nach0: multimodal natural and chemical languages foundation model.

Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creative solutions. Our paper introduces a new foundation model, nach0, capable of solving various chemical and biological tasks: biomedical question answering, named entity recognition, molecular generation, molecular synthesis, attributes prediction, and others. nach0 is a multi-domain and multi-task encoder-decoder LLM pre-trained on unlabeled text from scientific literature, patents, and molecule strings to incorporate a range of chemical and linguistic knowledge. We employed instruction tuning, where specific task-related instructions are utilized to fine-tune nach0 for the final set of tasks. To train nach0 effectively, we leverage the NeMo framework, enabling efficient parallel optimization of both base and large model versions. Extensive experiments demonstrate that our model outperforms state-of-the-art baselines on single-domain and cross-domain tasks. Furthermore, it can generate high-quality outputs in molecular and textual formats, showcasing its effectiveness in multi-domain setups.

Discovery of a Novel and Potent Cyclin-Dependent Kinase 8/19 (CDK8/19) Inhibitor for the Treatment of Cancer.

The mediator kinases CDK8 and CDK19 control the dynamic transcription of selected genes in response to various signals and have been shown to be hijacked to sustain hyperproliferation by various solid and liquid tumors. CDK8/19 is emerging as a promising anticancer therapeutic target. Here, we report the discovery of compound 12, a novel small molecule CDK8/19 inhibitor. This molecule demonstrated not only decent enzymatic and cellular activities but also remarkable selectivity in CDK and kinome panels. Besides, compound 12 also displayed favorable ADME profiles including low CYP1A2 inhibition, acceptable clearance, and high oral bioavailability in multiple preclinical species. Robust in vivo PD and efficacy studies in mice models further demonstrated its potential use as mono- and combination therapy for the treatment of cancers.

TNIK's emerging role in cancer, metabolism, and age-related diseases.

Traf2- and Nck-interacting kinase (TNIK) has emerged as a key regulator of pathological metabolic signaling in several diseases and is a promising drug target. Originally studied for its role in cell migration and proliferation, TNIK possesses several newly identified functions that drive the pathogenesis of multiple diseases. Specifically, we evaluate TNIK's newfound roles in cancer, metabolic disorders, and neuronal function. We emphasize the implications of TNIK signaling in metabolic signaling and evaluate the translational potential of these discoveries. We also highlight how TNIK's role in many biological processes converges upon several hallmarks of aging. We conclude by discussing the therapeutic landscape of TNIK-targeting drugs and the recent success of clinical trials targeting TNIK.

Acquired resistance to immunotherapy and chemoradiation in MYC amplified head and neck cancer.

The proto-oncogene MYC encodes a nuclear transcription factor that has an important role in a variety of cellular processes, such as cell cycle progression, proliferation, metabolism, adhesion, apoptosis, and therapeutic resistance. MYC amplification is consistently observed in aggressive forms of several solid malignancies and correlates with poor prognosis and distant metastases. While the tumorigenic effects of MYC in patients with head and neck squamous cell carcinoma (HNSCC) are well known, the molecular mechanisms by which the amplification of this gene may confer treatment resistance, especially to immune checkpoint inhibitors, remains under-investigated. Here we present a unique case of a patient with recurrent/metastatic (R/M) HNSCC who, despite initial response to nivolumab-based treatment, developed rapidly progressive metastatic disease after the acquisition of MYC amplification. We conducted comparative transcriptomic analysis of this patient's tumor at baseline and upon progression to interrogate potential molecular processes through which MYC may confer resistance to immunotherapy and/or chemoradiation and used TCGA-HNSC dataset and an institutional cohort to further explore clinicopathologic features and key molecular networks associated with MYC amplification in HNSCC. This study highlights MYC amplification as a potential mechanism of immune checkpoint inhibitor resistance and suggest its use as a predictive biomarker and potential therapeutic target in R/M HNSCC.

Psychogenic Aging: A Novel Prospect to Integrate Psychobiological Hallmarks of Aging.

