transfer studios work with companies and/or government labs to source ideas and intellectual property. They then transfer the IP and build the startup inside the venture studio.

Corporate studios, source ideas and intellectual property inside their own company. They then build the startup inside a separate corporate venture studio inside the company.

A niche studio  is a standalone venture studio that generates its own ideas and IP in a specific industry and domain.

An industry-agnostic studio  , is a standalone venture studio that generates its own ideas and IP and is industry and market agnostic.

Lab-on-a-chip: microfluidics in drug discovery
In key issues of drug discovery, such as chemical synthesis, screening of compounds and preclinical testing of drugs on living cells, microfluidic tools can meet the demands for high throughput, and can improve or might eventually replace existing technologies.

Microfluidics as a tool for drug delivery
One of the main possibilities offered by microfluidics is the possibility to study the effects of a certain drug at cellular level in various physicochemical microenvironments. Indeed, microfluidic-based cell culture platform can mimic in vivo conditions as well as generate different environmental profiles. Moreover, microfluidics allows precise and controlled drug flow to the culture chamber, so that it is possible to monitor, for instance, the cells’ response to high concentrations or to others biochemical stimuli.

Our team specializes in computer modeling program that uses AI to run through thousands of potential combinations of different compounds. It can screen every blend autonomously and predict the results of each one

Predicting 3D protein structure
AI can predict the 3D structure of proteins, which can help predict the effect of a compound on the target.

Designing new molecules
AI can design new molecules and predict the efficacy of potential drug candidates.

Analyzing biomedical data
AI can analyze large amounts of biomedical data to identify existing drugs that may have therapeutic potential for different diseases

Target identification:  At the target identification phase of drug discovery, AI is being trained on large datasets, including omics datasets, phenotypic and expression data, disease associations, patents, publications, clinical trials, research grants, and more.

Molecular simulations:  AI is also being used to reduce the need for physical testing of candidate drug compounds by enabling high-fidelity molecular simulations  that can be run entirely on computers (i.e., in silico) without incurring the prohibitive costs of traditional chemistry methods.

Prediction of drug properties: AI platforms that bypass simulated testing of drug candidates by predicting key properties such as toxicity, bioactivity and the physicochemical characteristics of molecules.  

Candidate drug prioritization:  Once a set of promising “lead” drug compounds has been identified, our AI platform ranks these molecules and prioritize them for further assessment.  

Synthesis pathway generation:  AI platforms that is capable of generating synthesis pathways for producing hypothetical drug compounds , in some cases suggesting modifications to compounds to make them easier to manufacture.

Predicting drug behavior: AI platforms that predict drug behavior by analyzing relationships between drug properties, formulation components, and physiological factors. 

Tailoring therapies: AI platforms that help our researchers design more targeted and effective therapies based on individual patients' genetic, metabolic, and clinical profiles.

AI for nanocarrier design and optimization: Examining the application of AI in designing and optimizing nanocarriers, which can deliver drugs to specific cells or tissues in the body, improving drug targeting and minimizing systemic side effects

AI for controlled drug release: Discussing the development of AI-based drug delivery systems that enable controlled drug release, ensuring optimal drug concentrations at the target site over an extended period.

AI in drug delivery route optimization: Analyzing the role of AI in determining the most effective and least invasive method of drug administration based on drug properties, patient characteristics, and delivery routes.

Microfluidyx is an emerging paradigm that combines different engineering solutions to drive data-driven discovery. Using this paradigm, we created a universal layered architecture that enables multiple-AI-assisted microfluidic platforms. 

Each platform incorporates patented microfluidic chips designed to fit market-specific needs. Technology layers, including machine vision, deep neural networks, edge computing, and cloud databases, are seamlessly woven to create fluidic discovery engines. 

Microfluidics are also proving to be an economical high speed solution for screening one sample against a large panel of reagents in testing for toxicity or looking for biomarkers, whether testing the reaction of one drug against many different bacteria or cells, or one cell against many different possible markers, and their cross interactions.

Zivaah Technologies collaborates with various research labs to commercialize microfluidics chips.

