PharmaSphere

Receptor.ai's AI Drug Design Platform, built around the PharmaSphere ecosystem, is a state-of-the-art AI-accelerated drug discovery solution designed for pharmaceutical researchers and biotechnology organizations seeking rapid, precise, and flexible drug candidate design. The platform combines dozens of experimentally validated AI models across dedicated modules for Proximity Inducers, Peptides, and Small Molecules, integrating LLM-powered workflows for molecular docking, target validation, and drug selectivity enhancement.

PharmaSphere is distinguished by four core principles: speed through 40+ advanced AI methodologies, atom-level precision via a holistic molecular design approach, customizability through modular platform architecture, and experimental validation through integration of biological assays to ensure high-quality outputs.

Chemical Space Preprocessing

• Novel Chemical Space Generation: Generate virtual chemical spaces containing trillions of synthesizable compounds using structure-based AI methodologies and chemical synthesis simulation techniques.
• Large Chemical Libraries: Begin with your own compound library or select from an extensive range of commercial libraries, each containing billions of compounds with an 80% synthesizability rate.
• Optimised Screening Map: Perform clusterization and navigation across large datasets to enable fast and effective screening of bioactive compounds.
• Drug-Like Chemical Space Filtering: Filter compounds using a comprehensive set of 32 physicochemical and drug-like properties to focus on the most relevant candidates.

Massive Virtual Screening

• Protein Structure Preparation: Define protein targets and binding pockets by providing an amino acid sequence or an available 3D structure.
• Protein-Ligand Preparation: Conduct protein structure pre-processing and optimization, including chemical data augmentation and 2D/3D similarity filtering.
• Ligand- and Structure-Based Virtual Screening: AI-enabled virtual screening identifies exceptionally promising bioactive compounds without requiring known ligands as templates, opening up previously unexplored areas of chemical space and enabling the discovery of diverse and novel molecular structures.

Precision Screening

• Safety Assessment: An innovative multi-task ADME-Tox Prediction System comprising 40+ AI pharmacokinetic endpoints provides comprehensive safety profiling of candidate compounds.
• Polypharmacology and Selectivity: Advanced ligand- and structure-based multi-target profiling and selectivity assessment performed against a panel of 17,000+ human proteins.
• Efficacy Evaluation: Top compounds are identified using quantitative structure-activity relationship (QSAR) assessment combined with physics-based simulations.
• Candidate Compound Profiling: Final candidates are evaluated across six key dimensions — safety, efficacy, affinity, selectivity, stability, and bioavailability — to ensure well-rounded drug-like profiles.

Scalable Hybrid Cloud Infrastructure

• Speed: The cloud infrastructure management system provides access to unlimited computing resources and enables multiple drug discovery workloads to run in parallel, compressing large-scale projects into a matter of days.
• Automation: The modular platform architecture automates heterogeneous data storage, collection, and processing across the entire workflow.
• Scalability: Flexible infrastructure configuration allows virtually unrestricted scaling up or down to match the demands of any drug discovery project.
• Security: The platform employs cutting-edge technologies to strictly maintain software security, data networking integrity, and secure information storage.

The PharmaSphere platform is delivered via a scalable hybrid cloud infrastructure, making it suitable for both focused research programs and large-scale drug discovery campaigns. Its modular, customizable architecture allows teams to fine-tune workflows to their specific therapeutic area or project requirements, while experimental validation ensures that AI-generated outputs translate into high-quality, actionable drug candidates.