搜索结果：Expert opinion on drug discovery

Despite remarkable advances in computational methods, pre-clinical drug discovery continues to grapple with rising timelines and costs. Software, data, and automation are more powerful than ever, and increasingly these technologies are being embraced. However, there is still work to be done to translate this potential into meaningful reductions in cost and time. This perspective discusses the growth in drug discovery capability, exploring modern data infrastructure including cloud-native platforms, active learning, and laboratory automation. It covers emerging technologies such as LLM-based orchestration and emulation. Implementation examples illustrate successes and challenges. AI presents an opportunity to envisage a new approach to drug discovery, but cultural and technological changes are required. The exponential growth in computational drug discovery tools requires solutions that enable researchers to access scalable and robust capabilities more easily. Data generation is usually the slowest and most expensive part of the design cycle; we advocate for a rigorous application of statistical methods focussing on learning efficiency from data over absolute predictive accuracy of models. Automation also plays a critical role in enabling rapid, high-quality data generation. Focussing on modular interoperable automated units with more attractive economics will drive much wider adoption.

GitHub has become essential to AI-driven drug discovery by facilitating code sharing, collaboration, and reproducible workflows. As more tools for screening, modeling, and data-driven decision-making are hosted on GitHub, researchers need clear, rigorous methods to identify, evaluate, and reuse repositories in a scientifically robust manner. In this report, the authors summarize GitHub concepts most relevant to research practice (e.g. repositories, documentation, licensing, releases, testing, and archiving) and propose a practical framework for navigating drug-discovery repositories. The report incorporates a Scopus trend analysis from 2013 to 2024 using a GitHub-focused search with drug discovery keywords, as well as keyword-based counts of GitHub repositories in selected categories to show topic density. GitHub repositories should be recognized as peer-assessable research outputs in AI-driven drug discovery rather than as supplementary material. At a minimum, this means clear documentation of intended use and limitations, a pinned environment or container for reproducibility, an explicit license, and automated testing via continuous integration. In addition, enhanced validation and governance are necessary to bridge exploratory research code with translational and industrial reliability expectations.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive motor neuron loss, with limited therapeutic options. Among the few approved drugs, edaravone, a free radical scavenger developed originally for ischemic stroke, has attracted particular attention for its ability to counteract oxidative stress, a key driver of neurodegeneration. Its amphipathic structure and ability to cross the blood-brain barrier support its potential neuroprotective action. The authors discuss preclinical studies demonstrating edaravone's ability to reduce oxidative damage, preserve mitochondrial function, and modulate neuroinflammatory responses in ALS cellular and animal models. They discuss variations in dosage, timing, and disease models that produced heterogeneous results. In transgenic mice, edaravone may delay symptom onset and modestly extend survival, but these effects are inconsistent and often limited to early disease stages. Clinically, edaravone provides modest benefits in a subset of patients, reflecting the translational gap between preclinical efficacy and clinical relevance. This case highlights broader challenges in ALS drug discovery, including limited model predictivity, methodological variability, and lack of patient stratification. The edaravone experience highlights key lessons for future neuroprotective approaches: the importance of standardized preclinical design, integration of human-based models, early pharmacokinetic validation, and biomarker-driven trials to advance precision neuroprotection in ALS.

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. 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. 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.

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. 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. 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.

Carbapenemase-producing Klebsiella pneumoniae severely limits treatment options by inactivating carbapenem and other β-lactam antibiotics. To support precision drug discovery, this study investigates how structural and dynamic differences among major carbapenemase families shape their interaction with carbapenem drugs. The authors performed an in-silico analysis of five key Klebsiella pneumoniae carbapenemases (KPC, NDM, VIM, IMP, OXA-48) using multiple sequence alignment, homology modeling, molecular docking, and 50-ns molecular dynamics simulations. Sequence identity between variants was low (33.8-48.5%), indicating deep evolutionary divergence. SWISS-MODEL homology models showed high stereochemical quality, supporting reliable active-site interpretation. Docking suggested stronger binding of meropenem and imipenem to KPC and NDM, mediated by conserved catalytic residues in class A (Ser70, Lys73, Glu166) and class B (His120, His122, Asp124). MD simulations indicated more rigid, compact complexes for KPC and NDM, contrasted with higher flexibility in OXA-48. These results reinforce that Klebsiella pneumoniae carbapenemases are not interchangeable targets and that inhibitor design must account for family-specific active-site geometries and dynamics. Integrating such structural insight with future virtual screening and experimental validation could enable variant-tailored inhibitors rather than relying on a single broad-spectrum carbapenemase blocker.

