Generative AI for Novel Target Combinations

Generative AI for Novel Target Combinations

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1 Scientist Founder 2 Scientist Co-Founders

AION Labs is a Venture Studio backed by AstraZeneca , Merck , Pfizer , Teva , Israel Biotech Fund , Amiti Ventures , and Amazon Web Services (AWS) , powered by BioMed X, with the support of the Israel Innovation Authority (IIA). The partners have come together to establish a fully funded new startup to pioneer:

Generative AI for Novel Target Combinations

At AION Labs, we are committed to partnering with exceptional founders from diverse backgrounds to build groundbreaking companies. Our unique Venture Studio model fosters cross-industry collaboration by connecting global pharmaceutical leaders with the best AI, computational, biotech, and tech entrepreneurs. Through strategic partnerships, funding, expert mentorship, and unparalleled access to pharma R&D expertise, we empower startups to accelerate their journey from idea to market, enabling the creation and validation of transformative solutions that address the industrys biggest R&D challenges—ultimately driving better healthcare outcomes for humanity.

What we are looking for

Multi-functional drug modalities, such as bi-specific antibodies and multi-specific peptides, have demonstrated substantial clinical success in treating complex diseases like cancer and metabolic disorders, and hundreds are currently under development. However, designing these drugs requires moving beyond single-target approaches toward multi-targets molecular discovery.

Traditional target discovery relies on expert-driven hypotheses, literature reviews, academic research, and fragmented experimental data, all generated on a single target with a single drug. We seek for a novel AI-driven platform that can systematically identify, rank, and validate molecular target combinations for multi-specific biologics. Prioritizing hypotheses should be based on disease relevance, biomarker predictability, and on-target adverse reaction risks, in therapeutic areas such as Oncology, Cardiovascular-Kidney-Metabolic (CKM), and Immune-mediated disorders.

Existing examples for rational combination include dual signal modulators and immune cell engagers in cancer, complementary biology for additive therapeutic effect (co-agonist) in Obesity, and modulating multiple molecular targets across an organ network in Cardiovascular diseases.

We invite exceptional entrepreneurs, scientists, and technologists in computational biology, bioinformatics, AI-driven drug discovery, target identification, machine learning and related fields, including academic experts to submit proposals for a new startup addressing this challenge.

A successful platform should address the following:

Target pair ranking: Given an indication, patient population, the desired clinical phenotype modulation and the strategy (synergism/engager/etc.), generate a ranked list of potential target pairs.

Mechanistic explanation: Provide detailed scientific rationale linking selected molecular target combinations to disease phenotype.

Biomarker prediction: Identify downstream biomarkers to select patient populations and assess target engagement.

On-target adverse reactions: Considering potential toxicity and incorporating them into the hypothesis ranking.

Validation strategy: Propose a functional validation to refine and confirm target combination hypotheses.

The new technology developed by the startup will be tested using selected indications as a proof-of-concept according to