insitro, the AI therapeutics company built on causal biology, announced that it has entered into a definitive agreement to acquire CombinAbleAI and launch its integrated TherML™ (Therapeutic Machine Learning) platform. The acquisition, which is expected to close in late January 2026, brings together advanced biologics design capabilities with insitro’s existing small molecule and oligonucleotide engine to create a unified, full-stack AI platform for therapeutic discovery and design.
This strategic acquisition strengthens insitro’s ability to guide both modality selection and molecular design from early target discovery through developability assessment, accelerating the progression of clinic-ready assets and reducing late-stage development risk.
“Drug discovery has traditionally optimized molecules for potency before assessing developability – often discovering that highly potent candidates face manufacturing constraints,” said Philip Tagari, Chief Scientific Officer, insitro. “By integrating CombinAbleAI’s physics-informed, AI-driven design for complex biologic therapeutics with our causal biology platform, we treat potency and manufacturability as interdependent design criteria from the outset. This fundamentally changes the translation from biological insight to viable therapeutic – instead of optimizing sequentially and hoping for serendipitous alignment, we’re designing for both simultaneously.”
insitro’s CEO and founder, Daphne Koller, Ph.D., highlighted the cultural and technical synergy behind the acquisition: “We are delighted to welcome the CombinAbleAI team in Israel as fellow insitrocytes, alongside our colleagues in the U.S., Poland and Malaysia. Their addition helps launch our integrated TherML platform, which is a critical part of our end-to-end industrialized AI-enabled system built for repeatable, scalable, predictable drug discovery.”
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A Unified AI Engine for Multimodal Therapeutic Design
The TherML platform represents a significant evolution in AI-driven drug design by supporting multiple therapeutic modalities within a single framework:
- Complex Biologics – Leveraging CombinAbleAI’s physics-informed optimization engine trained on more than 100,000 molecular dynamics surrogates to accurately predict protein structure and flexibility.
- Small Molecules – Utilizing insitro’s proprietary Quantitative Adaptive Libraries to densely map chemical space and build high-resolution training datasets.
- Oligonucleotides – Employing advanced automation and AI to industrialize siRNA design, optimizing sequence selection for target knockdown across diverse therapeutic targets.
The technological infrastructure, however, has been supplemented by advanced machine-learning algorithms that predict ADMET properties based on internal as well as external research collaborations. This would help optimize drug-likeness, safety, as well as binding affinity from the early stages of the discovery process itself.
By shifting key optimization parameters upstream, TherML helps reduce late-stage attrition and strike a balance between efficacy and developability, while its integration with insitro’s automated laboratories allows rapid experimental data generation and iterative model refinement.
Echoing the strategic value of the integration, CombinAbleAI co-founder Noam Katz, Ph.D., said: “Effective therapeutic design requires optimization across affinity, selectivity, stability, and manufacturability. We’re very excited to join insitro and integrate CombinAbleAI’s physics-informed AI modeling into insitro’s end-to-end discovery platform to reliably and rapidly translate high-value targets into molecules that perform as intended in the real world.”
CombinAbleAI and AION Labs Background
CombinAbleAI was originally incubated within AION Labs, an Israeli biotech venture studio supported by global pharmaceutical companies including Merck KGaA, AstraZeneca, Pfizer, Teva, Amazon Web Services, and the Israel Biotech Fund, along with Amiti Ventures and the Israel Innovation Authority. The team will now operate as a new insitro R&D center in Israel focused on advancing large molecule design.
