Expert Opinion on Drug Discovery最新文献

Introduction: High failure rates in clinical drug development are often attributed to inadequate therapeutic efficacy. While binding affinity has traditionally guided lead optimization, it reflects only the equilibrium state of drug-target interactions and often correlates poorly with in vivo pharmacological responses. This limitation has prompted growing interest in kinetic parameters that more accurately capture the dynamic nature of drug-target interactions.

Areas covered: This review focuses on drug-target residence time (τ), defined as the reciprocal of the ligand dissociation rate constant (k_off), which has emerged as a crucial determinant of drug efficacy. The authors discuss the impact of residence time on pharmacological outcomes, summarize factors influencing residence time, and outline experimental and computational approaches for its evaluation. This review is based on literature searches conducted using PubMed and Web of Science to identify articles published between the 2000 to 2025.

Expert opinion: Integrating residence time and traditional binding affinity provides a more comprehensive framework for understanding drug-target interactions and guiding rational drug design. Optimizing residence time can enhance pharmacodynamic efficacy, improve target selectivity, and enhance safety. Accordingly, residence time is emerging as a key kinetic parameter in modern drug discovery.

Introduction: Fragment-based drug discovery (FBDD) employs the Grow-Merge-Link (GML) model to identify therapeutic compounds through a combination of experimental and computational techniques. Generative models facilitate compound design, predict interactions, and enhance chemical diversity.

Areas covered: This perspective highlights recent FBDD developments, especially in silico methods where AI-ML accelerates discovery. Reinforcement learning optimizes properties, saving resources for targets like kinases and GPCRs. Generative chemistry enables de novo design, boosting diversity and IP, with pocket-aware design ensuring relevance and ADMET evaluation. Advances in VAEs and reinforcement learning speed up analogue creation and SAR by catalogue.

Expert opinion: AI is transforming FBDD by automating compound design, predicting fragment-protein interactions, and expanding chemical diversity through deep learning, generative models, and reinforcement learning. These tools accelerate hit-to-lead processes, improve drug properties, and support multi-objective optimisation. AI enables fragment generation, pocket-specific design, and large-scale virtual screening, aiding the targeting of challenging proteins and modalities such as PROTACs and molecular glues. Larger fragment libraries enhance model training; although experimental validation remains key, AI reduces development time, improves accuracy, and broadens FBDD's scope.

Introduction: Deep learning is reshaping stroke research by accelerating drug repurposing amid heterogeneous pathology, narrow therapeutic windows, and poor translation. This review highlights current therapeutic challenges and emerging DL applications from preclinical modeling to clinical decision support.

Area covered: This narrative review focuses on the application of DL in preclinical and clinical stroke research, with particular emphasis on their roles in drug discovery and repurposing, as well as the current limitations of these approaches. PubMed was searched for peer-reviewed studies using keywords related to drug repurposing, stroke, and computational approaches published between 2020 and 2025.

Expert opinion: Given the global burden of stroke and limited therapeutic options, DL offers a timely solution by enabling accelerated drug repurposing and efficient drug development. Its ability to analyze high-dimensional data contributes to target identification, virtual screening, and drug repurposing that bridges translational gaps in stroke research. The approval of multiple AI-based diagnostic tools by regulatory bodies like the US FDA reflects growing clinical adoption. However, challenges remain in model interpretability, generalizability, and real-world validation.

Introduction: AI has tremendous potential to reduce time and costs taken to discover and develop new medical entities. As technology evolves, it is essential to learn from successes and failures to realign expectations for scientists, stakeholders and investors.

Areas covered: The authors discuss the challenges associated with the traditional reductionist approach to drug discovery which relies on incomplete data for target validation and, specifically for small molecules, the expanse of chemical space providing potential candidates. The promise of AI is illustrated by both early success and failure stories. Lessons learned are provided at levels of realism, adoption and integration of AI within current Research and Development (R&D) organizational structures.

