A Strong Contender for the Next Nobel Prize
The startup Iambic has made a breakthrough that surpasses Google DeepMind’s AlphaFold. While AlphaFold, the AI system developed by Google DeepMind, recently earned a Nobel Prize in Chemistry for predicting the 3D structure of how molecules bind to target proteins, this is just the beginning. It’s impressive but not enough to drastically reduce the time (10-15 years) and costs ($1-2.6 billion per drug) required to bring new medicines to market.
Iambic has taken it a step further by developing an AI model that predicts, with remarkable accuracy, how well a human body will absorb a specific drug candidate. The predictions are validated against real-world data, making it a promising tool for pharmaceutical development.
The success of a drug candidate depends on several key properties—pharmacokinetics, efficacy, and toxicity. These are exactly the factors predicted by Iambic’s AI-powered drug discovery platform called Enchant, which boasts a predictive accuracy of 0.74, compared to just 0.58 achieved by previous models.
The Enchant model could potentially cut the costs of drug development in half, allowing pharmaceutical companies to assess a drug’s potential success at the earliest stages of research.
To understand the significance of Iambic’s breakthrough, consider the vast competition in the “AI in Biomedicine” sector, where more than 8,600 companies are racing to unlock the next major innovation (as shown in the chart above). In the sub-segment of “AI-based Analytics Platforms for Drug Development” alone, the number of companies has grown fourfold in the last three years, now standing at 950 competitors (see the chart below).
#AI #DeepPharma #DrugDiscovery #Biotech #Pharmacology #AlphaFold
#science
The startup Iambic has made a breakthrough that surpasses Google DeepMind’s AlphaFold. While AlphaFold, the AI system developed by Google DeepMind, recently earned a Nobel Prize in Chemistry for predicting the 3D structure of how molecules bind to target proteins, this is just the beginning. It’s impressive but not enough to drastically reduce the time (10-15 years) and costs ($1-2.6 billion per drug) required to bring new medicines to market.
Iambic has taken it a step further by developing an AI model that predicts, with remarkable accuracy, how well a human body will absorb a specific drug candidate. The predictions are validated against real-world data, making it a promising tool for pharmaceutical development.
The success of a drug candidate depends on several key properties—pharmacokinetics, efficacy, and toxicity. These are exactly the factors predicted by Iambic’s AI-powered drug discovery platform called Enchant, which boasts a predictive accuracy of 0.74, compared to just 0.58 achieved by previous models.
The Enchant model could potentially cut the costs of drug development in half, allowing pharmaceutical companies to assess a drug’s potential success at the earliest stages of research.
To understand the significance of Iambic’s breakthrough, consider the vast competition in the “AI in Biomedicine” sector, where more than 8,600 companies are racing to unlock the next major innovation (as shown in the chart above). In the sub-segment of “AI-based Analytics Platforms for Drug Development” alone, the number of companies has grown fourfold in the last three years, now standing at 950 competitors (see the chart below).
#AI #DeepPharma #DrugDiscovery #Biotech #Pharmacology #AlphaFold
#science
1👍45👀12⚡5🕊4😁2
🧬
DeepMind has released AlphaFold 4, pushing protein structure prediction into a new era.
The updated model handles:
• ~20,000 human proteins
• multi-chain complexes
• protein–protein interactions
• selected post-translational modifications
Reported accuracy reaches ~98% on benchmark datasets — approaching experimental resolution in many cases.
📄 Preprint (updated Feb 18, 2026):
https://arxiv.org/abs/2402.18567
⸻
🧪 Why this matters
This is no longer just about predicting isolated protein folds.
AlphaFold 4 moves toward modeling biological systems — complexes, assemblies, interaction interfaces — the level where real drug discovery happens.
Targets long considered “undruggable,” such as:
• KRAS
• MYC
may become structurally tractable thanks to improved interface prediction.
Pharma companies are already integrating AI-generated structures into drug pipelines, potentially shortening early-stage discovery timelines dramatically. (Not “10 years → 2 years” overnight — but the structural bottleneck is shrinking fast.)
⸻
🔬 Bigger picture
If AlphaFold 2 solved the protein folding problem,
AlphaFold 4 begins solving the interaction problem.
Structural biology is shifting from slow, expensive crystallography toward AI-assisted molecular design.
We are watching the transition from “map the molecule” to “engineer the molecule.”
The question now isn’t can we predict structure?
It’s how fast can we turn structure into therapy?
#AlphaFold #AI #DrugDiscovery #Biotech #ComputationalBiology
DeepMind has released AlphaFold 4, pushing protein structure prediction into a new era.
The updated model handles:
• ~20,000 human proteins
• multi-chain complexes
• protein–protein interactions
• selected post-translational modifications
Reported accuracy reaches ~98% on benchmark datasets — approaching experimental resolution in many cases.
📄 Preprint (updated Feb 18, 2026):
https://arxiv.org/abs/2402.18567
⸻
🧪 Why this matters
This is no longer just about predicting isolated protein folds.
AlphaFold 4 moves toward modeling biological systems — complexes, assemblies, interaction interfaces — the level where real drug discovery happens.
Targets long considered “undruggable,” such as:
• KRAS
• MYC
may become structurally tractable thanks to improved interface prediction.
Pharma companies are already integrating AI-generated structures into drug pipelines, potentially shortening early-stage discovery timelines dramatically. (Not “10 years → 2 years” overnight — but the structural bottleneck is shrinking fast.)
⸻
🔬 Bigger picture
If AlphaFold 2 solved the protein folding problem,
AlphaFold 4 begins solving the interaction problem.
Structural biology is shifting from slow, expensive crystallography toward AI-assisted molecular design.
We are watching the transition from “map the molecule” to “engineer the molecule.”
The question now isn’t can we predict structure?
It’s how fast can we turn structure into therapy?
#AlphaFold #AI #DrugDiscovery #Biotech #ComputationalBiology
arXiv.org
Diffusion Language Models Are Versatile Protein Learners
This paper introduces diffusion protein language model (DPLM), a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences. We first...
🔥36👍24👀19⚡15🕊13