RECEPTOR.AI

RECEPTOR.AI Overview

RECEPTOR.AI is an advanced AI-navigated drug discovery platform designed to accelerate small-molecule and peptide drug discovery for pharma and biotech companies. It achieves this by integrating data-driven strategies, predictive modeling, and experimental insights within a powerful multiplatform ecosystem. This unified system provides a four-level architecture for comprehensive R&D strategy planning and execution, streamlining the entire drug development pipeline.

RECEPTOR.AI Key Features

• AI-Driven R&D Strategy and Control: Utilizes Agentic AI to select validated R&D strategies, generate detailed project plans, define decision criteria, and continuously adapt them in real-time under expert oversight. This includes dynamic risk assessment and strategic adjustments based on iterative results.
• End-to-End Drug Discovery Workflows: Configures tailored, target-specific workflows for various therapeutic modalities such as small molecules, linear and cyclic peptides, covalent inhibitors, and proximity-inducing agents. It supports diverse target classes like GPCRs, kinases, and ion channels, seamlessly integrating both computational and experimental components.
• Advanced AI Model Stack: Features a robust suite of rigorously benchmarked predictive and generative AI models to power core drug discovery tasks. These include:
  • ArtiDock: A high-throughput AI-powered docking engine for both small molecules and peptides, trained on extensive datasets.
  • DeepTAG: A next-generation model that predicts protein-protein interaction interfaces even without requiring structural templates.
  • ADMET: An industry-leading prediction engine covering over 80 ADMET endpoints, using consensus scoring for superior decision-making.
  • OffTaRGet: A comprehensive selectivity profiling tool that combines ligand-based and structure-based prediction to assess off-target risks.
• Robust Data Engine: Provides a secure environment for collecting, engineering, and analyzing project-specific data, enabling fine-tuning of AI models. It integrates public data using LLM-powered knowledge graphs, performs feature engineering to uncover hidden structure-property relationships, guides projects with data-driven insights, and enriches sparse datasets through AI-based data augmentation.
• Modality-Specific Platforms: Operates dedicated platforms for Small Molecules, Peptides, and Proximity Inducers. Each platform is equipped with tailored workflows and AI models, orchestrated by Agentic AI to meet the distinct requirements of each modality, offering complete drug candidate design capabilities.