Vision loss in older adults is largely driven by age-related macular degeneration (AMD), characterized by progressive central visual field damage and functional decline. While current options for wet and dry AMD are limited and expensive, drug repurposing represents a promising strategy to accelerate the discovery of effective, accessible treatment by leveraging medications with established safety profiles. Notably, anti-diabetic agents including metformin, sulfonylureas, glucagon-like peptide-1 receptor agonists (GLP-1RAs), and insulin have emerged as modulators of the retinal pigment epithelium (RPE) function, photoreceptors, and retinal vascular integrity. This review highlights the roles of oxidative stress, inflammation, and complement-mediated immune dysregulation in AMD pathogenesis, alongside preclinical data demonstrating metformin's protective effects via AMP-activated protein kinase (AMPK) activation. Population-based studies and meta-analyses further suggest a modest reduction in AMD risk associated with metformin use in both diabetic and non-diabetic cohorts. Additional pharmacological agents include statins, glyburide, L-DOPA, fluoxetine, dimethyl fumarate, and nutraceuticals such as curcumin, melatonin, and N-acetylcysteine. Early AMD prevention through repurposed therapeutics, guided by AI-driven design and systems biology, may enable personalized care via multimodal risk stratification incorporating genetic, metabolomic, and microbiome data. Rigorous, stratified clinical trials integrating bioinformatics and precision medicine are essential to validate the most effective candidates.

β-Carbonic anhydrases (β-CAs) are zinc-dependent metalloenzymes that catalyze the reversible hydration of carbon dioxide to bicarbonate and protons. They are widely distributed in bacteria, where they support pH regulation, inorganic carbon homeostasis, and central metabolism. Unlike humans, which express only α-class carbonic anhydrases, many bacterial pathogens encode β-CAs, highlighting these enzymes as attractive antibacterial targets with reduced risk of host cross-reactivity. This review discusses integrated in silico and in vitro strategies for the discovery and validation of small-molecule inhibitors targeting bacterial β-CAs. Computational approaches - including pharmacophore modeling, molecular docking, molecular dynamics simulations, and machine learning - are increasingly used to prioritize and optimize candidate inhibitors. Experimental validation employs enzymatic activity assays, biophysical binding techniques, and whole-cell assays to assess target engagement and antibacterial effects. Current inhibitor classes include sulfonamides, coumarins, dithiocarbamates, phenolic compounds, and natural products, with selected chemotypes demonstrating antibacterial or antivirulence activity in specific models. Relevant literature was identified through searches of PubMed, Web of Science, and Scopus, focusing on studies published between approximately 2000 and 2025. β-CAs represent a tractable yet underexploited antibacterial target class. Successful translation will depend on improving bacterial penetration, pharmacokinetics, and target engagement. When strategically positioned as adjunctive or context-dependent therapies, β-CA inhibitors may contribute to the treatment of drug-resistant bacterial infections, including tuberculosis.

The opioid crisis burdens the health care system and poses major research challenges. One approach toward safer analgesics involves targeting opioid receptors in areas of tissue injury/inflammation. Increased proton concentrations have been successfully exploited for developing novel ligands with limited side effects. This review investigates the impact of other inflammatory components (free radicals) on opioid receptors, focusing on redox-sensitive thiols and disulfides. This narrative review is based on a systematic literature search up to 3 March 2025. Of the identified 938 articles, 29 articles met the authors' inclusion criteria. Risk of bias was assessed according to NINDS recommendations, and the review followed PRISMA guidelines. As the included studies indicate that disulfides are an essential structural component, it is conceivable that free radicals affect opioid receptor activity. Current drug design has largely overlooked that function of G-protein coupled receptors (GPCRs) can differ between healthy and pathological microenvironments. The success of pH-dependent opioid ligands illustrates the therapeutic potential of exploiting such conditions. Redox changes may represent another regulatory component, but functional and in vivo evidence is still lacking. The consideration of microenvironmental factors may enable the development of safer, peripherally acting analgesics and refine GPCR-targeted drug design.

