A new study published by Insilico Medicine using artificial intelligence has identified potential FP2 blockers, which could lead to the development of new antimalarial drugs. The research provides insights into the properties of E64 approaches and their interaction with falcipain-2, a key target for malaria treatment.
The special issue brings together contributions from top academics and industry experts, highlighting the use of generative chemistry and GANs for de-novo molecular design. Experimental validation of generated molecules demonstrates high activity and selectivity in a novel inhibitor of Janus Kinase 3.
Researchers at Insilico Medicine developed an Entangled Conditional Adversarial Autoencoder (ECAAE) that generates molecular structures based on various properties. The generated molecule demonstrated high activity and selectivity against a specific protein, laying the foundation for AI-powered drug discovery.
Researchers at Johns Hopkins and Insilico Medicine discovered novel epigenetically silenced genes in ovarian cancer, including methylation of the GULP1 gene. GULP1 expression is associated with late-stage disease and poor overall survival, suggesting its potential as a biomarker.
Researchers developed a deep-learning model to predict biological age of muscles and estimate the importance of genetic and epigenetic factors driving muscle aging. The study identified tissue-specific biomarkers of aging, which can be used to track the effectiveness of interventions.
Insilico Medicine introduces a novel deep neural network architecture called Reinforced Adversarial Neural Computer (RANC) for de novo molecular design. RANC outperforms other methods in generating unique structures and passing medical chemistry filters.
The Adversarial Threshold Neural Computer (ATNC) model, a proof-of-concept, combines Generative Adversarial Networks (GANs) with Reinforcement Learning (RL) to generate novel small organic molecules. The GAN-RL architecture demonstrated the ability to produce valid and unique molecular structures, paving the way for future drug discovery.
Researchers propose roadmap for making humans resistant to radiation and stress damage, with a focus on radioresistance. The strategies aim to maximize productive life years in space, addressing challenges like high-LET radiation.
The researchers developed a new class of bifunctional immunotherapeutic agents called Y-traps, which can target and disable multiple immune suppressive molecules. These Y-traps were found to be effective in inhibiting tumor growth and activating antitumor immunity, even against cancers that do not respond to existing immunotherapies.
A novel deep-learning based hematological human aging clock predicts the biological age of individual patients with high accuracy. The model outperforms chronological age in predicting all-cause mortality, highlighting population-specific patterns of aging.
A new study uses deep learning methods to identify genes involved in embryonic development, fetal transition, and cancer. The research may lead to innovative strategies for induced tissue regeneration and cancer treatment.
Researchers from Insilico Medicine and BitFury Group present a blockchain-based life data interchange system to decentralize and accelerate biomedical research. The system utilizes deep learning technologies to analyze human life data, reducing biases and democratizing healthcare access.
A new model proposes returning control over human life data to patients, accelerating biomedical research through a secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies. This approach can resolve challenges faced by regulators and improve patient satisfaction.
Insilico Medicine showcases its use of artificial intelligence to identify disease targets, generate molecular structures, and track interventions for metabesity. The company aims to prevent metabolic-rooted disorders such as diabetes and dementia.
A new AI model called druGAN enables the generation of novel molecules with desired properties, surpassing previous GAN-based approaches. The model uses reinforcement learning to generate effective molecular graphs, paving the way for improved pharmaceuticals.
Researchers found that SMAD4 loss is associated with cetuximab resistance and induction of MAPK/JNK activation in HPV-negative head and neck squamous cell carcinoma (HNSCC) patients. The study suggests that targeting pro-survival pathways may be a novel strategy for overcoming cetuximab resistance.
Insilico Medicine will showcase its latest AI advancements in aging research and personal health data management. The company aims to apply deep learning to improve biomarker development, predict chronological age, and provide geroprotectors for various diseases.
Scientists have applied iPANDA, a novel approach for analyzing high-throughput transcriptomic datasets, to identify the earliest signaling harbingers of malignant transition in OSCC. The study reveals that dysregulation of these signaling networks occurs even before precancerous cells invade and acquire malignant potential.
