
AI pioneer Innoplexus and phytopharmaceutical company DrD are partnering toRead More
AI pioneer Innoplexus and phytopharmaceutical company DrD are partnering toRead More
Michelle Hoiseth, Chief Data Officer for Parexel and Dr. GunjanRead More
The cost of developing a new drug roughly doubles everyRead More
In medicine, a Biomarker is a biological indicator, which canRead More
Our AI and Blockchain technologies aim to empower patients over their own data. Hence, we created an ecosystem in which all stakeholders (i.e. CROs, BioTech, Patients) in the entire drug discovery process will/can benefit.
Check out how this unique utility token: Amrit will work!
Via our Apps Curia for Cancer and Neuria for Neurologic diseases, Real World Data is being provided and utilized. (questionnaires) via Curia App & Neuria App Physicians Dashboards Hospital Dashboards
Cookie | Duration | Description |
---|---|---|
cookielawinfo-checkbox-analytics | 11 months | The cookie is used to store the user consent for the cookies in the category "Analytics". |
cookielawinfo-checkbox-functional | 11 months | The cookie is set to record the user consent for the cookies in the category "Functional". |
cookielawinfo-checkbox-necessary | 11 months | The cookies are used to store the user consent for the cookies in the category "Necessary". |
cookielawinfo-checkbox-others | 11 months | This cookie is used to store the user consent for the cookies in the category "Other. |
cookielawinfo-checkbox-performance | 11 months | The cookie is used to store the user consent for the cookies in the category "Performance". |
viewed_cookie_policy | 11 months | The cookie is used to store whether or not a user has consented to the use of cookies. It does not store any personal data. |
0
Gene regulatory network analysis provides insights into transcription factors and their target interplay, also aids in understanding the hierarchy of transcription factors in a given network along with identification of master regulators. scRNAseq analysis platform enables investigations into distinct cell populations,their relative proportions and cell type specific differential expression of genes across different experimental conditions.
At Innoplexus, we use an AI driven approach for generation of optimized molecular structures to facilitate drug discovery endeavor in the following ways:
Innoplexus leverages its AI based discovery engine to identify the most promising target with high biological relevance and market potential. The advantage of AI-based process are: an automation of manual and labor intensive tasks, an ability to process, integrate and make sense of the large volumes of complex and unstructured datasets and an AI-enhanced process of knowledge discovery for understanding their cross-connections in biological network, which is especially important in the early phases of drug discovery. AI can accelerate the target identification process and also it can optimize the identification and optimization of lead molecule discovery. AI does so by searching through the past knowledge of the compounds, by examining a large variety of combinations and by recommending the most suitable leads. In particular, deep learning methods can explore existing data to help predict how tissue or body systems may respond to a given drug.
Further, AI can play a role in the identification of target patient populations for clinical trials, in predicting disease progression based on molecular data obtained from tissue samples and in stratifying patients either in the clinical trials preparation phase or in the optimization of the treatments based on patients’ responses and their individual characteristics.
Such advantages are honored by regulatory authorities with shortened and expedited drug approval processes. At Innoplexus we tighten up the data-driven safety even further by validating drug repurposing candidates with AI-tailored predictive biomarkers to track the efficacy of repurposed therapeutics and with our AI-powered clinical trial prediction tool.
Predicting the probability or likelihood of success (PoS) for a clinical trial is important for pharma investors and to the clinical investigators that help them in evaluating the economic and scientific decisions. Without the proper estimate of PoS there is a chance for misjudging the risk and value proposition for the drug development that may lead to loss of opportunities for both investors and patients. Furthermore, to help prioritize the proper resource allocations, accurate and timely assessment of risk is critical and is the need of the hour for the drug development companies conducting the clinical trials.
Innoplexus, an AI-based company, leveraged its life sciences data ocean and proprietary AI technologies to build a clinical trial prediction (CTP) engine that can predict the probability of success for a clinical trial.
To discover and prioritize indication, our approach consists of the following steps:
Pathway Correlation and Simulations: To identify pathways based on literature mining and publicly available databases.
We also leverage our research graph including PPI, co-expression studies to identify the 1st tier and 2nd tier pathways: Indication identification, Literature and evidence-based Analysis, Assessment of Biological mode, Estimate of trial complexity & feasibility.