BenevolentAI

Type

• Neural Networks

  AI use

Benevolent AI use the predictive power of AI algorithm to design new molecules; extracting new hypothesis based on a knowledge graph composed of over a billion relationships between genes, targets, diseases, proteins and drugs. Integrates AI into every step of drug discovery from early discovery to late stage clinical development. The AI model is built on vast quantities of mined and inferred biomedical data. The data is then fed into proprietary knowledge graph, which extracts and contextualizes relevant information. The knowledge graph is made up of a machine curated relationships between genes, diseases, drugs and over twenty types of biomedical entities. Relation inference AI models aids to predict potential non-obvious disease targets overlooked by scientists.

The machine learning models are used to identify patient groups by the molecular signature of design thus facilitating faster clinical trials. This precision medicine approach allows company to identify patient subtypes more likely to respond to drugs, hence, increase success probability in clinic.

Rate of return on customer’s investment to make AI work

Immediate:

• More money, time and effort is spent on building framework that will support research and advanced technology

Long term:

• Less effort is required except for upgrades in technology

Databases

• Gene Sequences
• Drug Architecture
• Chemogenomics data

Quantum Computing

• Benevolent AI are using quantum-inspired approaches, often in combination with machine learning, and aim to achieve quicker and more-accurate drug discovery.

Assets

• Proprietary AI Drug Discovery Platform (e.g., baricitinib) for Covid-19.
• Cross Functional Expertise Team inclusive of biologists, chemists, engineers, informaticians and data scientists
• Drug discovery and development facility for clinical testing
• Partnership with top pharmaceutical companies and global research institutes
• Open Source Programme to improve data diversity
• User-friendly platform to attract contributors

Processes

• Active R&D drug programs for intelligent drug discovery
• Publishes research in distinguished scientific journals and world-renowned conferences
• Adoption of AI and machine learning technology to reduce timelines for drug discovery and improve the agility of the research process
• Acquisition of research lab to expand research space and capabilities

Priorities

• Increase partnership network
• Raise funds
• Grow highly skilled and diversify workforce
• Focus resources on the fastest growing areas of businesses