Insilico Medicine will showcase its pioneering work in applying deep learning techniques to drug discovery, biomarker development, and aging research. The company's presentation highlights the applications of Generative Adversarial Networks (GANs) in oncology and infectious diseases.
Scientists at InSilico Medicine developed a proof-of-concept AI model using Generative Adversarial Autoencoders (AAEs) to generate molecular fingerprints of cancer-killing molecules. The study demonstrates the potential for AI to accelerate pharmaceutical R&D and improve clinical trial success rates.
Insilico Medicine developed a novel tool, iPANDA, to derive new insights from gene expression repositories. The method combines precalculated gene coexpression data with gene importance factors for obtaining pathway activation scores, producing highly consistent sets of biologically relevant biomarkers.
The article presents a comprehensive overview of in silico drug repurposing methods, highlighting the advantages and disadvantages of various techniques. The authors emphasize the potential of incorporating deep learning approaches into modular workflows, which can accelerate development and reduce costs.
Insilico Medicine will present new research data on geroprotectors, small molecules that mimic the young healthy state in old human tissues. The results are a result of a multi-year research program with multiple in silico predictions made using algorithms validated using data from many age-related diseases.
Researchers utilized Regeneration Intelligence to evaluate signaling pathways in lung and liver fibrosis and glaucoma, identifying potential biomarkers and therapeutic targets. The study suggests that pathway signatures may play a role in aging-related diseases.
Insilico Medicine scientists will present advances in deep learning for biomarker development and drug discovery at the ISFA-Columbia University Actuarial Science Workshop. The workshop aims to integrate deep learning with actuarial science to assess risk in finance, insurance, and other industries.
Scientists from Insilico Medicine used deep neural networks to predict therapeutic use of large numbers of drugs from gene expression data, achieving 54.6% accuracy in class prediction. The study also found that many misclassified drugs had dual use, suggesting potential for drug repurposing.
Insilico Medicine presents research on applying deep learning to biomarker development and cosmetics applications at INNOCOS World Beauty Innovation Summit. The company's app RYNKL evaluates anti-aging interventions using machine learning methods, minimizing animal testing.
InSilico Medicine presents recent advances in applying signaling pathway activation analysis and deep learning to drug discovery and age-related diseases. The company's mission is to extend healthy human longevity through faster and more effective diagnostics and cures.
The paper calls for creating a task force to evaluate the classification of aging as a disease in ICD-11. Classifying aging as a disease could help shift the focus from treatment to prevention, attracting more resources to aging research and business cases for pharmaceutical companies.
A new method for testing human induced pluripotent stem cells (iPSCs) has been developed, allowing for the evaluation of their differentiation potential. This approach uses pathway activation profiling to identify impaired iPSC lines and predict in vitro differentiation capabilities.
A new algorithm developed by InSilico Medicine has the potential to improve the effectiveness of targeted therapy for cancer patients. The algorithm predicts whether a specific drug will work for an individual patient based on activation of intracellular regulatory pathways.
Researchers discovered that photoaging can be reversed by understanding how UV radiation affects skin fibers. Polina Mamoshina's study using Geroscope software platform analyzed pathway dysregulation in chronologically-aged and photoaged skin.
Scientists from InSilico Medicine have developed an approach to screen and rank geroprotective drugs using big data analysis, identifying compounds with potential geroprotective properties. The Geroscope software was applied to gene expression data derived from stem cells to select five drugs that displayed geroprotective action.
A new biomarker, PAS, has been identified to predict cetuximab response in colorectal cancer (CRC) patients with wild-type K-ras. This finding could help physicians make better treatment decisions, improving patient outcomes.
A team of researchers from InSilico Medicine has successfully mapped the molecular pathway for myeloid-derived suppressor cell (MDSC) cancer progression. The study identifies several proliferation and invasion-related pathways that are key to MDSC's immune-suppressive effects, opening up new avenues for therapy targeting these cells.
GeroScopeTM is a system that evaluates age-related changes in human tissue and predicts the efficacy of drugs with known molecular targets. The technology will be presented at the FEBS-EMBO 2014 Conference, allowing delegates to learn about its functionality.