Expert Opinion on Drug Discovery最新文献

Introduction: Understanding the interactions between functional groups, ligands, molecular fragments, and whole molecules is critical in modern drug discovery. Key to this endeavor is the theoretical development of the fundamental inter-particle forces at play and their implementation in numerous software packages that allow the calculation of interaction energies at varying levels of theory ranging from the entirely classical at one extreme to the fully quantum mechanical at the other.

Areas covered: In this review, the authors consider the concept of an intermolecular potential energy function and its separation into short- and long-range regions. This is followed by a summary of the perturbation theory calculation of the electrostatic, induction, and dispersion energy shifts by expanding the charge distribution in terms of source multipole moments. Next, the authors outline the construction of a typical molecular force field and its parameterization; they also discuss the fundamental background of molecular dynamics (MD) simulations, their implementation in several well-known software packages and their deployment in modern computational drug discovery, including recent work with Artificial Intelligence and Machine Learning techniques. Papers cited by SSC were the result of a literature search conducted using PubMed and Google Scholar during Jan-July 2025 as well as from the authors' personal literature stock.

Expert opinion: While the underlying quantum mechanical theory of intermolecular forces is well-known, their accurate and reliable calculation for an ever-growing variety of increasingly complex systems mirrors the advances in computational hardware on which such simulations are performed. Coupled with emerging machine learning techniques, this allows for the rapid and efficient computer-aided discovery of potential new drug candidates, in the process revolutionizing research and development in both academia and industry.

Background: Cathepsin K (CTSK), a cysteine protease of the papain family, exhibits high expression in activated osteoclasts, making it a key therapeutic target for osteoporosis. However, there are currently no CTSK inhibitors available for clinical use.

Research design and methods: The authors employed a combination of deep learning approaches and experimental methods to identify novel CTSK inhibitors. Firstly, the authors utilized Chemprop to develop a predictive model for predicting CTSK inhibition. Subsequently, the top 100 predicted molecules were selected for experimental validation, with the most potent inhibitors chosen for further analysis, including enzyme kinetics, molecular docking, molecular dynamics simulations, and RANKL-induced osteoclastogenesis assays.

Results: The authors identified six compounds exhibiting concentration-dependent CTSK inhibitory effects, with Quercetin, γ-Linolenic acid (GLA), and Benzyl isothiocyanate (BITC) demonstrating the highest potency. Enzyme kinetics studies revealed that these inhibitors employ distinct mechanisms of CTSK inhibition. Molecular dynamics simulations further showed that Quercetin and BITC form stable interactions at the CTSK active site. Moreover, in-vitro studies demonstrated that Quercetin and GLA significantly inhibit RANKL-induced osteoclastogenesis in RAW264.7 cells.

Conclusions: This study led to the development of a deep learning model capable of predicting CTSK inhibitors and identified Quercetin, GLA, and BITC as promising candidates for the treatment of osteoporosis.

Introduction: Despite advances in antidepressant development, many patients with major depressive disorder (MDD) remain inadequately treated. Gepirone, a selective 5-HT1A agonist without reuptake inhibition, offers a novel mechanism potentially improving efficacy and tolerability over selective serotonin reuptake inhibitors (SSRIs) and earlier agents.

Areas covered: This case history describes gepirone's discovery and development, including its pharmacodynamic profile, preclinical data on pharmacology, mechanism of action, and effects on depressive-like behavior and anxiety, as well as early clinical findings on its safety and efficacy in major depressive disorder. The review draws on English peer-reviewed articles and trials (1983-2025) from major databases, including PubMed, Embase, and ClinicalTrials.gov.

Expert opinion: Although gepirone ER was approved due to evidence supporting clinical efficacy and favorable tolerability in MDD, its antidepressant effect size is modest relative to other monoamine-based antidepressants. It may offer particular benefit for patients who experience anxiety-related adverse effects on standard antidepressants and may be particularly useful in anxious depression or patients prioritizing tolerability. Approved by the U.S. Food and Drug Administration in 2023, withdrawn in 2024, the product will relaunch in late 2025. Future research should assess head-to-head efficacy, pharmacoeconomics, real-world outcomes, and its potential role in treatment-resistant depression.

