Trends in pharmacological sciences · 2023
Frank W Pun、Ivan V Ozerov、Alex Zhavoronkov
Disease modeling and target identification are the most crucial initial steps in drug discovery, and influence the probability of success at every step of drug development. Traditional target identification is a time-consuming process that takes years to decades and usually starts in an academic setting. Given its advantages of analyzing large datasets and intricate biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We review recent advances in target discovery, focusing on breakthroughs in AI-driven therapeutic target exploration. We also discuss the importance of striking a balance between novelty and confidence in target selection. An increasing number of AI-identified targets are being validated through experiments and several AI-derived drugs are entering clinical trials; we highlight current limitations and potential pathways for moving forward.
Nature · 2026
N. Savage、Shan Grewal、M. Shaikh 等
Glioblastoma is a lethal brain tumour for which current multimodal treatment rarely prevents recurrence1. Therapeutic failure is driven by extensive intratumoural cellular heterogeneity2 with a microenvironment dominated by tumour-associated macrophages that sustain tumour growth and immunosuppression3. Although chimeric antigen receptor (CAR)-T cell therapies are being developed for glioblastoma, sustained response has been undermined by non-uniform antigen expression, antigen loss and microenvironmental barriers that are not directly engaged by tumour-targeting designs4. These limitations motivate new strategies that address the disease as a coupled tumour–immune system rather than a single malignant compartment. Here we use a multi-omic target discovery platform to identify GPNMB as a dual-compartment antigen in glioblastoma. Anti-GPNMB CAR-T cells showed potent anti-tumour activity, with long-term disease control in orthotopic patient-derived xenografts and syngeneic glioma models through concomitant depletion of GPNMB+ tumour and immunosuppressive myeloid populations. By collapsing tumour control and microenvironmental reprogramming, these findings provide a new strategy for antigen selection and targeting in heterogenous, myeloid-rich solid cancers. Integrated multi-omic profiling of glioblastoma reveals GPNMB as a shared antigen in tumour cells and the surrounding microenvironment, and GPNMB-targeted chimeric antigen receptor T cells demonstrate therapeutic activity in vitro and in animal models.
bioRxiv · 2025
E. Shulman、E. Campagnolo、Roshan Lodha 等
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Frontiers in immunology · 2021
Marina Saade、Giovanna Araujo de Souza、Cristoforo Scavone 等
Inflammation is a response to a lesion in the tissue or infection. This process occurs in a specific manner in the central nervous system and is called neuroinflammation, which is involved in neurodegenerative diseases. GPNMB, an endogenous glycoprotein, has been recently related to inflammation and neuroinflammation. GPNMB is highly expressed in macrophages and microglia, which are cells involved with innate immune response in the periphery and the brain, respectively. Some studies have shown increased levels of GPNMB in pro-inflammatory conditions, such as LPS treatment, and in pathological conditions, such as neurodegenerative diseases and cancer. However, the role of GPNMB in inflammation is still not clear. Even though most studies suggest that GPNMB might have an anti-inflammatory role by promoting inflammation resolution, there is evidence that GPNMB could be pro-inflammatory. In this review, we gather and discuss the published evidence regarding this interaction.
bioRxiv · 2026
Dongmin Xun、Lan-Qing Li、Yi Liu 等
Safe and effective gene delivery remains a central challenge for therapeutic applications. While non-viral and viral vectors have enabled substantial progress, their reliance on non-human components often triggers immune responses, limiting their use in chronic treatments. Here, we developed DeepDelivery, an artificial intelligence-driven platform to repurpose human proteins for mRNA delivery. An unbiased screening of the human proteome nominated 512 candidates, with experimental validation confirming that 80% of top-ranked hits form mRNA-encapsulating particles and mediate efficient functional delivery in human cells without provoking detectable inflammation. Notably, multiple tripartite motif (TRIM) family proteins, typically linked to antiviral responses, exhibited strong assembly and delivery activity. Quantitative analysis and interpretation of the model revealed structural domains that govern nanocage formation, enabling domain-guided engineering of TRIM25 variants with enhanced function. Our work establishes a generalizable framework for discovering human-derived delivery vehicles and provides a path toward programmable, non-immunogenic mRNA therapeutics.
