Expert Opinion on Drug Discovery最新文献

Background: Skin aging is linked to the overactivity of matrix metalloproteinases (MMPs) and elastase, making their inhibition a promising approach for antiaging. This study aimed to discover novel antiaging peptides from Chlorella proteins using high-throughput virtual screening.

Methods: Batch molecular docking protocol with a custom Python script for 3D peptide structure modeling and AutoDock Vina was applied to predict inhibitory peptides on MMPs and elastase from 1,965 peptides theoretically resistant to gastrointestinal digestion. The top candidates were synthesized for activity assay, and MD simulation illustrated the binding mechanism of potent peptides.

Results: Seventeen peptides with a binding energy < -7.0 kcal/mol showed IC₅₀ ≤ 150 μM. Peptide DGSY acted high potency against MMP-1 (IC₅₀ = 32.6 μM), and HDISHW inhibited MMP-9 and elastase at the lowest IC₅₀ (20.1, 16.5 μM). GAASF inhibited all three enzymes (IC₅₀ = 54.0, 41.9, 62.5 μM). MD simulations confirmed the stability of these peptide-protein complexes, which coincided with the in vitro activity well.

Conclusion: The virtual strategy efficiently identified multifunctional antiaging peptides and could accelerate the discovery of bioactive peptides for cosmetic and therapeutic use. Additionally, its efficiency makes it useful for building high-quality training sets in deep learning models for bioactive structure discovery.

Introduction: The recent surge in cholera outbreaks worldwide, partly driven by climate change, highlights its potential as a significant public health threat. The absence of definitive treatments underscores the urgent need for developing effective targeted therapeutics. The cholera holotoxin comprises a catalytically active A1 subunit, which mediates ADP-ribosylation to induce secretory diarrhea, and a pentameric B subunit responsible for toxin-host cell attachment via GM1 receptors. A1 activation requires binding to human ADP-ribosylation factor 6 (ARF6). Although the inhibition of B-pentamer - GM1 binding has been extensively investigated, several structural and pharmacokinetic challenges remain.

Areas covered: This article is based on a keyword-based literature survey across relevant research repositories, covering studies published up to 2025. It summarizes the structure- and ligand-based molecular modeling approaches employed for identifying inhibitors targeting toxin-host binding, including GM1 mimetics, glycomimetics, and natural compounds. Alternative avenues of toxin inhibition, including occlusion of the B-pentamer pore, A1 catalytic site, and the A1-ARF6 interface to disrupt toxin assembly, ADP-ribosylation, and A1 activation, respectively, are also discussed.

Expert opinion: Targeting the B-pentamer pore, A1 active site, or A1-ARF6 interface holds significant therapeutic potential against cholera-induced dehydration and hypovolaemic shock. These underexplored yet promising druggable targets warrant further investigation for developing effective, targeted therapies.

Introduction: The discovery of romosozumab, a monoclonal antibody to sclerostin and treatment option for severe osteoporosis, resulted from convergent genetic research of persons with rare hyperostotic bone diseases and the discovery of the Wnt-signaling pathway, a vital pathway in bone metabolism.

Areas covered: The authors provide an overview of the discovery of the SOST gene in humans and of Wnt signaling in animals, leading to the identification of sclerostin, a major regulator of bone formation and resorption. The authors further provide an overview of the studies that led to the development of romosozumab, a unique dual action monoclonal antibody that increases bone formation while decreasing bone resorption.

Expert opinion: In postmenopausal women, the administration of romosozumab over one year decreased the risk of vertebral and clinical fractures versus placebo and versus alendronate. Furthermore, sequential treatment, switching romosozumab over to denosumab, reduced the risk of vertebral fractures compared to switching the placebo to denosumab. Meanwhile, switching romosozumab to alendronate reduced the risk of vertebral, clinical, nonvertebral, and hip fractures compared to continuous alendronate. An imbalance in cardiovascular events was found when using romosozumab in comparison to alendronate but not versus placebo. Romosozumab was eventually approved by EMA and FDA in 2019 for the treatment of patients with very high risk of fractures while considering their cardiovascular risk and is available and reimbursed in many countries.

