First-Principles Drug Discovery
At Denovo Sciences, we build AI-powered technologies to discover small-molecule drug candidates with greater efficiency. Our technologies allow us to work against targets where conventional and AI-based methods have failed or remain constrained.
6
Drug Discovery Programs
300+
Tested AI-generated Molecules
30%
Average Hit Rate
Science
We apply a fully rational, structure-based approach to small-molecule drug discovery. Our proprietary AI platform selects the optimal target conformation, designs molecules de novo inside the binding pocket, and prioritizes candidates that are both drug-like and synthetically accessible. By replacing trial-and-error with first-principles molecular design, we accelerate discovery and deliver higher-quality candidates ready for real-world development.
01
Rational Selection of Target Structure
Our Proteus platform systematically analyzes all available protein conformations, benchmarking each to identify the optimal structure for drug design.
Proteus Platform
02
Designing Hit Candidates
Proprietary generative algorithms design molecules de novo directly within the binding pocket, maximizing complementarity with the target.
De Novo Generation
03
Rational Selection
Synthony predicts synthesizability while our AI system evaluates 100+ features to assess drug-likeness and development potential.
Synthony + AI Assessment
Technology
Our integrated platform combines cutting-edge AI with deep scientific expertise to accelerate the drug discovery process.
Team
Hovakim
Zakaryan
Co-Founder, CEO
Vardan
Harutyunyan
Co-founder, CAIO
Mher
Matevosyan
Co-Founder, CTO
Partnerships
News and Publications
Multi-target computational pipeline for discovery of pan-influenza neuraminidase
In this study, we developed a multi-target computational pipeline to discover broad-spectrum influenza neuraminidase inhibitors. By screening nearly 500,000 compounds and applying cross-validation across influenza A and B neuraminidase subtypes, we identified promising candidates with stable pan-influenza binding profiles.
Data-driven discovery of chemical signatures for developing new inhibitors against human influenza viruses
In this study, we mapped the antiviral chemical space of influenza A and B by curating over 400,000 molecules from public databases. We identified key chemical signatures, promising scaffolds, and multi-target candidates that can guide the design of next-generation influenza inhibitors.
Discovery of new antiviral agents through artificial intelligence-based de novo design targeting influenza virus neuraminidase
In this foundational paper, we showed that the Denovo Platformcan generate optimized small molecules with potent anti-influenza activity in both in vitro and in vivo studies. The work provides direct proof that our AI-driven approach can move from computational design to experimentally validated antiviral candidates.
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