platoseed
Multi-targeted therapeutics to treat complex diseases
We are a drug discovery company developing a new generation of therapeutics that embrace the complexity of disease. Currently available approaches for creating drugs work on the principle of finding one drug-one protein 'magic bullets'. However, diseases such as cancer and autoimmunity are often the result of several dysregulated proteins across many distinct biological pathways. We are building a computational-experimental platform to design therapeutics that can target several disease-causing proteins at once. By designing multi-specific drugs, we are able to create therapeutics that are more efficacious and safer than existing medicines.
Harmonic Discovery develops next-generation therapeutics using a generative chemistry approach to tune out off-target effects and enable multi-target drug design. They combine machine learning and precision pharmacology to address disease complexity.
The platform uses multi-layer information integration—from protein sequence mutations to 3D conformations and gene expression changes—to identify molecule modifications that reduce toxic off-targets while enabling additional therapeutic targets. It supports multi-targeted drug discovery by engineering medicines with precision pharmacology through data-driven predictions of compound-kinase bioactivity and alignment with medicinal chemist preferences.
Who it’s for: Biotech and pharmaceutical companies seeking precision, multi-target therapeutics; medicinal chemistry teams; drug discovery programs addressing complex diseases.
Hiring/traction mentions (platform, partnerships, careers) and ongoing investor backing implied by phrasing; no explicit numbers.
I am a chemist with training in Medicinal, Organic, Computational, and Peptide chemistry. In my 25+ years in the field, I carried out drug discovery projects involving small molecules and peptides. I worked in environments of startup and global large pharma companies where I led teams and departments in all aspects of early drug discovery. I currently focus on merging experience-based knowledge of molecular optimization with ML/AI systems.

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