Expert opinion: The first decade of AI adoption in Big BioPharma has been characterized by genuine breakthroughs and sobering realities. While AI has delivered notable accelerations in hit identification and early-stage design, it has yet to fundamentally alter the success rates of late-stage clinical trials. The industry has learned that AI is neither a silver bullet nor a passing fad, though a critical and evolving component of modern R&D. By consolidating lessons from early adoption, the next decade may see AI truly shift the innovation frontier in global pharmaceutical discovery.

Introduction: Hit identification is a pivotal yet resource-intensive stage of early drug discovery, where large chemical libraries are screened to uncover compounds with target-specific activity. Traditional fluorescence- and luminescence-based high-throughput assays, while fast and automation-friendly, suffer from label-associated artifacts, limited biological relevance, and signal interference that can compromise data fidelity. These challenges, coupled with the growing scale of screening campaigns, have intensified the need for more robust and physiologically relevant label-free screening strategies.

Areas covered: This review highlights the emergence of label-free detection technologies as powerful alternatives for hit identification. By enabling direct measurement of biomolecular interactions or cellular responses without secondary reporters, these modalities reduce false positives, improve assay reliability, and enhance mechanistic insight. The authors also summarize their operating principles, recent applications, and practical considerations, emphasizing how label-free approaches can strengthen screening accuracy and accelerate early drug discovery.

Expert opinion: Label-free assays have rapidly advanced, offering real-time measurements, improved physiological relevance, and expanding throughput for early drug discovery. While these methods reduce artifacts and broaden target compatibility, challenges remain in validating biological relevance and managing complex kinetic data. Recent software innovations, including automated kinetic modeling and high-throughput data pipelines, are accelerating analysis and enhancing scalability.

Introduction: Small open reading frame-encoded peptides (SEPs) are short peptides translated from small open reading frames (sORFs) that were previously overlooked in genome annotations. SEPs have relatively small molecular sizes, fewer than 100 amino acids, some SEPs can be as short as a dozen amino acids. Recent studies have revealed their widespread presence across plants, animals, and microorganisms, as well as their diverse biological functions and potential applications.

Areas covered: This review introduces the characteristics and biogenesis of SEPs, the processes and methods for their identification and validation, and their functional roles and target sites, highlighting the significant potential of SEPs in biological research and therapeutic development. Relevant literature was identified on PubMed (2010-2025) by searching for 'SEP,' 'sORF,' and 'Microprotein.'

Expert opinion: The revolutionary advances in high-throughput omics technologies - particularly mass spectrometry and ribosome profiling - combined with computational prediction methods such as machine learning, have enabled the discovery of an increasing number of SEPs. Research on SEP is currently in a phase of rapid development, and this suggests that the field of peptide drugs may gain many promising molecular candidate.

Introduction: Ebolavirus (EBOV) is a highly pathogenic member of the Filoviridae family, causing severe hemorrhagic fever with high mortality rates and is persistent threat to global health with recurrent outbreaks. Although advances, including vaccine and monoclonal antibody therapeutic development, have improved survival chances, challenges such as cost and availability remain as well as potential viral evolution. Consequently, there is an urgent need for alternative, cost-effective and scalable therapeutics, which has driven interest in computational drug discovery as rapid response strategies.

Area covered: This review synthesizes structural and functional insights into EBOV's key molecular targets and explores their roles in viral entry, replication, and immune evasion. The authors also discuss host-pathogen interactions as potential therapeutic routes of entry and present the most recent computational advances for hit identification and optimization. There is also coverage given to case studies highlighting successful in silico discovery efforts. Literature identified via PubMed, Scopus, and Web of Science were utilized to compose this manuscript with the authors focusing on on recently published studies, while also utilizing their own experience and insight.

Expert opinion: While promising, translation of Ebola viral research remains limited by incomplete structural coverage, small and imbalanced datasets, and a gap between computational prediction and experimental validation. Hybrid in silico workflows are embedded within standardized, data-driven, and experimentally anchored pipelines, and are essential to bridge this gap. By uniting computational precision with laboratory validation, integrative strategies can accelerate the development of robust, deployable EBOV therapeutics and serve as a model for pandemic preparedness against other high-risk pathogens.

