×

What does it do?

  • Drug Discovery
  • Medicinal Chemistry
  • Cheminformatics
  • Predictive Analytics
  • Molecular Simulation

How is it used?

  • Use web app to input data
  • get drug efficacy reports.
  • 1. Access web platform
  • 2. Input data
  • 3. Use simulation tools
See more

Who is it good for?

  • Pharmaceutical Researchers
  • Medicinal Chemists
  • Biotechnology Scientists
  • Drug Discovery Professionals
  • Computational Biologists

Details & Features

  • Made By

    Harmonic Discovery
  • Released On

    2021-10-24

Harmonic Discovery is a biotechnology company that leverages advanced data science, machine learning, and cheminformatics to innovate in medicinal chemistry. The company's platform utilizes generative artificial intelligence to accelerate drug discovery and development processes, offering researchers and pharmaceutical professionals powerful tools for analyzing chemical properties and biological interactions.

Key features:

- Machine Learning Models: Analyze vast datasets related to chemical properties and biological interactions to predict drug compound efficacy and safety efficiently.
- Cheminformatics Integration: Simulate and analyze molecular structures and their interactions at scale and speed to identify promising compounds early in the drug development process.
- Data Science Applications: Extract valuable insights from complex biological data to inform decision-making processes in drug design and development.
- Predictive Analytics: Forecast outcomes of drug interactions, potential side effects, and market needs to reduce time and cost associated with traditional drug development methods.

How it works:
1. Users input chemical and biological data into the web platform.
2. The platform's AI systems process and analyze the data.
3. Users utilize simulation tools for virtual testing of drug interactions and molecular behavior.
4. The system generates comprehensive reports and visualizations to aid in decision-making.

Integrations:
Various scientific databases and research tools

Use of AI:
Harmonic Discovery employs generative artificial intelligence to enhance its capabilities in drug discovery and development. The AI features analyze complex datasets, simulate molecular interactions, and provide predictive analytics for drug development outcomes.

AI foundation model:
The AI features are built on large language models (LLMs) and other advanced machine learning algorithms. These models are continuously updated to incorporate the latest research and data.

Target users:
- Researchers
- Data scientists
- Professionals in the pharmaceutical industry
- Professionals in the biotechnology industry

How to access:
Harmonic Discovery is available as a web application, providing global accessibility for users. The platform is proprietary and not open source.

  • Supported ecosystems
    Unknown
  • What does it do?
    Drug Discovery, Medicinal Chemistry, Cheminformatics, Predictive Analytics, Molecular Simulation
  • Who is it good for?
    Pharmaceutical Researchers, Medicinal Chemists, Biotechnology Scientists, Drug Discovery Professionals, Computational Biologists

Alternatives

Boston Dynamics creates advanced robots for industrial, research, and entertainment tasks.
Semantic Scholar helps researchers find and understand scientific papers using advanced search
Notably AI extracts insights from unstructured data using NLP for businesses and researchers.
Blue J predicts legal outcomes and provides research insights for tax and employment law pros.
Nextnet helps biomedical researchers discover insights from vast scientific data networks.
Meticulate automates business research, generating reports and insights for analysts and executives.
Galileo evaluates and protects generative AI applications for enterprises with metrics and tools
Argon AI streamlines clinical and commercial workflows for pharma and biotech professionals.
CoLoop analyzes qualitative research data to generate insights and presentations for consultancies
Accelerate science product development using AI and academic research insights