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. 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. 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.

Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of severe infections with excess mortality. Progress with traditional antibiotics has been incremental, while resistance, persistence, tolerance, and biofilm formation continue to erode effectiveness. Parallel advances in small-molecule discovery, long-acting lipoglycopeptides, next-generation β-lactams, and non-traditional modalities such as bacteriophage lysins have renewed interest in expanding therapeutic options, though transition from promising preclinical signals to clinical benefit remains challenging. A literature search was conducted using PubMed/MEDLINE, and Embase, for articles published from January 2010 through March 2025. This review synthesizes developments across: (i) agents in key clinical trials for invasive MRSA infection, emphasizing on trial designs, efficacy, and safety considerations; (ii) clinical study data with newer agents for MRSA skin infections and their potential application in invasive disease; (iii) preclinical pipelines including natural products, novel compounds, and other innovative antimicrobial strategies. Among investigated agents, ceftobiprole, ceftaroline, dalbavancin, and exebacase represent promising options for invasive MRSA infections. The pipeline is further strengthened by novel classes and antimicrobial peptides, which show anti-MRSA activity and a low risk for resistance in preclinical models. Continued multidisciplinary collaboration and robust clinical trial infrastructure are essential to translate these advances into improved patient outcomes.

Affinity selection mass spectrometry (AS-MS) is a powerful label-free technique for characterizing macromolecule-ligand interactions that has been used as a hit finding tool with significant success. Recent advances in MS and separation technology have positioned AS-MS to impact more areas of drug discovery. This manuscript provides a brief historical review of AS-MS and the recently developed technologies that have enabled AS-MS. The report also provides examples and references for how AS-MS has been used for high-throughput screening (HTS) to DNA-encoded library (DEL) screening hit confirmation, Direct-to-Biology, and natural product screens. The references for this work were collected from a broad range of sources, including Google Scholar, Scopus, review articles identified via Google Scholar, and the internal AI resource at AbbVie Inc. AS-MS is a unique biophysical binding assay that does not rely on labels and can specifically detect binders from large pools of potential ligands based on molecular weight. There is still significant room for growth in areas of impact that will be driven by decreases in separation time and a move toward equilibrium conditions during separation. Increased use for driving rapid structure-activity relationships (SAR) has potential to decrease project cycle times in lead identification and optimization.

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults, but primary tumor treatment carries a high risk of permanent vision loss and does not adequately prevent metastatic progression. In vitro UM models are needed to accurately represent human disease to support translation of laboratory research to the clinic. This review covers current and emerging in vitro UM models. A PubMed search used keywords 'uveal melanoma' and 'cell line,' 'spheroid,' 'organoid,' 'culture,' or 'in vitro' to identify cell lines, three-dimensional (3D) cultures (spheroids and organoids), and co-culture systems. Model successes and shortcomings are described, considering features that make models more or less representative of in vivo human UM. Insights are provided for consideration when selecting UM models for novel drug discovery. While traditional cell lines have provided an important foundation for UM research, emerging spheroid and patient-derived organoid models may more accurately represent in vivo tumor behavior and the tumor microenvironment. Pairing these 3D models with co-culture techniques could dramatically improve the representativeness of UM models. Researchers should consider testing promising therapeutics on a panel of models representing different UM subtypes, with particular attention to high-risk UM, such as those with BAP1 loss.

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. 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. 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.

