protein degradation tools such as PROTACs (proteolysis-targeting chimeras). In parallel, artificial intelligence and advanced computational methods are beginning to illuminate the complex conformational landscapes of IDPs and provide new ways to predict, screen, and optimize modulators of condensate behavior. However, major obstacles remain, including the intrinsic heterogeneity of disordered regions, context-dependent condensate properties, and variability across cell types and patients. Addressing these hurdles will require integrated advances in biosensors, high-throughput screening, quantitative imaging, and delivery technologies. Together, these developments are paving the way for a new generation of targeted therapies that exploit or correct the phase behavior of IDPs.
{"title":"Intrinsically disordered proteins and liquid–liquid phase separation in drug discovery","authors":"Nilakshi Deka , Niharika Nag , Timir Tripathi","doi":"10.1016/j.drudis.2026.104603","DOIUrl":"10.1016/j.drudis.2026.104603","url":null,"abstract":"<div><div>Intrinsically disordered proteins (IDPs) and their involvement in liquid–liquid phase separation (LLPS) have reshaped our understanding of how cells organize biochemical reactions in space and time. Unlike structured proteins, IDPs populate highly dynamic conformational ensembles that promote weak, multivalent interactions and enable the formation of biomolecular condensates (BCs). When these phase-separation processes become dysregulated, they often contribute to diseases, including cancer, neurodegeneration, viral infection, and certain metabolic disorders. This emerging biology presents both conceptual challenges and promising opportunities for drug discovery. Therapeutic strategies now extend beyond classical small molecules to include physicochemical modulators, post-translational modification mimetics, peptides, nanobodies, and targeted protein degradation tools such as PROTACs (proteolysis-targeting chimeras). In parallel, artificial intelligence and advanced computational methods are beginning to illuminate the complex conformational landscapes of IDPs and provide new ways to predict, screen, and optimize modulators of condensate behavior. However, major obstacles remain, including the intrinsic heterogeneity of disordered regions, context-dependent condensate properties, and variability across cell types and patients. Addressing these hurdles will require integrated advances in biosensors, high-throughput screening, quantitative imaging, and delivery technologies. Together, these developments are paving the way for a new generation of targeted therapies that exploit or correct the phase behavior of IDPs.</div></div>","PeriodicalId":301,"journal":{"name":"Drug Discovery Today","volume":"31 2","pages":"Article 104603"},"PeriodicalIF":7.5,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145987693","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}
Pub Date : 2026-01-12DOI: 10.1016/j.drudis.2026.104601
Björn Niebel , Wanyin Chen , Kaori Mukai , Christophe Henry
Lysosome-targeting chimeras (LYTACs) have emerged as a powerful modality in the field of extracellular targeted protein degradation (eTPD). The proximity-inducing mode of action of LYTACs can be applied to a growing number of lysosome-targeting receptors (LTRs) and membrane-bound E3 ligases to degrade extracellular proteins. In this review, we highlight preceding eTPD approaches and discuss in depth the plethora of newly identified LTRs that can be exploited in a LYTAC molecule. To provide guidance in the fast-growing LYTAC field, we elaborate on parameters to assess the preclinical validation of the various TACnologies, highlight opportunities for engineering catalytic LYTACs, and finish with pharmacokinetic (PK) considerations.
{"title":"LYTACs and other extracellular targeted protein degradation TACnologies: guiding principles and potential clinical prospects","authors":"Björn Niebel , Wanyin Chen , Kaori Mukai , Christophe Henry","doi":"10.1016/j.drudis.2026.104601","DOIUrl":"10.1016/j.drudis.2026.104601","url":null,"abstract":"<div><div>Lysosome-targeting chimeras (LYTACs) have emerged as a powerful modality in the field of extracellular targeted protein degradation (eTPD). The proximity-inducing mode of action of LYTACs can be applied to a growing number of lysosome-targeting receptors (LTRs) and membrane-bound E3 ligases to degrade extracellular proteins. In this review, we highlight preceding eTPD approaches and discuss in depth the plethora of newly identified LTRs that can be exploited in a LYTAC molecule. To provide guidance in the fast-growing LYTAC field, we elaborate on parameters to assess the preclinical validation of the various TACnologies, highlight opportunities for engineering catalytic LYTACs, and finish with pharmacokinetic (PK) considerations.</div></div>","PeriodicalId":301,"journal":{"name":"Drug Discovery Today","volume":"31 2","pages":"Article 104601"},"PeriodicalIF":7.5,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145984262","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}
Pub Date : 2026-01-08DOI: 10.1016/j.drudis.2026.104600
Alban Lepailleur, Damien Geslin, Johanna Giovannini, Bertrand Cuissart, Jean-Luc Lamotte, Ronan Bureau
Understanding ligand selectivity and efficacy at melatonin (MT1, MT2) and serotonin (5-HT2C) receptors remains a key challenge in central nervous system (CNS) drug discovery. Ligands combining MT1/MT2 agonism with 5-HT2C antagonism are of therapeutic relevance in depression and circadian rhythm disorders. This review provides a comprehensive overview of ligands reported for these receptors in ChEMBL, with an emphasis on compounds evaluated across multiple targets. A pharmacophore-based approach was applied to group ligands by shared features, revealing pharmacomodulations influencing receptor selectivity and polypharmacology. Together, these insights can inspire the rational design of selective or polypharmacological ligands, offering new opportunities for multitarget drug discovery.
{"title":"Mapping ligands for MT<sub>1</sub>/MT<sub>2</sub> and 5-HT<sub>2C</sub> receptors: Chemotypes, SAR, and polypharmacology.","authors":"Alban Lepailleur, Damien Geslin, Johanna Giovannini, Bertrand Cuissart, Jean-Luc Lamotte, Ronan Bureau","doi":"10.1016/j.drudis.2026.104600","DOIUrl":"10.1016/j.drudis.2026.104600","url":null,"abstract":"<p><p>Understanding ligand selectivity and efficacy at melatonin (MT<sub>1</sub>, MT<sub>2</sub>) and serotonin (5-HT<sub>2C</sub>) receptors remains a key challenge in central nervous system (CNS) drug discovery. Ligands combining MT<sub>1</sub>/MT<sub>2</sub> agonism with 5-HT<sub>2C</sub> antagonism are of therapeutic relevance in depression and circadian rhythm disorders. This review provides a comprehensive overview of ligands reported for these receptors in ChEMBL, with an emphasis on compounds evaluated across multiple targets. A pharmacophore-based approach was applied to group ligands by shared features, revealing pharmacomodulations influencing receptor selectivity and polypharmacology. Together, these insights can inspire the rational design of selective or polypharmacological ligands, offering new opportunities for multitarget drug discovery.</p>","PeriodicalId":301,"journal":{"name":"Drug Discovery Today","volume":" ","pages":"104600"},"PeriodicalIF":7.5,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145948175","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}
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