Psychological factors are amongst the most robust predictors of healthspan and longevity, yet are rarely incorporated into scientific and medical frameworks of aging. The prospect of characterizing and integrating the psychological influences of aging is therefore an unmet step for the advancement of geroscience. Psychogenic Aging research is an emerging branch of biogerontology that aims to address this gap by investigating the impact of psychological factors on human longevity. It is an interdisciplinary field that integrates complex psychological, neurological, and molecular relationships that can be best understood with precision medicine methodologies. This perspective argues that psychogenic aging should be considered an integral component of the Hallmarks of Aging framework, opening the doors for future biopsychosocial integration in longevity research. By providing a unique perspective on frequently overlooked aspects of organismal aging, psychogenic aging offers new insights and targets for anti-aging therapeutics on individual and societal levels that can significantly benefit the scientific and medical communities.

Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an in vitro study.

Introduction: The significance of automated drug design using virtual generative models has steadily grown in recent years. While deep learning-driven solutions have received growing attention, only a few modern AI-assisted generative chemistry platforms have demonstrated the ability to produce valuable structures. At the same time, virtual fragment-based drug design, which was previously less popular due to the high computational costs, has become more attractive with the development of new chemoinformatic techniques and powerful computing technologies. Methods: We developed Quantum-assisted Fragment-based Automated Structure Generator (QFASG), a fully automated algorithm designed to construct ligands for a target protein using a library of molecular fragments. QFASG was applied to generating new structures of CAMKK2 and ATM inhibitors. Results: New low-micromolar inhibitors of CAMKK2 and ATM were designed using the algorithm. Discussion: These findings highlight the algorithm's potential in designing primary hits for further optimization and showcase the capabilities of QFASG as an effective tool in this field.

Synthesis and structure-activity optimization of azepane-containing derivatives as PTPN2/PTPN1 inhibitors.

Protein tyrosine phosphatases PTPN2 and PTPN1 (also known as PTP1B) have been implicated in a number of intracellular signaling pathways of immune cells. The inhibition of PTPN2 and PTPN1 has emerged as an attractive approach to sensitize T cell anti-tumor immunity. Two small molecule inhibitors have been entered the clinic. Here we report the design and development of compound 4, a novel small molecule PTPN2/N1 inhibitor demonstrating nanomolar inhibitory potency, good in vivo oral bioavailability, and robust in vivo antitumor efficacy.

Higher blood biochemistry-based biological age developed by advanced deep learning techniques is associated with frailty in geriatric rehabilitation inpatients: RESORT.

BACKGROUND: Accelerated biological ageing is a major underlying mechanism of frailty development. This study aimed to investigate if the biological age measured by a blood biochemistry-based ageing clock is associated with frailty in geriatric rehabilitation inpatients. METHODS: Within the REStORing health of acutely unwell adulTs (RESORT) cohort, patients' biological age was measured by an ageing clock based on completed data of 30 routine blood test variables measured at rehabilitation admission. The delta of biological age minus chronological age (years) was calculated. Ordinal logistic regression and multinomial logistic regression were performed to evaluate the association of the delta of ages with frailty assessed by the Clinical Frailty Scale. Effect modification of Cumulative Illness Rating Scale (CIRS) score was tested. RESULTS: A total of 1187 geriatric rehabilitation patients were included (median age: 83.4 years, IQR: 77.7-88.5; 57.4 % female). The biochemistry-based biological age was strongly correlated with chronological age (Spearman r = 0.883). After adjustment for age, sex and primary reasons for acute admission, higher biological age (per 1 year higher in delta of ages) was associated with more severe frailty at admission (OR: 1.053, 95 % CI:1.012-1.096) in patients who had a CIRS score of <12 not in patients with a CIRS score >12. The delta of ages was not associated with frailty change from admission to discharge. The specific frailty manifestations as cardiac, hematological, respiratory, renal, and endocrine conditions were associated with higher biological age. CONCLUSION: Higher biological age was associated with severe frailty in geriatric rehabilitation inpatients with less comorbidity burden.

Discovery of macrocyclic CDK2/4/6 inhibitors with improved potency and DMPK properties through a highly efficient macrocyclic drug design platform.