Introduction: Fluorinated amino acids are at the focal point of two key current strategic areas within drug discovery being both important for the design/development of new small molecule drugs as well as having the potential to be exploited in the rapidly expanding area of peptide-based therapeutics. While the challenges of developing therapeutic peptides are relatively well understood and strategies have been developed to overcome these, the utilization of fluorinated amino acids for this purpose appears to be relatively sparse, particularly in progressing peptide derivatives into clinical development.

Areas covered: This review discusses the applications of fluorinated amino acid (FAA) derivatives in modern drug discovery with a specific focus on their incorporation into therapeutic peptide derivatives, based on the currently available literature.

Expert opinion: While only one naturally occurring and fluorinated amino acid (FAA) is known, the use of this class of compounds is prevalent in drug discovery campaigns. Synthetic advances have allowed ready access to a diverse range of compounds featuring this structural motif, and the bifunctional modularity of the amino and carboxylic acid moiety typically allows their ready incorporation into novel molecular entities through well-precedented chemistries. However, caution should be exercised when FAAs are utilized in solid-phase peptide synthesis (SPPS) as the electronegativity of the fluorine substation often mitigates the reactivity of the system leading to poor yields. Future efforts should look toward developing a more reliable understanding of the impact of these changes to enable a robust platform for therapeutic peptides to be designed based on the incorporation of fluorinated amino acids.

Introduction: Structural biology has become a cornerstone of modern drug discovery, enabling atomic-level insights into protein - ligand interactions and guiding rational therapeutic design. As the field evolves, it faces growing demands for accuracy, reproducibility, and integration with computational and pharmacological data.

Areas covered: This article explores the impact of sample heterogeneity and radiation damage on macromolecular crystallography, emphasizing how these factors can compromise structural integrity. It reviews current strategies for mitigating crystal damage, including optimized cooling, dose-aware data collection, and emerging technologies such as serial crystallography and advanced detectors. The manuscript also discusses the limitations of existing validation tools and the need for improved metadata reporting to ensure reliable structural models. Cryo-electron tomography is highlighted as a promising technique for studying drug - target interactions in native cellular environments, offering complementary insights to traditional crystallographic methods.

Expert opinion: To advance drug discovery, the structural biology community must adopt unified standards for data validation and experimental documentation. High-quality, reproducible structures are essential for minimizing artifacts and supporting AI-driven modeling and screening. A coordinated effort to integrate damage-aware practices and metadata standards will enhance the fidelity of structural data and its utility in therapeutic innovation.

Introduction: Dengue virus (DENV) remains a significant global health threat, causing millions of infections and substantial morbidity each year. The viral E protein is a key target for neutralizing antibodies and vaccine design; however, antibody-dependent enhancement (ADE) complicates safe immunization. Currently licensed vaccines offer only partial and serotype-dependent protection, highlighting the urgent necessity for additional therapeutic strategies.

Areas covered: This review summarizes advances in direct-acting antivirals (DAAs) targeting the E protein and host-directed therapies (HDTs). It discusses DAAs that block E-mediated viral entry and HDTs that modulate lipid metabolism, nucleotide biosynthesis, protein folding, translation, and immune pathways. Promising preclinical candidates and experimental DAAs-HDTs combinations with synergistic activity are highlighted, alongside persistent challenges related to viral diversity, ADE risk, host toxicity, and translational limitations. Relevant literature was identified through PubMed, Scopus, Web of Science, ClinicalTrials.gov, and WHO databases, including recent and foundational studies up to September 2025.

Expert opinion: Combining DAAs with HDTs represents a rational strtategy to enhance antiviral efficacy, broaden serotype coverage, reduce resistence, and pontentially limit toxicity. Although clinical evidence remains limited, expandend preclinical and clinical evaluation of these approaches, incorporating serotype-specific testing and sex-based analyses will be essential for advancing effective dengue therapeutics.