Metanova Labs: Bittensor revolutionizes drug discovery with decentralized virtual screening, combinatorial reactions expand possibilities to 65 billion, and dual incentives drive innovation | TWIST

2 hours ago 7

Key takeaways

Bittensor is a decentralized network that uses crypto incentives to reward contributions to AI models and compute.
The network can support a variety of applications, including drug discovery and renting compute resources.
Subnets in Bittensor involve three main actors: subnet owners/operators, miners, and validators.
The drug discovery process is currently costly and time-consuming, often described as being in a state of crisis.
Metanova Labs launched a proof of concept for decentralized virtual screening, pioneering this approach in drug discovery.
Dual incentive mechanisms in the network allow miners to submit molecules or compete with chemical search algorithms.
The heat picking process in drug development evaluates submissions for potential toxicity and efficacy.
Combinatorial reactions can expand the dataset of potential molecules to approximately 65 billion possibilities.
Drug development involves derisking assets and generating intellectual property at multiple stages.
The complexity of drug development requires refinement and testing to ensure safety and efficacy.
Personalized medicine is crucial due to the varying responses of individuals to treatments.
Decentralized networks like Bittensor can streamline the drug discovery process by incentivizing global creativity.

Guest intro

Micaela Bazo is CEO of Metanova Labs, the crypto-native biotech company behind NOVA, Bittensor Subnet 68, a decentralized AI network that crowdsources drug discovery to screen billions of molecules against protein targets. Her platform has already screened 4.8 million molecules across 7,000 targets, accelerating the identification of novel therapeutics for mental states like mood and reward. Metanova aims to cut drug discovery costs in half by replacing Big Pharma’s slow trial-and-error model with distributed AI optimization.

The structure and purpose of Bittensor

Bittensor is a decentralized network that incentivizes contributions to AI models and compute through crypto rewards.

— Metanova Labs
The network supports a wide range of applications, including drug discovery and renting compute.
One of the things that makes it very unique is the fact that you can use this network to train any kind of AI use case.

— Metanova Labs
Bittensor’s operational model is based on rewarding useful AI contributions.
The network’s versatility showcases its potential impact across multiple industries.
Understanding decentralized networks is crucial for grasping Bittensor’s role in AI.
Subnets operate with three main actors: subnet owners/operators, miners, and validators.
You’ve got the subnet owner/operator, miners, and validators, each playing a crucial role.

— Metanova Labs

The crisis in drug discovery

Drug discovery is described as being in a state of crisis due to high costs and long timelines.
Most people are describing it as being in a state of crisis with the average drug taking about $2.6 billion and ten years.

— Metanova Labs
The traditional process is costly and time-consuming, necessitating innovative solutions.
Decentralized networks like Bittensor offer potential solutions to streamline drug discovery.
Metanova Labs is pioneering a decentralized approach to address these challenges.
The need for innovative solutions is underscored by the significant issues in the pharmaceutical industry.
Drug discovery’s current state highlights the importance of decentralized problem-solving.
Understanding the challenges in traditional drug discovery processes is essential for appreciating new approaches.

Decentralized virtual screening

Metanova Labs launched a proof of concept for decentralized virtual screening.
We launched March 1 and it was a proof of concept of doing this in a decentralized way.

— Metanova Labs
The approach had never been attempted before, highlighting its pioneering nature.
Decentralized virtual screening aims to improve drug discovery through innovative methods.
The dual incentive mechanisms enhance the virtual screening process.
Miners can submit molecules or compete using chemical search algorithms.
Our miners are either submitting molecules of interest or competing on chemical search algorithms.

— Metanova Labs
This innovative approach leverages decentralized methods and incentivization.

The role of combinatorial reactions in drug discovery

Combinatorial reactions can expand the dataset of potential molecules significantly.
We started with a dataset of a billion molecules and expanded it to about 65 billion possibilities.

— Metanova Labs
This expansion demonstrates the scale of possibilities in drug discovery.
The innovative approach emphasizes synthesizing new molecules through combinatorial chemistry.
Understanding combinatorial chemistry is crucial for appreciating its role in drug discovery.
The potential for drug discovery is greatly enhanced by expanding the dataset.
This approach provides a quantitative perspective on the scale of possibilities.
The dataset expansion underscores the innovative nature of Metanova Labs’ methods.

The process of derisking assets and generating IP

Drug development involves derisking assets and generating intellectual property.
It is a game of derisking the assets and generating IP.

— Metanova Labs
Creating IP and managing risks are essential strategies in drug development.
The strategic approach highlights the importance of risk management in biotech.
Understanding the complexities of drug development is crucial for appreciating these strategies.
The process of derisking assets is fundamental to successful drug development.
Generating IP is a key component of the biotech industry’s strategic approach.
This insight provides a clear explanation of strategic approaches in drug development.

The complexity of drug development

Drug development is a complex process requiring refinement and testing.
The idea is to improve versus what would be random, accelerating getting to cures.

— Metanova Labs
Iterative testing is necessary to ensure safety and efficacy in treatments.
Personalized medicine is crucial due to varying individual responses.
The complexity of drug development underscores the need for innovative solutions.
Understanding the challenges in achieving effective treatments is essential.
The necessity of refinement and testing highlights the iterative nature of drug development.
This insight explains the challenges faced in achieving effective treatments.

Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our Editorial Policy.

Read Entire Article