Receptor provides a modality‑agnostic AI platform, PharmaSphere, that accelerates therapeutic design across small molecules, peptides, and proximity‑inducing biologics. The ecosystem combines AI‑driven pocket identification, de novo binder design, automated SAR analysis, and multiparametric virtual screening with extensive ADMET and selectivity prediction to enable rapid, data‑rich lead optimization for pharma and biotech R&D teams.
Funding
$0 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.
Founders
Product
Problem
Designing effective therapeutics is hampered by the vast chemical and biological space, ambiguous binding pockets, and the need to balance potency, selectivity, and ADMET properties across multiple modality types. Traditional workflows rely on iterative synthesis and testing, leading to long timelines and high attrition rates.
Solution
Receptor AI offers a modality‑agnostic, AI‑accelerated ecosystem—PharmaSphere—that integrates dozens of experimentally validated models to support rapid design of small molecules, peptides, and proximity‑inducing biologics. The platform generates virtual chemical space containing trillions of synthesizable compounds, identifies conventional and cryptic pockets, and performs structure‑based or ligand‑based virtual screening without requiring known ligands. Integrated multi‑task ADMET and polypharmacology predictors evaluate safety, efficacy, and selectivity across thousands of human proteins. Users can iteratively optimize leads through automated SAR analysis, multiparametric screening, and selectivity tuning, while a scalable cloud infrastructure provides parallel compute resources for large‑scale projects.
Target Audience
Primary customers are pharmaceutical and biotech R&D teams seeking AI‑enabled design of small‑molecule, peptide, or biologic therapeutics, as well as contract research organizations that run large‑scale discovery projects.
Features
- AI‑driven pocket identification covering allosteric, hidden, transient, and cryptic sites for any target class
- De novo binder design for peptides using a library of 10 K+ non‑canonical amino acids and non‑peptide blocks
- Proximity‑inducer and molecular‑glue design tools that predict ternary complex structures without homology templates
- Multiparametric virtual screening of billions of compounds with 32 phys‑chem filters and 40+ ADMET endpoints
- Automated SAR analysis and binding‑mode prediction using hybrid intelligence models
- Selectivity optimization against highly similar isoforms, mutants, and ~17 K human proteins
- Scalable hybrid‑cloud infrastructure enabling parallel workloads and automated data handling