Deep generative models are reshaping de novo drug design by enabling creation of novel, property-optimized molecules beyond traditional chemical libraries. Advances in deep learning, molecular representation learning, and structure-aware modeling now enable algorithms to propose molecules that satisfy complex pharmacological constraints, accelerating hit identification. This review outlines recent advances in generative molecular design, including neural network-based frameworks, reinforcement learning systems, diffusion models, and language model-based transformers. The authors outline how each class generates and optimizes molecular structures and review generative AI's practical applications in drug discovery, illustrating translational progress. Current bottlenecks are critically analyzed alongside emerging solutions. This review is based on a systematic literature search conducted in Google Scholar and PubMed, covering studies published up to December 2025. Generative AI's greatest promise lies not in generating more molecules, but in generating better hypotheses, structures that are synthetically accessible, biologically plausible, optimized across potency, selectivity, and pharmacokinetics. The next phase will be led by multimodal foundation models capable of reasoning jointly about chemistry, protein structure, and cellular response, supported by automated synthesis and high-throughput experimentation. As these components are integrated, generative molecular design will guide lead optimization and reshape how new therapies are discovered and developed.

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. 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. 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.

Prion diseases comprise a heterogeneous group of rare and fatal neurodegenerative disorders characterized by self-propagating misfolding of the cellular prion protein. Although no therapy has yet proven capable of halting disease progression, several promising approaches are beginning to shift the field from repeated disappointment toward cautious but genuine translational optimism. This review examines emerging therapeutic approaches targeting key nodes of prion biology, including prion protein-targeting strategies and interventions directed at cellular pathways involved in disease pathogenesis. The authors further discuss the potential and challenges associated with polypharmacology, such as drug combinations and multi-target-directed ligands, which aim to address the biological complexity of prion disease. The persistent limitations of single-target therapies for human prion disease emphasize the need to better align therapeutic strategies with disease stage, biological heterogeneity, and network-level pathogenesis. Achieving meaningful therapeutic impact will require an integrated strategy that brings together earlier intervention, improved patient stratification, and rational use of combination and multi-target approaches, supported by advances in biomarkers and experimental modeling.

Efficient structure-based design requires robust protocols for generating protein-ligand complex structures to support iterative chemical optimization. However, developing reliable crystallization conditions suitable for drug discovery remains challenging, especially for novel targets and when working with diverse ligand classes. The review focuses on establishing robust crystallization workflows and providing solutions when standard methods prove inadequate for obtaining protein-ligand crystal structures. In addition to reviewing the literature for generic technical advances, the authors provide a comprehensive overview of project- and protein-specific approaches. To further substantiate their claims, the authors analyzed metadata from their proprietary structure collection, representing 20 years of crystallography supporting structure-based drug design. The authors provide two detailed examples showcasing rescue strategies in action. Crystal structures will remain fundamental to structure-based drug design moving forward. Successful crystallization demands adaptable, multi-faceted strategies that systematically explore diverse protein variants and crystallization conditions. Future progress depends on integrating AI tools for construct design with project insights and robust experimental workflows. Success ultimately hinges on synergy between innovative problem-solving approaches and deep expertise in navigating this rapidly evolving landscape.

Current immunosuppressive regimens in solid organ transplantation (SOT) have markedly improved short-term graft survival. However, the adverse effects of these drugs, together with incomplete control of rejection, underscore the need for new, more selective therapies. In silico approaches offer a useful and underexploited avenue in SOT. This review summarizes in silico strategies used to discover drugs for the prevention of allograft rejection. The authors discuss the identification of therapeutic targets using omics data, followed by computational drug repositioning approaches and the main principles of computer-aided drug design (CADD). They also highlight applications to immune targets that are relevant to transplantation. Finally, the authors examine emerging advances in quantitative systems pharmacology (QSP) and virtual clinical trials and their potential use in SOT. The literature was identified through searches of PubMed and Scopus for articles published between 2015 and 2025, from the last 10 years in SOT and, when necessary, on work in related immune-mediated diseases. In silico approaches already offer a framework to prioritize safer and more selective immunomodulators in SOT. However, integration across omics-based target discovery, CADD, computational repurposing and QSP is still uncommon in this field. Building multidisciplinary consortia and adopting more standardized analytical workflows will be essential to unlock their full translational potential.