Decentralized AI Drug Development

Decentralized AI startup for computational drugs and therapeutics while rewarding computation & data, with percent of profits to community

Justin Diamond - PhD Candidate - AI Researcher in Physics, Chemistry, Pharma, Bioinformatics at academic institutions including University of Michigan, Toyota Technological Institute of Chicago, Boston University, University of Luxembourg, and University of Basel.

[IMPACT]

Overview

A myriad of sectors are heavily dependent on large simulations of physical systems based primarily on traditional methods like Molecular Dynamics, and Density Functional Theory. Such sectors include Pharmaceutical, energy, semiconductors, etc. For example, in the recent covid-times, millions of Molecular Dynamics simulations have been run, largely independently, related to the ACE receptor and spike protein to better understand the binding mechanisms[3]. Currently, most of this information is dormant, redundant, and inconclusive. The data is frequently dormant as the simulation data is analyzed for publications or industrial applications and then held on local data storage units, redundant as there are often teams around the world doing highly similar simulations, and inconclusive because often single simulations lack enough information to lead to conclusive results. Thus, centralized infrastructures are rather limiting in developing AI-centric frameworks for improving the efficiency and accuracy of physics computation and knowledge extraction. As bad as this is, this is only the surface of the problem. The larger problem is that there is no natural way to incorporate vast and diverse amounts of physics information (experiments, quarks, chemicals, proteins), data, knowledge , and algorithms in a cohesive and synergistic manner.

Description of us and our goals

Our end goal is clear. We hope to create the correct infrastructure to incentive mass adoption of cardano-based protocols in the computationally oriented scientific communities including academia, industry, start-ups, and individual community members.

We are creating a decentralized protocol for the simulation of physical systems while leveraging Nunet for decentralized computational resources and SingularityNet for decentralized AI enhancements with open ended improvements using anything from Deep Learning [1], to neuro-symbolic AI [2], quantum chemistry [4], cognitive architectures[5], etc. Additionally, we are building a tokemonics system to incentive computation, data, algorithm development, mining, and community rewards for collaborations and support from individual community members, academics, and even corporations. One of our driving principles is the coupling of advancements in artificial intelligence to advancements in functional near-term technologies.

Our solutions will be useful in markets like Biotechnology, Artificial Intelligence, Chemical Synthesis, and many more. These are quickly growing markets, and would be absolutely amazing for the health of the cardano ecosystem to bridge the market demand home. Take for instance just the Biotechnology market; it is expected to surpass 1.5 Trillion by 2030 and growing at nearly ten percent per year [6].

The paradigm shift we are creating with SNet and Nunet stems from creating a computational and algorithm environment for end-to-end integration of multi-scale simulations for developing and employing theoretical and AI algorithms built up from heterogeneous data sources, symbolic knowledge extraction, and cognitive principles to lead to the most interconnected framework for self-consistent computations in the physical sciences. This will all be done to mimic the use of High Performance Computing infrastructures, and in principle, we should be able to simulate molecular systems faster than many of the top supercomputer when Nunet is fully developed with a large enough ecosystem. All of our code will be developed for parallelized, multi-virtual node CPUs/GPUs. By using AI integration, we should also be able to surpass many of the conventional bottlenecks of such computations.

Community and Industry relations

From an industry perspective, users (entities taking advantage of our computational protocol) can exchange tokens for theoretical computations of a particular system of study and/or private/public algorithms developed by various entities (individuals, research labs, corporations, community members). From the community perspective they will get rewarded for the contribution to data, computation, algorithm development (to name a few).

Rewards are mostly obtained from the following procedures: physics data (experiments, simulation data, theory), computational resources and storage, algorithm developments (developing new algorithms, training neural networks, improving existing networks), mining, and technology development. The first two are rather clear. In short, mining is the eventually-automated process of performing specific computations as suggested by community members or recommended by an AI agent that anyone can partake in by staking or resource allocation. As well, entities that develop on the protocol (via any of the above including mining) can obtain rewards via a predetermined ratio of tokens paid by industrial entities using smart contracts.

In this proposal we are specifically looking to develop a minimum viable product (MVP) that can be built on Cardano to begin collaborating with pharmaceutical industries and biotechnology with the goal of being the decentralized cloud solution for AI-based drug and therapeutic development.

Phases

Phase I is the development of MVP of AI-based algorithms. After this we will begin to collaborate with industry.

Phase II is the growth of our team and external funding events to continue development begin collaborations.

Phase III is to create a smart contract platform and interface as well as increase connectivity of the algorithms to begin fully autonomous drug development solutions. Here 'connected' means that the algorithms should be more synergistic, end-to-end, and covering multi-faceted areas of AI development from quantum properties of the small molecules to coarse grained dynamics of proteins and solvent.

Phase IV is continued automation, community development, tokenomics, and the most rewarding stage. At this stage we expect to begin to turn a profit, allowing a percentage of our profits to be cycled into the community and allow for continued development.

Our proposal is concentrated on Phase I of the above list.

The goal of the Business solutions challenge is to remove the middle man and create automated processes to increase Cardano's adoption outside the financial sector. Here we hope to do just that by creating a decentralized AI based drug development solution by providing the computation and the AI algorithms necessary to help pharma and biotechnology industries develop better technologies, all while incentivizing community involvement, industry and academic collaborations and involvement, and guiding Cardano's technology strongly into new directions.

Mostly general technical research and development uncertainties and complexity of the project from that side. We are fairly confident that the team will be able to deal with difficulties, but that may require additional time and work. Of course, we are working with Nunet, and any delays on their side could be near-term problematic, but can be circumvented by focusing on the details that can be directly implemented at current times. They are a well-proven team, and delays may happen, but they build great code.

