Drug development, genome analysis and proteogenomics pathway analysis for the future precision medicine and precision health.
Graphen Personal Whole Genome Analytics System analyzes one's entire 6.4B genome with one single test. It provides risk likehood of 350+ diseases in 10 major categories and can provide life long updates as new relevant researches become available over time.
Graphen's Gene Dynamic Detection provides one's current status on 100+ diseases in the protein pathway. It uses genetic tracking to understand the trend of one's genetic performance and grasp the safe distance between you and the disease. It assesses specific disease risks, design precise gene probes and provide precise prevention and health risk managment strategies.
Graphen Atom presents a great opportunity to the biochemical and pharmaceutical industries by offering efficient access and understanding of vast amounts of chemical data, potentially improving the processes and outcomes of drug development.
Graphen has been working with major pharmaceutical companies for years to create novel AI and machine learning methods, pioneering the use of machine learning techniques such as using neural networks to help establish new molecules to be considered, better understand the biological effects of existing molecules.
Graphen contributes to the worldwide community in the fight against the COVID-19 coronavirus by providing our expertise in genome analysis. Graphen analyzed the variants of each reported whole genome sequencing from more than 100 countries and regions to-date, and identified the mutations as they spread.
To help understand large volumes of complex and lengthy medical articles, Graphen invented two important mechanisms: Biomedical Literature Question-Answering Methodology and Citations Methodology. Utilizing AI technologies to read and understand tens of thousands of medical articles and reports efficiently.
Together, they are paving a way towards future precision medicine and precision health.
Graphen Medical approaches health care with the individual variability in genes, environment and lifestyle in mind.
Built upon the next-gen Ardi AI Platform, Atom Medical utilizes Graph Database and Analytics, Auto Model Optimizer and Autonomous Learning in analysis, prediction, treatment and prevention.
Do you want to know more about yourself? What does your blueprint say about your health and wellbeing? Graphen Personal Whole Genome Analytics System analyzes your entire 6.4B genome. It provides your risk likehood of 350+ diseases in 10 major categories.
Graphen Proteome Analytics System provides you with your current status of wellbeing on 100+ diseases in the protein pathways. It gives you a graphical representation on what your protein variations have been impacting your body.
Combining the genomic, proteomic, and environmental analysis, in conjunction with personal Electronic Medical Record, Graphen Atom helps you find what may come next, based on the pathway analysis. Let our analytic results help you towards a healthy life.
Graphen has been working with major pharmaceutical companies for years to create novel AI and machine learning methods, pioneering the use of machine learning techniques such as using neural networks to help establish new molecules to be considered, better understand the biological effects of existing molecules. Graphen uses techniques like active learning to guide the search, selection, and refinement of these molecules.
Graphen has four stages to develop drugs:
Our drug development solution outperforms the best-known competitors in all tools except the protein structure prediction tool, which Graphen is outperformed by Google DeepMind.
Using the newest drug database to find out suitable drug candidates and predict affinity (Ki/Kd/Ka) for druggable target.
Using the newest Drug Database with multi-objective to generate suitable drug candidates from Affinity Score (Ki/Kd/Ka), QED score and Drug solubility Score.
Using Graphen affinity and contacting prediction to find out effective antibody structure, end to end predict structure and affinity.
Using Graphen amino acid substitution and restructure force filed model to check the developability of antibody structure and humanization
Optimize vaccine by variant information, structure energy and contact information, analyzing vaccine protein immunogenicity and the ability of immune escape from antibody or immune cell.
Selection highest immunogenicity epitopes form oncoprotein by TCR interaction, analyzing mutated protein epitope structures and the ability of multi epitope from TCR interaction.