Introduction: Advances in artificial intelligence (AI) have transformed the drug design and discovery process, introducing novel methods that can reduce costs, increase success rates, and shorten development timelines. However, due to the complexity and multifactorial nature of this process, no single AI approach is likely to be universally effective.

Areas covered: This review summarizes progress made over the past five years toward diverse drug development goals using AI tools. It also discusses the main challenges that inhibit the development and adoption of a broad AI solution in this field.

Expert opinion: Despite major advancements, AI fails to reach its full potential due to issues related to data quality, model complexity, computational costs, and organizational barriers. At present, the effectiveness of any AI approach heavily depends on its application. Ultimately, while the world strives for a general-purpose AI, no method in drug discovery can yet be considered universally applicable, and rather than relying on a one-size-fits-all solution, individual trade-offs and research objectives need to be carefully aligned to harness AI's potential in drug discovery.

Introduction: Carbohydrates are ubiquitous biomolecules that play indispensable roles in living systems, functioning in cellular communication, genetic information storage, cellular energy provision, structural support, host-pathogen interactions, and the biosynthesis of secondary metabolites such as antibiotics. Their inherent multifunctionality, stereochemical complexity, and natural affinity for binding specific proteins make them highly attractive scaffolds for drug discovery. Despite their biological significance, carbohydrate-based therapeutics remain underrepresented in the pharmacopoeia, comprising only a small fraction of approved drugs. This underutilization highlights the untapped potential of carbohydrates as sources of novel therapeutic agents with innovative mechanisms of action.

Areas covered: In this concise review, the authors summarize the current landscape of approved small-molecule drugs containing carbohydrate moieties and highlight recent advances in carbohydrate-based compounds with a wide spectrum of pharmacological activities, including antimicrobial, anticancer, antidiabetic, anti-inflammatory, neuroprotective, antiviral, and enzyme inhibitory effects.

Expert opinion: Carbohydrate-based therapeutics are transitioning from niche applications to mainstream drug discovery platforms and, as such, hold significant promise for generating future generations of pharmaceuticals. Consequently, the authors firmly advocate continued efforts in designing carbohydrate-derived drug candidates which are well positioned to deliver first or best-in-class drugs.

Introduction: Long COVID (LC), also known as post-acute COVID-19 syndrome (PASC), has emerged as a significant public health concern characterized by persistent symptoms following SARS-CoV-2 infection. This condition affects regardless of initial illness severity and can significantly impair daily functioning. Understanding the implications of LC is crucial, given that approximately 6.9 % of adults reported related symptoms in 2022, with increased prevalence among women and individuals of Hispanic descent. The pathogenesis of LC is multifactorial, involving mechanisms such as endothelial dysfunction, chronic inflammation, immune dysregulation, and potential viral persistence. The clinical manifestations include fatigue, cognitive impairment, musculoskeletal pain, and sleep disturbances. Current research emphasizes the importance of early antiviral interventions and vaccines to mitigate the risk of developing LC. Despite promising therapies like anti-inflammatory agents and metabolic enhancers, the lack of established biomarkers complicates diagnosis and treatment.

Areas covered: The authors provide an overview of the pathogenesis of LC and briefly review the currently available therapy. The authors then give their perspectives on how best future drug discovery efforts can be utilized to address the current demand for novel LC therapeutics to reduce the burden of this public health problem.

Expert opinion: Progress has been made in understanding the pathophysiology and potential treatment options, as well as in establishing reliable biomarkers for potential tailored strategies. Future research should prioritize both pharmacological and non-pharmacological interventions to enhance patient outcomes and quality of life. Addressing these challenges is essential for developing comprehensive care protocols for individuals affected by LC.

Introduction: The efficacy of current treatments for bacterial infections is under threat due to the continuing rise in the prevalence of antimicrobial resistance (AMR). Resistance can arise due to a wide variety of changes in the bacterial cell that prevent the antibiotic from acting on its target. This can be through changes to the target itself or changes that limit access to the target. Strategies to overcome resistance therefore either seek to reestablish access to the target or to engage a different target for which resistance is yet to arise. This has been done successfully in the clinic through co-dosing of more than one molecule, but a long-held aim has been to achieve efficacy in a single 'hybrid' molecule.