We also need to hire two AI developers in order to successfully attempt development.

[FEASIBILITY]

Overview of Specific Algorithms to be implimented

• In two-three months:
• Beginning prototypes of AI algorithms on single Node and Single GPU
• Data collection via simulations, collaborations, and open-source institutions
• In five-six months:
• Finished Prototypes of AI algorithms on multi Node and multi GPUs relevent to:
• protein-ligand binding efficiency
• solvation effects
• non-local effects of protein - small molecule interactions
• thermodynamics and kinetics of arbitrary molecular compounds using graph generative models
• In six-eight months after fund
• Prototypes of all algorithms in multi node/ multi-GPU settings and look for collaborations
• Training on heterogeneous data obtained from community members or Traditional Algorithms as test bed for multi-scale approaches
• Possible collaborations with DeepChainAda in training neural networks with private information cryptographically
• Funding event to grow team

Miscellaneous Hardware for local testing and development is not needed as we currently have self-owned servers. Any additional resources will be obtained out-of-pocket to improve our chances of obtaining funding.

Function Person/months People Salary Total

AI developers 8 2 $3,500    $56,000

Justin Diamond - PhD Candidate - AI Researcher in Physics, Chemistry, Pharma, Bioinformatics at academic institutions including University of Michigan, Toyota Technological Institute of Chicago, Boston University, University of Luxembourg, and University of Basel.

Years of experience in academic settings studying machine learning related to chemistry, physics, bioinforamtics, and drug development. Some examples are at the University of Michigan I worked on Machine Learning for Protein Structure Prediction (working with Dr. Jinbo Xu, one of the inspirations for DeepMind's AlphaFold ) and at the University of Luxembourg I worked on generative machine learning models for calculating thermodynamic properties of small molecules as well as quantum mechanical and Molecular Dynamics to study the Spike Protein in the corona-virus in a highly parallelized and distributed fashion on a HPC.

https://www.linkedin.com/in/justin-sidney-diamond-881798193

https://github.com/blindcharzard

Floriane LeFloch - Foudning Member in lili.ai AI startup and Web3 Consultant

[AUDITABILITY]

This Catalyst proposal will aid Hetzerk in prototyping large scale computations of physical systems using SingularityNet and Nunet relevant to accurately and efficiently develop therapeutics and drugs with industrial partners allowing for the continued growth and progressive development with further funding.

Increased number of transactions on Cardano due to SingularityNET AI service calls.

Successful measures include

1. Metric showing scalability of Nunet (and future projections) of theoretical simulations and benefits of decentralized infrastructures
2. Increased accuracy of AI enhanced simulations due to heterogeneous data collection relevant to drug and therapeutic development
3. In due course, we will prototype neuro-symbolic integration using OpenCog 2.0 (among others), and other approaches such as Recommendation Systems to suggest optimal computations to be performed when faced with uncertainty in results or model
4. Completed white paper and tokenomics by month 5
5. Peer-reviewed article comparing to centralized solutions
6. Projected Cost Savings for Corporations to work with us
7. lower computational fees and automated pipelines decrease costs
8. Increased partnerships with academic institutions, we plan to have one strong academic partnership by the end of 8 months
9. By 2-3 years, we plan to have 4-5 academic collaboratings in different fields of expertise ranging from quantum mechanics, drug discovery, biomolecular interactions, Artificial Intelligence.
10. By the end of the year, we will have a fund raising event either via token distribution, venture capital, or government research grants.

Our end goal is simple, we are building computational infrastructure on Cardano, in collaboration with Nunet and SingularityNet, to create the correct incentive structures and a recursive cycle of development, roll-out, rewards, and increased efficiency and increasing user-base to obtain a decentralized platform of simulation based solutions for academic and industry related problems like AI based drug development or simulations of bio-molecules with Quantum Mechanical algorithms.

One of the key mechanisms of incentives is to allow, at least, academic groups free computational resources. We can do this, by coupling the loss of value due to computational usage to the gain of actionable knowledge, data, and algorithms to solve some of the most computationally demanding problems in dramatic need of better data, algorithms, knowledge, and efficient and connected solutions. To create a net profitable cycle will take time and a growing ecosystem of partnerships and solutions, but by building now we create the future infrastructure to naturally and with decentralized protocols create more opportunities and participation in the future of beneficial technological and materials development. 

These are medium to long term goals that we hope to accomplish in the next three to five years.

In contrast, at the end of the eight months of funding, we will have a core team of AI developers working collaboratively with pharma industries in developing novel drugs and therapeutics for specific disease, health improvement, and more. At eight months, we will have working prototypes with visible results and periodic updates via twitter and telegram and be looking to increase the size of the team to meet the demands of such a large market. We do NOT plan to have everything solved in eight months, but we will show the benefits for Cardano's community, industry and academic ease of collaboration, and have working prototypes of novel key AI algorithms.

Entirely New Project

SDG Rating

References:

[1] https://pubs.acs.org/doi/10.1021/acs.accounts.0c00472

[2] https://arxiv.org/abs/2006.11287

[3] https://pubs.acs.org/doi/10.1021/acscentsci.0c01236

[4] http://quantum-machine.org/gdml/

[5] https://arxiv.org/abs/1410.5401

[6] https://www.globenewswire.com//news-release/2022/01/18/2368681/0/en/Biotechnology-Market-Size-to-Surpass-US-1-683-52-Bn-by-2030.html