Nature · 2026
Yue Xi、Waner Zeng、Jie Luo 等
The prevalence of metabolic-dysfunction-associated steatohepatitis (MASH) is rising globally, yet effective treatments remain limited1. Here we found that systemic or hepatocyte-specific ablation of the gene encoding glycoprotein non-metastatic melanoma protein B (Gpnmb)-a top upregulated gene in MASH-protected mice from diet-induced MASH. Notably, MASH progression was driven specifically by the secreted GPNMB ectodomain (G-ECD), rather than full-length GPNMB. Serum G-ECD levels showed a strong positive correlation with MASH severity in human patients. Using an unbiased screen of a cell-surface-displayed transmembrane protein library, we identified related to receptor tyrosine kinase (RYK) as a functional receptor for G-ECD. Hepatocyte-specific Ryk ablation protected mice against MASH and abolished the pathogenic effects of G-ECD. Mechanistically, G-ECD binding to RYK activated ERK1/2 signaling, resulting in transcriptional activation of PPARγ-CD36 and SREBP1C pathways that promote hepatic lipid uptake and lipogenesis. Multiple therapeutic strategies targeting the GPNMB-RYK axis-including vaccination, short hairpin RNA, neutralizing antibody and N-acetylgalactosamine small interfering RNA-effectively prevented and treated MASH in preclinical models. Our findings identify the GPNMB-RYK axis as a new pathogenic ligand-receptor pathway and a promising therapeutic target for MASH.
Advancement of science · 2026
A. T. Fajar、G. Lambard、Jessie Manopo 等
Molecular passivation is an effective strategy to suppress interfacial defects in perovskite solar cells (PSCs), yet the discovery of new passivation molecules remains limited by empirical design and narrow chemical libraries. Here, for the first time, we present an AI‐driven framework integrating discriminative and generative language models to accelerate the discovery of effective passivators. A SMILES‐X classifier trained on literature data achieved high predictive performance (F1 = 0.80, ROC–AUC = 0.88), while a GPT‐2‐based generative model iteratively produced over 100 000 novel molecules, more than 80% of which were predicted to be effective. Multi‐criteria filtering reduced this pool to ∼8000 high‐quality candidates, from which clustering analysis identified ten diverse representatives. Three molecules, including a surrogate analog, were prioritized for experimental testing, and all exhibited a clear passivation effect. In particular, 4‐maleimidobutyric acid increased the average open‐circuit voltage from 1.08 to 1.12 V and improved the average power conversion efficiency from 19.3% to 22.2%, while markedly reducing hysteresis. This study demonstrates that generative AI can autonomously propose synthetically accessible, functionally effective molecules for PSC passivation, offering a powerful paradigm for accelerated materials discovery beyond conventional chemical space exploration.
Science (New York, N.Y.) · 2022
Maria E Diaz-Ortiz、Yunji Seo、Marijan Posavi 等
Many risk loci for Parkinson's disease (PD) have been identified by genome-wide association studies (GWASs), but target genes and mechanisms remain largely unknown. We linked the GWAS-derived chromosome 7 locus (sentinel single-nucleotide polymorphism rs199347) to GPNMB through colocalization analyses of expression quantitative trait locus and PD risk signals, confirmed by allele-specific expression studies in the human brain. In cells, glycoprotein nonmetastatic melanoma protein B (GPNMB) coimmunoprecipitated and colocalized with α-synuclein (aSyn). In induced pluripotent stem cell-derived neurons, loss of GPNMB resulted in loss of ability to internalize aSyn fibrils and develop aSyn pathology. In 731 PD and 59 control biosamples, GPNMB was elevated in PD plasma, associating with disease severity. Thus, GPNMB represents a PD risk gene with potential for biomarker development and therapeutic targeting.
2026
Chuhan Shi、Zijian Guo、Ze-Lin Zang 等
Gene perturbation analysis plays a critical role in drug discovery by enabling researchers to investigate how interventions on specific genes influence global gene expression patterns within cells. Recent advances in artificial intelligence-driven virtual cell models have made it possible to predict gene expression outcomes for a wide range of perturbation strategies in silico, substantially reducing reliance on costly and time-consuming biological experiments. However, effectively exploring and interpreting the high-dimensional perturbation spaces produced by these models remains challenging because of the combinatorial nature of perturbations and the complex cell-specific gene expression responses they generate. In this work, we present CellPrism, a visual analytics system designed to support the systematic exploration of gene perturbation strategies for drug discovery. Specifically, CellPrism integrates clustering-based overviews to summarize perturbation outcomes, a glyph-based representation to compactly encode gene expression patterns across cell types, and coordinated views that enable fine-grained comparison and interpretation of perturbation effects. We demonstrate the effectiveness of CellPrism through a real-world case study and expert interviews. This work highlights the value of visual analytics in bridging virtual cell modeling with expert-driven decision making in drug discovery.
Drug discovery today · 2024
Amit Gangwal、Antonio Lavecchia
Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.
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