Our Approach
Artificial Intelligence
Artificial Intelligence (AI) can help mitigate challenges in drug developing by accelerating, de-risking, and lowering costs of the process. Even more importantly, smart use of AI has the potential to improve the success rate in drug development, by expanding the search range for promising targets or leads, front-loading late-stage success criteria in earlier development stages, and assisting scientists in complex decision-making.
Using patient data, we could identify new drug targets and link the patient all the way back to target identification.
Accelerate the journey from data to new cancer drugs with the help of AI.
The Artificial Intelligence Platform focuses on leveraging AI to develop, train, and verify models for implementation throughout the preclinical development pipeline. The AI platform's expertise in de novo drug design, mode of action prediction, and affinity prediction accelerates the discovery of novel cancer therapies by enabling efficient target identification, compound optimization, and early efficacy assessment, reducing development time and increasing the likelihood of clinical success.
The Platform's value is multiplied by overlaying innovations from the other two innovation Platforms of Oncode Accelerator: well-defined Patient Cohorts and Organoids. Drug developers partnering with Oncode Accelerator can apply AI tools to clinically relevant datasets from our Patient Cohorts Platform to support identification and optimization of therapeutic candidates, predict the efficacy of therapies, and identify potential patients for proof-of-concept studies. Similarly, data from patient-derived organoids can be leveraged to develop, train, and optimize predictive models.
The synergy of AI-analyzed human data and refined experimental models presents a significant opportunity to boost drug development success, moving beyond the traditional reliance on cell lines and animal models. In addition, AI can be used to identify patient subpopulations with the highest likelihood of success at the clinical trial stage, early on. Our innovative approach should lead to both an accelerated pace of discovery and development, as well as enhanced patient outcomes.
Harness AI for optimized preclinical research, from discovery to first-in-human study
Key to the success of the Oncode Accelerator program is the selection and execution of high-quality Demonstrator Projects which aim to both validate the Oncode Accelerator infrastructure while simultaneously addressing unmet medical needs. New partners can join the Oncode Accelerator Program with a Demonstrator Project to co-develop assets within one of our four preclinical development pipelines, so called Workstreams:
Together with our two other innovation Platforms (Patient Cohorts and Organoids), the Artificial Intelligence Platform aims to integrate innovative technology into the preclinical discovery and development infrastructure built and implemented within the Oncode Accelerator Workstreams.
The combination of our Workstreams and Platforms creates a synergistic effect, which should allow for earlier and more accurate predictions of the safety and effectiveness of newly developed therapies for specific patient groups, eventually resulting in better therapies tailored to individual patients or patient subgroups.
Click on the arrows below to learn more about the activities of the Oncode Accelerator AI Platform.
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AI can support cancer researchers and drug developers to...
- Apply computational methods for target identification & validation through gene regulatory networks
- Use AI methods to predict protein-protein interactions from structural data
- Efficiently screen virtual libraries starting from both the target structure and multiple ligand structures
- Predict affinity of ligands to the target structure, learning from biochemical data
- Use reinformed learning to generate novel chemical structures with favorable affinity, physicochemical properties, selectivity over off-targets, and favorable ADMET properties
- Use AI methods for the design of combination treatments by understanding drug mode of action and cellular responses
- Develop novel vaccines to oncologically relevant drug targets
- Apply Thermal Protein Profiling (TPP) to verify and validate computational predictions experimentally, and generate data relevant for model training
- Use machine learning to determine optimal patient stratification for drug trial design
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The AI Platform...
- Utilizes human centered hybrid (AI) methods to accelerate the discovery and development of innovative anti-cancer drugs
- Uses Whole Genome Sequencing (WGS) data and ancillary (single-cell) multi-omics datasets to systematically identify the downstream effects for many coding and non-coding germline and somatic variation at once
- Applies gene regulatory networks, to identify potential drug targets and gene-expression data for quantitative trait locus (eQTL) mapping
- Predicts the full biological interaction spectrum for untested molecules
- Virtually screens large computational libraries to find new active molecules
- Has developed AlphaBridge as a tool for ranking, validating, and scoring macromolecular interaction predictions which can be used in custom pipelines for validating prediction data
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Why should this be of interest?
Drug development is a notoriously difficult, lengthy, and cost-intensive process. Traditional drug discovery and development methods often demand high financial and time inputs, while achieving only a low success rate– resulting in high economic and social costs of failure. AI can help mitigate these challenges by accelerating, de-risking, and lowering costs of drug development. Even more importantly, smart use of AI has the potential to improve the success rate in drug development, by expanding the search range for promising targets or leads, front-loading late-stage success criteria in earlier development stages, and assisting scientists in complex decision-making.
The Oncode Accelerator oncology ecosystem in which the AI Platform is embedded fosters collaboration among academic researchers, biotech companies, and industry partners, combining state-of-the-art facilities and technologies with diverse expertise. This human-technology synergy, bolstered by the co-funding and project management support allocated to successful Demonstrator Project applicants, will accelerate the development of innovative cancer therapies that for the benefit of patients of all ages.
Interested in learning more about the activities of the AI Platform and how to join our program?
Contact Us
Oncode Accelerator is a dynamic collaboration of more than 35 public and private partners. The following consortium partners are part of the AI Platform.
Below is a curated selection of scientific publications by the Artificial Intelligence Platform. Click on each publication to access the full text online.