Pioneering Interpretability for AI Drug Discovery.

Update: Our Paper on our Multi-Unimodal Model architecture has been published in ISBI!
https://ieeexplore.ieee.org/document/10981182

Bioforager

Assisted Intelligent Drug Design

Genesys Model Family

Built-In Transparency by Design

Multimodal by Architecture

  • Native integration of proteins, peptides, small molecules, ligand analogs
  • MUM framework enables incremental learning across modalities
  • Self-organizing protein class selection

 Efficient by Design

  • 10x less training data than benchmarks
  • Up to > 20x lower compute budgets
  • Superior out-of-distribution generalization

Interpretable by Default

  • Built-in transparency layers
  • Native hooks for attention mapping, layer archaeology, conformational analysis
  • Seamless integration with Circe’s confidence cartography

Circe

Agentic Intelligence that Delivers Insight to How AI 'Thinks'

Implement at Every Part of Your Work Flow

Neuralgap supports custom deep learning builds for Pharma clients.

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Structure Evaluation

  • Target protein selection & validation
  • Mutation-aware binding site analysis
  • Conformational dynamics assessment

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Hit Discovery & Optimization

  • Binding affinity prediction (sub-1.0 MSE)
  • Pose generation with uncertainty maps
  • SAR-guided analog prioritization

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Lead Optimization & Candidate Selection

  • Structure-based screening with interpretability
  • Ligand analog testing & ranking
  • Atomic-level modification assessment
  • False positive risk navigation

Our Articles

Follow our article series to keep track of the latest advances in AI in bio and cheminformatics

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