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Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as an anti-fibrotic target using a predictive artificial intelligence (AI) approach. Using AI-driven methodology, we generated INS018_055, a small-molecule TNIK inhibitor, which exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects in addition to its anti-fibrotic profile, validated in multiple in vivo studies. Its safety and tolerability as well as pharmacokinetics were validated in a randomized, double-blinded, placebo-controlled phase I clinical trial (NCT05154240) involving 78 healthy participants. A separate phase I trial in China, CTR20221542, also demonstrated comparable safety and pharmacokinetic profiles. This work was completed in roughly 18 months from target discovery to preclinical candidate nomination and demonstrates the capabilities of our generative AI-driven drug-discovery pipeline. An AI-generated small-molecule inhibitor treats fibrosis in vivo and in phase I clinical trials.

PKMYT1 has recently emerged as a compelling therapeutic target for precision cancer therapy due to its synthetic lethality with oncogenic alterations such as CCNE1 amplification and mutations in FBXW7 and PPP2R1A . Current small molecule PKMYT1 inhibitors face limitations, such as insufficient molecular diversity and poor selectivity. We herein use our generative AI platform to develop a bifunctional PKMYT1 degrader by linking an entirely novel PKMYT1 inhibitor to an optimized cereblon (CRBN) binder. The lead PROTAC D16-M1P2 demonstrates dual mechanisms of PKMYT1 degradation and inhibition, with strong antiproliferative potency facilitated by high selectivity. It also exhibits favorable oral bioavailability, stronger pharmacodynamic effects relative to the PKMYT1 inhibitor alone, and robust antitumor response as a monotherapy in xenograft models. This PROTAC serves as a precise chemical probe to explore PKMYT1 biology and a promising lead for further cancer therapy exploration. Using generative AI, Insilico Medicine developed an oral PROTAC that potently inhibits and degrades PKMYT1, a synthetically lethal target in cancer, demonstrating high selectivity and strong antitumor efficacy in preclinical models.

Cardiotoxicity, particularly drug-induced arrhythmias, poses a significant challenge in drug development, highlighting the importance of early-stage prediction of human ether-a-go-go-related gene (hERG) toxicity. hERG encodes the pore-forming subunit of the cardiac potassium channel. Traditional methods are both costly and time-intensive, necessitating the development of computational approaches. In this study, we introduce AttenhERG, a novel graph neural network framework designed to predict hERG channel blockers reliably and interpretably. AttenhERG demonstrates improved performance compared to existing methods with an AUROC of 0.835, showcasing its efficacy in accurately predicting hERG activity across diverse datasets. Additionally, uncertainty evaluation analysis reveals the model's reliability, enhancing its utility in drug discovery and safety assessment. Case studies illustrate the practical application of AttenhERG in optimizing compounds for hERG toxicity, highlighting its potential in rational drug design. Scientific contribution AttenhERG is a breakthrough framework that significantly improves the interpretability and accuracy of predicting hERG channel blockers. By integrating uncertainty estimation, AttenhERG demonstrates superior reliability compared to benchmark models. Two case studies, involving APH1A and NMT1 inhibitors, further emphasize AttenhERG's practical application in compound optimization.

Human coronaviruses (CoV) cause respiratory infections that range from mild to severe. CoVs are a large family of viruses with considerable genetic heterogeneity and a multitude of viral types, making preventing and treating these viruses difficult. Comprehensive treatments that inhibit CoV infections fulfill a pressing medical need and may be immensely valuable in managing emerging and endemic CoV infections. As the main protease (M pro ) is highly conserved across many CoVs, this protease has been identified as a route for broad CoV inhibition. We utilize the advanced generative chemistry platform Chemistry42 for de novo molecular design and obtained novel small-molecule, non-peptide-like inhibitors targeting the SARS-CoV-2 M pro . ISM3312 is identified as an irreversible, covalent M pro inhibitor from extensive virtual screening and structure-based optimization efforts. ISM3312 exhibits low off-target risk and outstanding antiviral activity against multiple human coronaviruses, including SARS-CoV-2, MERS-CoV, 229E, OC43, NL63, and HKU1 independent of P-glycoprotein (P-gp) inhibition. Furthermore, ISM3312 shows significant inhibitory effects against Nirmatrelvir-resistant M pro mutants, suggesting ISM3312 may contribute to reduced viral escape in these settings. Incorporating ISM3312 and Nirmatrelvir into antiviral strategy could improve preparedness and reinforce defenses against future coronavirus threats. A novel covalent inhibitor, ISM3312, targets the main protease of multiple human coronaviruses, including drug-resistant strains, and shows broad antiviral activity. It offers a promising therapeutic strategy against current and future coronavirus threats.

Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understanding of the molecular processes underlying tumorigenesis and significant intra- and inter-tumor heterogeneity. Identification of novel molecular biomarkers stratifying cancer patients with different survival outcomes may provide new opportunities for target discovery and subsequent development of tailored therapies. Here, we applied the artificial intelligence-driven PandaOmics platform ( https://pandaomics.com/ ) to explore gene expression changes in rare DNA repair-deficient disorders and identify novel cancer targets. Our analysis revealed that CEP135, a scaffolding protein associated with early centriole biogenesis, is commonly downregulated in DNA repair diseases with high cancer predisposition. Further screening of survival data in 33 cancers available at TCGA database identified sarcoma as a cancer type where lower survival was significantly associated with high CEP135 expression. Stratification of cancer patients based on CEP135 expression enabled us to examine therapeutic targets that could be used for the improvement of existing therapies against sarcoma. The latter was based on application of the PandaOmics target-ID algorithm coupled with in vitro studies that revealed polo-like kinase 1 (PLK1) as a potential therapeutic candidate in sarcoma patients with high CEP135 levels and poor survival. While further target validation is required, this study demonstrated the potential of in silico-based studies for a rapid biomarker discovery and target characterization.

Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in December 2019 in Wuhan, China. It was quickly established that both the symptoms and the disease severity may vary from one case to another and several strains of SARS-CoV-2 have been identified. To gain a better understanding of the wide variety of SARS-CoV-2 strains and their associated symptoms, thousands of SARS-CoV-2 genomes have been sequenced in dozens of countries. In this article, we introduce COVIDomic, a multi-omics online platform designed to facilitate the analysis and interpretation of the large amount of health data collected from patients with COVID-19. The COVIDomic platform provides a comprehensive set of bioinformatic tools for the multi-modal metatranscriptomic data analysis of COVID-19 patients to determine the origin of the coronavirus strain and the expected severity of the disease. An integrative analytical workflow, which includes microbial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification, allows to analyze the presence of the most common microbial organisms, their antibiotic resistance, the severity of the infection and the set of the most probable geographical locations from which the studied strain could have originated. The online platform integrates a user friendly interface which allows easy visualization of the results. We envision this tool will not only have immediate implications for management of the ongoing COVID-19 pandemic, but will also improve our readiness to respond to other infectious outbreaks.

The exploration and mapping of chemical space remain a central challenge in modern drug discovery. Traditional compound libraries and databases cover only a minute fraction of this space, limiting the discovery of novel, bioactive, and patentable chemotypes. Here, we present a unique dataset containing approximately 110 M molecular structures of potential NLRP3 inhibitors enabled by the LEGION ( Latent Enumeration, Generation, Integration, Optimization, and Navigation ) workflow, which integrates generative AI, AI-guided screening within the Chemistry42 platform and auxiliary cheminformatics tools to enable large-scale exploration of chemical space around specific drug targets. Using the structural data of NLRP3 co-crystals, a clinically relevant target, LEGION combined ligand- and structure-based design strategies, in-house algorithms for 3D pharmacophore-aware scaffold extraction, and distinct library enumeration methods to identify over 34,000 unique scaffolds, which can be multiplied into a dataset of 123B molecular structures within the provided code. The resulting dataset of unprecedented size proved effective for scaffold hopping, chemical space navigation, and supporting intellectual property applications by generating structurally diverse and synthetically accessible structures.

As aging and tumorigenesis are tightly interconnected biological processes, targeting their common underlying driving pathways may induce dual‐purpose anti‐aging and anti‐cancer effects. Our transcriptomic analyses of 16,740 healthy samples demonstrated tissue‐specific age‐associated gene expression, with most tumor suppressor genes downregulated during aging. Furthermore, a large‐scale pan‐cancer analysis of 11 solid tumor types (11,303 cases and 4431 control samples) revealed that many cellular processes, such as protein localization, DNA replication, DNA repair, cell cycle, and RNA metabolism, were upregulated in cancer but downregulated in healthy aging tissues, whereas pathways regulating cellular senescence were upregulated in both aging and cancer. Common cancer targets were identified by the AI‐driven target discovery platform—PandaOmics. Age‐associated cancer targets were selected and further classified into four groups based on their reported roles in lifespan. Among the 51 identified age‐associated cancer targets with anti‐aging experimental evidence, 22 were proposed as dual‐purpose targets for anti‐aging and anti‐cancer treatment with the same therapeutic direction. Among age‐associated cancer targets without known lifespan‐regulating activity, 23 genes were selected based on predicted dual‐purpose properties. Knockdown of histone demethylase KDM1A, one of these unexplored candidates, significantly extended lifespan in Caenorhabditis elegans. Given KDM1A's anti‐cancer activities reported in both preclinical and clinical studies, our findings propose KDM1A as a promising dual‐purpose target. This is the first study utilizing an innovative AI‐driven approach to identify dual‐purpose target candidates for anti‐aging and anti‐cancer treatment, supporting the value of AI‐assisted target identification for drug discovery. This study identified common dysregulated genes and pathways in aging and cancer, revealing tissue‐specific age‐associated gene expression and cellular processes. Using an AI‐driven approach, we identified multiple novel dual‐purpose targets for cancer and aging treatment. Notably, we highlighted KDM1A as a promising dual‐purpose target, with our findings demonstrating lifespan extension in C. elegans and opening new avenues for therapeutic advancements.

Synthetic data generation in omics mimics real-world biological data, providing alternatives for training and evaluation of genomic analysis tools, controlling differential expression, and exploring data architecture. We previously developed Precious1GPT, a multimodal transformer trained on transcriptomic and methylation data, along with metadata, for predicting biological age and identifying dual-purpose therapeutic targets potentially implicated in aging and age-associated diseases. In this study, we introduce Precious2GPT, a multimodal architecture that integrates Conditional Diffusion (CDiffusion) and decoder-only Multi-omics Pretrained Transformer (MoPT) models trained on gene expression and DNA methylation data. Precious2GPT excels in synthetic data generation, outperforming Conditional Generative Adversarial Networks (CGANs), CDiffusion, and MoPT. We demonstrate that Precious2GPT is capable of generating representative synthetic data that captures tissue- and age-specific information from real transcriptomics and methylomics data. Notably, Precious2GPT surpasses other models in age prediction accuracy using the generated data, and it can generate data beyond 120 years of age. Furthermore, we showcase the potential of using this model in identifying gene signatures and potential therapeutic targets in a colorectal cancer case study.