Cyclin-dependent kinases (CDKs) are critical cell cycle regulators that are often overexpressed in tumors, making them promising targets for anti-cancer therapies. Despite substantial advancements in optimizing the selectivity and drug-like properties of CDK inhibitors, safety of multi-target inhibitors remains a significant challenge. Macrocyclization is a promising drug discovery strategy to improve the pharmacological properties of existing compounds. Here we report the development of a macrocyclization platform that enabled the highly efficient discovery of a novel, macrocyclic CDK2/4/6 inhibitor from an acyclic precursor (NUV422). Using dihedral angle scan and structure-based, computer-aided drug design to select an optimal ring-closing site and linker length for the macrocycle, we identified compound 8 as a potent new CDK2/4/6 inhibitor with optimized cellular potency and safety profile compared to NUV422. Our platform leverages both experimentally-solved as well as generative chemistry-derived macrocyclic structures and can be deployed to streamline the design of macrocyclic new drugs from acyclic starting compounds, yielding macrocyclic compounds with enhanced potency and improved drug-like properties.

Discovery of 3-hydroxymethyl-azetidine derivatives as potent polymerase theta inhibitors.

Inhibition of the low fidelity DNA polymerase Theta (Polθ) is emerging as an attractive, synthetic-lethal antitumor strategy in BRCA-deficient tumors. Here we report the AI-enabled development of 3-hydroxymethyl-azetidine derivatives as a novel class of Polθ inhibitors featuring central scaffolding rings. Structure-based drug design first identified A7 as a lead compound, which was further optimized to the more potent derivative B3 and the metabolically stable deuterated compound C1. C1 exhibited significant antiproliferative properties in DNA repair-compromised cells and demonstrated favorable pharmacokinetics, showcasing that 3-hydroxymethyl-azetidine is an effective bio-isostere of pyrrolidin-3-ol and emphasizing the potential of AI in medicinal chemistry for precise molecular modifications.

A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.

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.

PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery.

PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed therapeutic target and biomarker hypotheses with the desired properties and is available through licensing or collaboration. Targets and biomarkers generated by the platform were previously validated in both in vitro and in vivo studies. PandaOmics is a core component of Insilico Medicine's Pharma.ai drug discovery suite, which also includes Chemistry42 for the de novo generation of novel small molecules, and inClinico─a data-driven multimodal platform that forecasts a clinical trial's probability of successful transition from phase 2 to phase 3. In this paper, we demonstrate how the PandaOmics platform can efficiently identify novel molecular targets and biomarkers for various diseases.

Defining the progeria phenome.

Progeroid disorders are a heterogenous group of rare and complex hereditary syndromes presenting with pleiotropic phenotypes associated with normal aging. Due to the large variation in clinical presentation the diseases pose a diagnostic challenge for clinicians which consequently restricts medical research. To accommodate the challenge, we compiled a list of known progeroid syndromes and calculated the mean prevalence of their associated phenotypes, defining what we term the 'progeria phenome'. The data were used to train a support vector machine that is available at https://www.mitodb.com and able to classify progerias based on phenotypes. Furthermore, this allowed us to investigate the correlation of progeroid syndromes and syndromes with various pathogenesis using hierarchical clustering algorithms and disease networks. We detected that ataxia-telangiectasia like disorder 2, spastic paraplegia 49 and Meier-Gorlin syndrome display strong association to progeroid syndromes, thereby implying that the syndromes are previously unrecognized progerias. In conclusion, our study has provided tools to evaluate the likelihood of a syndrome or patient being progeroid. This is a considerable step forward in our understanding of what constitutes a premature aging disorder and how to diagnose them.

Discovery of novel MAT2A inhibitors by an allosteric site-compatible fragment growing approach.

The methionine adenosyltransferase MAT2A catalyzes the synthesis ofthe methyl donor S-adenosylmethionine (SAM) and thereby regulates critical aspects of metabolism and transcription. Aberrant MAT2A function can lead to metabolic and transcriptional reprogramming of cancer cells, and MAT2A has been shown to promote survival of MTAP-deficient tumors, a genetic alteration that occurs in âˆ¼ 13 % of all tumors. Thus, MAT2A holds great promise as a novel anticancer target. Here, we report a novel series of MAT2A inhibitors generated by a fragment growing approach from AZ-28, a low-molecular weight MAT2A inhibitor with promising pre-clinical properties. X-ray co-crystal structure revealed that compound 7 fully occupies the allosteric pocket of MAT2A as a single molecule mimicking MAT2B. By introducing additional backbone interactions and rigidifying the requisite linker extensions, we generated compound 8, which exhibited single digit nanomolar enzymatic and sub-micromolar cellular inhibitory potency for MAT2A.

