Advancing Drug Discovery with Deep Learning

Genesys: Beyond Single-Modal Screening

A Multi-Unimodal Transformer model that unifies dynamic ligand-receptor interactions and structural data for virtual screening.

Our state-of-the-art Multi-Unimodal Architecture rethinks drug discovery by analyzing multiple types of molecular interactions simultaneously. 

By processing everything from structural properties to dynamic binding patterns, we enable superior prediction accuracy while using fewer computational resources than traditional approaches.

Earlier Predictions
– Identifies patterns across multiple molecular features
– Catches binding insights others miss
– Excels with limited training data
– Accelerates time to lead identification

Discovery Efficiency
– Processes multiple molecular representations simultaneously
– Eliminates redundant screening steps
– Integrates with existing discovery pipelines

Implement Deep Learning in Drug Discovery

Supporting custom, exploratory Deep Learning for Virtual Screening

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Target Identification & Validation

  • AI-driven genomics data analysis
  • Natural language processing for literature mining

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Hit Discovery & Optimization

  • AI-powered virtual screening
  • Structure-based virtual screening using deep learning
  • Machine learning for ADMET prediction
  • Molecular dynamics simulations with GPU acceleration

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Lead Optimization & Candidate Selection

  • Quantitative structure-activity relationship (QSAR) modeling using AI
  • AI-driven toxicity prediction and safety assessment

Our Articles

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