Introduction: Oxetanes are increasingly prevalent motifs in medicinal chemistry. While oxetanes feature famously in the taxol family, it was not until the recent approval of rilzabrutinib that they have been validated in a fully synthetic drug. These four-membered oxygen-containing rings are valued for their ability to modulate key physicochemical properties such as polarity, lipophilicity, and metabolic stability. Their incorporation can improve aqueous solubility, reduce metabolic liability, reduce basicity of adjacent amines, and redirect metabolic clearance away from cytochrome P450 enzymes.

Areas covered: This review examines the role of the oxetane in nine clinical candidates, one very recently approved, two recently discontinued clinical candidates, two tool compounds, and three lead compounds disclosed in 2024. Recent advances indexed in SciFinder-n (2017-2024) in the process-scale and laboratory-scale synthetic routes are discussed, highlighting ongoing innovation required to access these motifs efficiently and sustainably.

Expert opinion: The regulatory approval of rilzabrutinib and likely approval of ziresovir provide confidence in using oxetanes as important elements in drug design. While oxetanes have so far been incorporated primarily as pendant groups to optimize physicochemical properties, their use as scaffolding and binding elements presents an exciting opportunity. Enhanced synthetic accessibility to oxetane derivatives will expedite their inclusion in drug discovery campaigns.

Introduction: Malaria remains a major health challenge, with increasing resistance to frontline chemotherapy. Recent cheminformatics studies have revealed that potent antiplasmodials occupy a distinct antimalarial chemical space (AMCS), defined by specific property cutoffs.

Areas covered: We show that only ~ 6-8% of compounds in the representative commercial libraries are AMCS-compliant, emphasizing the need for pre-filtering to improve their suitability for phenotypic screening. Most clinically used antimalarials and several clinically used oral drugs repurposed for antimalarial activity comply with AMCS indicating the important role AMCS can play in rationalizing drug repurposing studies. Furthermore, AMCS-guided scaffold modification offers a valuable strategy during lead optimization. With the help of recent literature examples we show how AMCS can be exploited for multimodal antimalarials or explain confounding structure-activity relationships.

Expert opinion: Due to the lack of funding and economically weaker patient population new antimalarials need to be cost-effective. The concept of AMCS can be harnessed to achieve this goal. Although there are inherent limitations associated with using hard cut offs and existing computational methodologies, the AMCS has strong potential to identify novel chemical matter for designing antimalarials that are resilient to resistance development.

Introduction: In silico technologies are increasingly shaping vaccine development, supporting the field beyond empirical discovery toward rational, data-driven design. Contemporary computational pipelines enable rapid antigen screening, high-precision epitope-MHC binding prediction, structural modeling, and immune response simulations. These approaches are accelerating vaccine discovery not only for infectious diseases but also in oncology, where neoantigen prediction underpins personalized cancer immunotherapy.

Areas covered: This review explores recent advances in computational pipelines for epitope-based vaccine design, covering antigen discovery; B- and T-cell epitope mapping; safety and specificity assessment; vaccine construct assembly with adjuvants and linkers; structural modeling; and immune-response simulations that predict efficacy in specific disease contexts using advanced platforms. It showcases applications in infectious diseases, including SARS-CoV-2, tuberculosis, and influenza, and poxivirus infections, as well as in cancer immunotherapy. It is based on literature obtained through searches utilizing PubMed, Scopus, and Web of Science databases covering publications up to 2025, using combinations of keywords such as epitope-based vaccines, reverse vaccinology, immunoinformatics, and immune system simulation.