Skinly: A novel handheld IoT device for validating biophysical skin characteristics.

BACKGROUND: Recent advancements in artificial intelligence have revolutionized dermatological diagnostics. These technologies, particularly machine learning (ML), including deep learning (DL), have shown accuracy equivalent or even superior to human experts in diagnosing skin conditions like melanoma. With the integration of ML, including DL, the development of at home skin analysis devices has become feasible. To this end, we introduced the Skinly system, a handheld device capable of evaluating various personal skin characteristics noninvasively. MATERIALS AND METHODS: Equipped with a moisture sensor and a multi-light-source camera, Skinly can assess age-related skin parameters and specific skin properties. Utilizing state-of-the-art DL, Skinly processed vast amounts of images efficiently. The Skinly system's efficacy was validated both in the lab and at home, comparing its results to established "gold standard" methods. RESULTS: Our findings revealed that the Skinly device can accurately measure age-associated parameters, that is, facial age, skin evenness, and wrinkles. Furthermore, Skinly produced data consistent with established devices for parameters like glossiness, skin tone, redness, and porphyrin levels. A separate study was conducted to evaluate the effects of two moisturizing formulations on skin hydration in laboratory studies with standard instrumentation and at home with Skinly. CONCLUSION: Thanks to its capability for multi-parameter measurements, the Skinly device, combined with its smartphone application, holds the potential to replace more expensive, time-consuming diagnostic tools. Collectively, the Skinly device opens new avenues in dermatological research, offering a reliable, versatile tool for comprehensive skin analysis.

Validation of biomarkers of aging.

The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.

Discovery of Novel and Potent Prolyl Hydroxylase Domain-Containing Protein (PHD) Inhibitors for The Treatment of Anemia.

Stabilization of hypoxia-inducible factor (HIF) by inhibiting prolyl hydroxylase domain enzymes (PHDs) represents a breakthrough in treating anemia associated with chronic kidney disease. Here, we identified a novel scaffold for noncarboxylic PHD inhibitors by utilizing structure-based drug design (SBDD) and generative models. Iterative optimization of potency and solubility resulted in compound 15 which potently inhibits PHD thus stabilizing HIF-α in vitro. X-ray cocrystal structure confirmed the binding model was distinct from previously reported carboxylic acid PHD inhibitors by pushing away the R383 and Y303 residues resulting in a larger inner subpocket. Furthermore, compound 15 demonstrated a favorable in vitro/in vivo absorption, distribution, metabolism, and excretion (ADME) profile, low drug-drug interaction risk, and clean early safety profiling. Functionally, oral administration of compound 15 at 10 mg/kg every day (QD) mitigated anemia in a 5/6 nephrectomy rat disease model.

Discovery of Tetrahydropyrazolopyrazine Derivatives as Potent and Selective MYT1 Inhibitors for the Treatment of Cancer.

Breast and gynecological cancers are among the leading causes of death in women worldwide, illustrating the urgent need for innovative treatment options. We identified MYT1 as a promising new therapeutic target for breast and gynecological cancer using PandaOmics, an AI-driven target discovery platform. The synthetic lethal relationship of MYT1 in tumor cell lines with CCNE1 amplification enhanced this rationale. Through structure-based drug design, we developed a series of novel, potent, and highly selective inhibitors specifically targeting MYT1. Importantly, our lead compound, featuring a tetrahydropyrazolopyrazine ring, exhibits remarkable selectivity over WEE1, a related kinase associated with bone marrow suppression upon inhibition. Optimization of potency and physical properties resulted in the discovery of compound 21, a novel MYT1 inhibitor, exhibiting optimal pharmacokinetic properties and promising in vivo antitumor efficacy.

A comprehensive AI-driven analysis of large-scale omic datasets reveals novel dual-purpose targets for the treatment of cancer and aging.

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.