Nea Bioscience “Organs-on-Chips,”  (OoC) combines microfluidics, AI, and human cells to recreate the complex interactions of organs in a controlled environment to study various diseases, including lung infections and liver toxicity.

OoC OoC platform simulates human organs on a chip of the physiological environment and functionality, and with high fidelity reproduction organ-level of physiology or pathophysiology, exhibits great promise for innovating the drug development pipeline. Applications, including drug testing, disease modeling, and personalized medicine.

Arisymo Genomic-Driven, AI-Enabled Small Molecules Drug Discovery platform applies pharmacogenetics, pharmacogenomics and functional genomics to dissect, predict and monitor the nature of the individual response to medications predicting individual responses to drugs, and improve safety and efficacy in therapeutic.

This approach is likely to have radical consequences in the planning, conduct of clinical trials and medical treatment of diseases, and customize the use of pharmaceuticals for specific subgroups of patients.

Vexpra Genomic-Driven Drug Discovery Platform, using AI methods and technologies, designs antibodies based on three alternative and complementary models.

• -The full in silico and de novo design of antibodies and T cell receptors, including epitope definition, and the Ab prediction of their aggregation and immunogenicity propensities.
• -Full in silico affinity maturation of known antibodies
• -Affinity maturation using experimental high-throughput sequencing and display technologies, whose results are enriched using deep learning models, resulting in the generation of innovative and enhanced antibody sequences.

Ackyee Inc is AI-Driven Drug Design and Discovery platform company, focusing on Small Molecule Drugs Targeting Allosteric, Functional, and Subunit-Selective Sites on GPCRs utilizing is Machine Learning (ML) GPCR Molecular Activity Predictor (MAP) and Deep Learning (DL) GPCR Molecular Identification Design Structure (MIDAS) platforms.

GPCRs are central to human biology and are the target of approximately 30% of all currently approved drugs but there are undruggable targets still!

Sprectroo AIDRRP is a Genomics Driven Drug Derivatization and Repositioning Platform that implements Deep Learning, (DL) with Blockchain and Genomics de novo design technology called "derivatization design and discovery" that applies artificial-intelligence-assisted forward in Silico synthesis for the generation of near neighbor lead analogues as well as scaffold variations.

The several attractive features of the methodology include synthetic feasibility, reagent availability and cost data associated with each new molecule; thus, detailed synthetic assessment is automatically generated during the design.

Enzia Bioscience is a Genome-Driven Alzheimer Drug Discovery company that employs AI tools to analyzing multi-omics, various types of heterogeneous biological networks, and clinical databases for target identification and development of effective prevention and treatment for AD.

Enzia Bioscience employs Sprectroo AI platform for repurpose the drug for target identification and development of effective prevention and treatment for AD.

Genomic-Driven, AI-Enabled Targeted Cancer Therapy 

Target only the genetic mutations contributing to cancer cell growth, delivering genomic-guided, precise treatment for each person’s disease characteristics.

Targeted drugs attack only cancer cells and have fewer side effects than chemotherapy.

Targeted therapies are designed to destroy only cancer cells, reduce damage to healthy cells and minimize side effects that negatively affect the patient’s quality of life

Targeted therapeutics block or turn off signals that tell cancer cells to grow and divide

Bioqenix's approach creates a secondary immune system designed to target and combat Alzheimer's disease specifically.

AI integration allows for intelligent adaptation and response, increasing therapeutic effectiveness. Using AI to create tailored treatment regimens based on individual's disease.

AI optimization determines the perfect combination of clones for maximum therapeutic effect.

Neurolics Inc is an AI-Enabled neurological GPCR Drug Discovery company that focused on targeting allosteric sites of neurological GPCRs as a mean to develop allosteric modulators to combat neurodegenerative diseases.

Zymetics AI platform includes predicting dissolution, solubility, optimal carriers for solid dispersion, and optimizing nanocarriers, by streamline the drug delivery process, ultimately saving time and resources while enhancing the effectiveness and safety of drug therapies.

This will lead to more targeted and effective therapies tailored to individual patients based on their genetic, metabolic, and clinical profiles.