Expert opinion: In silico approaches offer a transformative advantage to vaccine research by delivering speed, cost-efficiency, and enhanced precision. Yet the predictive power of current computational pipelines is still constrained by algorithmic limitations and by their incomplete integration of immune-regulatory processes. Progress in artificial intelligence, multi-omics integration, and formal recognition of digital evidence by regulatory agencies will be crucial for narrowing the gap between computational predictions and experimental validation. Ultimately, combining predictive immunoinformatics with advanced immune simulations and rigorous verification could help establish in silico methodologies as a cornerstone of next-generation vaccine development.

Introduction: N-methyl-D-aspartate (NMDA) receptor antagonists such as phencyclidine (PCP) and ketamine can induce schizophrenic features in healthy volunteers and exacerbate the symptoms in schizophrenic patients. Furthermore, the administration of NMDA receptor antagonists to rodents produces hyperlocomotion. The ability of drugs to attenuate this hyperlocomotion correlates with clinical efficacy on positive symptoms. Similarly, social withdrawal is taken as a surrogate of unsociability in schizophrenia. Furthermore, first episode psychosis and chronic schizophrenia can be modeled by acute and subchronic administration of NMDA receptor blockers, respectively. Therefore, the NMDA hypofunction model provides a powerful tool to develop new therapeutic strategies in drug discovery to treat schizophrenia.

Areas covered: This perspective describes the similitudes between schizophrenia in humans and the traits demonstrated by rodent models based upon the hypofunction of NMDA receptors. Comparisons are made in terms of behavioral, neurochemical, neuroimaging and neurophysiological studies. Different therapeutic responses are also discussed.

Expert opinion: Both schizophrenic patients and developed rodent models exhibit many similitudes such as decreased expression of NMDA receptors, enhanced dopaminergic and serotonergic transmission as well as altered gamma oscillations and deficits in cognitive paradigms. The NMDA receptor antagonism model can thus represent an excellent strategy to study the neurobiological underpinnings of schizophrenia and the potential therapeutic role of new antipsychotic drugs.

Introduction: The search for molecular novelty frequently collides with the fact that drug candidates with the best translational prospects are confined to - or concentrated in - defined regions of chemical space. The new possibilities of AI, particularly retrosynthesis prediction and generative AI, allow for the automated or semi-automated exploration of less restricted and unexplored areas of chemical space.

Areas covered: The notion of novelty in drug discovery is discussed, and representative examples of AI-guided de novo drug design, optimization, and retrosynthesis prediction are presented, with a focus on reports on open-source tools published in the last 3 years (2022-2025). Scopus was used to search relevant literature.

Expert opinion: Modern deep learning architectures have been adapted for the de novo design and molecular optimization. These technologies, and especially those based on conditional generation, will possibly have a great impact on expanding the regions of chemical space that are exploited therapeutically. However, there are some persistent challenges in the field that are gradually being addressed, including how to assess the synthetic accessibility of designed molecules without compromising the generation of structural novelty; the need to increase the availability and diversity of benchmark datasets; and the relative scarcity of large-scale experimental validation of the designs.

Introduction: Cheminformatics has become a cornerstone of modern drug discovery, offering the ability to efficiently manage and analyze large volumes of chemical and biological data. Publicly available databases such as PubChem, ZINC, ChEMBL, DrugBank, ChemDiv, natural product databases, among others, are essential for accessing diverse chemical structures, biological activities, and pharmacological properties.

Areas covered: This review provides an overview of recent (2024-2025) trends in mining data from PubChem and other representative public databases for virtual screening. It also discusses the integration of experimental validation and computational tools in drug design and cheminformatics workflows. The article is based on literature retrieved from SciFinder.

Expert opinion: Public chemical databases contain thousands to billions of compounds and various computational strategies have necessitated development to navigate this vast chemical space effectively. These include application programming interfaces, similarity searches, physicochemical filtering, and target-based selection. Such filtering strategies have enabled the extraction of focused compound subsets for evaluation through various cheminformatics tools, ultimately supporting informed decision-making in lead discovery and optimization.