Areas covered: The authors review the progress since 2016 of hybrid antibiotics in clinical trials, cover some advances in preclinical research into dual-acting hybrids, and examine alternative approaches to using bi-functional hybrid molecules to tackle AMR.

Expert opinion: Many contributory factors, both scientific and economic, have limited the success of dual-acting hybrids where both partners are antibiotics. The success of cefiderocol highlights the potential of linking molecules that target bacteria directly and non-antibiotics. These strategies offer some exciting possibilities.

Introduction: Click chemistry, first introduced by Sharpless and colleagues in 2001, has been an essential tool of drug research owing to its modularity, high efficiency, excellent yields, chemoselectivity, and mild reaction conditions.

Areas covered: This review provides an overview of recent advances in drug development based on click chemistry over the past five years. It highlights key applications including fluorescent probes, lead identification and optimization, drug delivery systems, as well as emerging therapeutic modalities such as antibody-drug conjugates and protein degraders. The literature search was primarily conducted using PubMed and Web of Science.

Expert opinion: Click chemistry serves as a powerful enabler of accelerated drug discovery and development. Nevertheless, its clinical translation faces challenges such as physiological interference, pharmacokinetic requirements, and the potential toxicity of metal catalysts. Going forward, research should prioritize optimizing click chemistry reactions to enhance biocompatibility, safety, and stability. Meanwhile, combining click chemistry with artificial intelligence offers promise for identifying structurally diverse candidate molecules that are also synthetically feasible.

Introduction: Congestive heart failure (CHF) is a complex multi-organ syndrome representative of many chronic 'diseases,' and as such it has proven resistant to traditional cell-based drug discovery cannot readily be captured the relevant systemic biology. In vivo drug discovery screens offer unique opportunities to identify the initial dysfunction which ultimately drives heart failure (HF) and novel pathways modifying the cardiac response to injury.

Areas covered: In this review, the author discusses phenotype-driven screens which allow rigorous and unbiased approaches to the biological systems which underpin HF (PubMed search terms on 07/11/2025-heart failure, cardiomyopathy, zebrafish, screen, drug). The rationale for specific models of HF and the relevance of the zebrafish in screens for suppressors of HF is discussed. Central principles are detailed for the successful design and execution of phenotypic screens for HF modifiers. A major focus is the development of scalable HF assays in the zebrafish.

Expert opinion: In vivo phenotypic screening in the zebrafish is a reproducible approach to the identification of potent suppressors of complex multisystem disorders including different forms of HF. Design features associated with success are the rigor and human fidelity of the initial mechanistic modeling and quantitative screen endpoints.

Introduction: Psoriasis is a chronic, immune-mediated inflammatory skin disorder with a multifactorial pathogenesis involving keratinocyte proliferation, dysregulated immune responses, and vascular remodeling. The development of effective therapeutics mainly relies on preclinical models that can reproduce disease-relevant mechanisms.

Areas covered: This review outlines current in vivo psoriasis models, including spontaneous mutation models, transgenic and knockout mice, xenotransplantation systems, and cytokine-induced and imiquimod-induced models. Each model is evaluated for its ability to replicate key histological and immunological features of human psoriasis, such as acanthosis, immune cell infiltration, and cytokine network activation. The utility of CRISPR/Cas9 gene editing in generating targeted models is also discussed, thus highlighting its potential use for mechanistic studies. Finally, this review also emphasizes the limitations in translational applicability and the need for multimodel validation strategies regarding psoriasis. This article was based on a comprehensive literature search using PubMed, Scopus, and Google Scholar databases, covering publications from January 2015 to March 2025.

Expert opinion: Despite extensive model development, no single system fully mimics human psoriatic disease. The imiquimod-induced model remains widely used due to its practicality, although it better reflects acute inflammation compared with chronic pathology. The combination of complementary models and the incorporation of human-derived tissues or immune components may improve translational relevance. Advances in genome editing and humanized systems are likely to shape the future of psoriasis research and therapeutic discovery.