Harmonic Discovery develops a kinase inhibitor drug platform that utilizes machine learning, structural modeling, and generative chemistry to predict clinical resistance mutations and optimize small molecule therapeutics. This approach addresses the challenge of off-target interactions and enhances drug efficacy by tuning out toxic anti-targets while incorporating beneficial secondary targets.
Funding
$8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
BCFounders
Product
Problem
Current drug discovery methods often focus on single-target interactions, leading to unexpected off-target effects and potential adverse reactions. These methods may also overlook beneficial secondary targets that could enhance drug efficacy or mitigate resistance.
Solution
Harmonic Discovery is developing a precision pharmacology platform that leverages machine learning, structural modeling, and generative chemistry to design next-generation therapeutics. The platform identifies and tunes out toxic off-target interactions while incorporating beneficial secondary targets, addressing the complexity of disease. By optimizing small molecule therapeutics, Harmonic Discovery aims to improve drug efficacy, reduce adverse effects, and overcome clinical resistance. The platform integrates multiple data types, including protein sequence mutations, 3D protein structure conformations, and protein gene expression changes, to improve compound-kinase bioactivity prediction.
Target Audience
The primary target audience includes pharmaceutical companies and research institutions focused on developing kinase inhibitor drugs with improved precision and efficacy.
Features
- Machine learning models for predicting compound-kinase bioactivity
- Generative chemistry platform for identifying modifications to tune out toxic off-targets
- Structural modeling to analyze 3D conformational changes of protein structures
- Preference optimization for aligning generative chemistry models with medicinal chemist preferences
- Multi-target drug discovery approach to incorporate beneficial secondary targets