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BiotechNewsUsing AI to Crack Undruggable Drug Targets
Using AI to Crack Undruggable Drug Targets
BioTechAIHealthcare

Using AI to Crack Undruggable Drug Targets

•February 13, 2026
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Labiotech.eu
Labiotech.eu•Feb 13, 2026

Why It Matters

By unlocking undruggable targets, ProPhet could dramatically increase the pipeline of viable drug candidates, attracting pharma investment and accelerating patient access to new therapies.

Key Takeaways

  • •AI maps proteins and compounds into shared interaction space
  • •Screens billions of molecules without solved structures
  • •Accelerates hit-finding for traditionally undruggable targets
  • •Reduces data requirements compared to conventional models
  • •Positions ProPhet as AI-driven discovery leader

Pulse Analysis

Artificial intelligence is reshaping drug discovery by addressing one of its longest‑standing challenges: targets that lack clear binding pockets or structural data. Traditional high‑throughput screening relies on crystal structures or extensive biochemical assays, limiting the scope of searchable proteins. AI models that learn abstract representations of protein‑ligand interactions can bypass these constraints, opening a new frontier for therapeutic exploration and reducing time‑to‑clinic for innovative treatments.

ProPhet’s platform builds on this paradigm shift, embedding both protein surfaces and small‑molecule chemistries into a unified latent space. In practice, the system evaluates billions of virtual compounds in seconds, flagging promising hits even when no experimental structure exists. Early benchmarks reported hit rates several folds higher than conventional docking, and collaborations with academic labs have validated the approach on historically “undruggable” enzymes. By minimizing data hunger and eliminating the need for exhaustive structural libraries, ProPhet delivers a scalable, cost‑effective pipeline that can be integrated into existing pharma workflows.

The broader market implications are significant. Venture capital and big‑pharma AI funds have surged, reflecting confidence that computational methods will shorten discovery cycles and lower R&D risk. ProPhet’s success could accelerate this trend, prompting more partnerships and licensing deals. Moreover, the company’s leadership under Avital Sharir‑Ivry highlights the growing influence of women scientists in high‑tech biotech, reinforcing diversity as a driver of innovation. As AI continues to mature, platforms like ProPhet are poised to redefine how the industry tackles the most intractable disease targets.

Using AI to crack undruggable drug targets

As we mark the International Day of Women and Girls in Science, we welcome Avital Sharir‑Ivry, Co‑Founder and Chief Scientific Officer of ProPhet, an innovative Israeli startup launched in late 2024 from the AION Labs venture studio.

With a PhD in computational biology and drug research, Avital brings deep expertise in structural biology, enzyme design, and evolutionary bioinformatics to her role leading ProPhet’s scientific efforts.

ProPhet itself is changing small‑molecule drug discovery by using advanced AI and machine learning to map proteins and compounds into a shared interaction space. This enables rapid, scalable screening of billions of molecules—even for so‑called “undruggable” targets—without relying on solved structures or massive datasets, speeding up hit‑finding and expanding the reachable therapeutic landscape.

01:36 Meet Avital Sharir‑Ivry

08:55 How ProPhet emerged from AION Labs challenge

12:12 Core AI technology for hit‑finding at scale

15:00 Benchmarks and collaborations

17:54 ProPhet’s differentiation from traditional drug discovery

19:45 Importance of scaling small‑molecule exploration

24:19 Pharma AI investments and emerging trends

26:11 Future AI breakthroughs in drug discovery

27:04 Challenges and progress for women in science

31:15 Keep up with ProPhet

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