docs/ROADMAP.md

🗺️ BioFlow Orchestrator Development Roadmap

This roadmap outlines the collaborative development of a unified R&D platform for biological discovery using fully open-source tools and models.

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🏗️ Phase 1: Infrastructure & Core Framework ✅ COMPLETE

Goal: Establish the "modality-agnostic" foundation so tools can be plugged in without rewriting core logic.

• Core Abstractions (bioflow/core/base.py):

  • BioEncoder: Interface for vectorization (ESM-2, ChemBERTa, PubMedBERT, CLIP)
  • BioPredictor: Interface for predictions (DeepPurpose, ADMET)
  • BioGenerator: Interface for candidate generation
  • BioRetriever: Interface for vector DB operations
  • Data containers: EmbeddingResult, PredictionResult, RetrievalResult

• Tool Registry (bioflow/core/registry.py):

  • Central hub to manage multiple tools
  • Register/unregister by name
  • Default tool fallbacks
  • Utility methods for listing and summary

• Configuration Schema (bioflow/core/config.py):

  • NodeConfig: Single pipeline node definition
  • WorkflowConfig: Complete workflow definition
  • BioFlowConfig: Master system configuration
  • YAML-compatible dataclasses

• Stateful Pipeline Engine (bioflow/core/orchestrator.py):

  • BioFlowOrchestrator: DAG-based workflow execution
  • Topological sort for dependency resolution
  • ExecutionContext for state passing
  • Custom handler support
  • Error handling and traceability

• Sample Workflows (bioflow/workflows/):

  • drug_discovery.yaml: Encode → Retrieve → Predict → Filter
  • literature_mining.yaml: Cross-modal literature search

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🧪 Phase 2: Parallel Tool Implementation ✅ COMPLETE

The team works on their respective modules using the core interfaces.

1. OBM Integration ✅

• OBMEncoder - Unified multimodal encoder (bioflow/plugins/obm_encoder.py)
• TextEncoder - PubMedBERT/SciBERT (bioflow/plugins/encoders/text_encoder.py)
• MoleculeEncoder - ChemBERTa/RDKit (bioflow/plugins/encoders/molecule_encoder.py)
• ProteinEncoder - ESM-2/ProtBERT (bioflow/plugins/encoders/protein_encoder.py)
• Lazy loading for efficient memory usage
• Dimension projection for cross-modal compatibility

2. Qdrant Retriever ✅

• QdrantRetriever implements BioRetriever interface (bioflow/plugins/qdrant_retriever.py)
• HNSW indexing with cosine/euclidean/dot distance
• Payload filtering (species, experiment type, modality)
• Batch ingestion support
• In-memory, local, or remote Qdrant connections

3. DeepPurpose Predictor ✅

• DeepPurposePredictor implements BioPredictor (bioflow/plugins/deeppurpose_predictor.py)
• DTI prediction with Transformer+CNN architecture
• Graceful fallback when DeepPurpose unavailable
• Batch prediction support

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🔗 Phase 3: The Unified Workflow ✅ COMPLETE

Goal: Connect the tools into a coherent discovery loop.

• Typed Node System (bioflow/core/nodes.py):

  • EncodeNode: Vectorize inputs via BioEncoder
  • RetrieveNode: Query vector DB for similar items
  • PredictNode: Run DTI predictions on candidates
  • IngestNode: Add new data to vector DB
  • FilterNode: Score-based filtering and ranking
  • TraceabilityNode: Link results to evidence sources

• Discovery Pipelines (bioflow/workflows/discovery.py):

  • DiscoveryPipeline: Full drug discovery workflow (encode → retrieve → predict → filter → trace)
  • LiteratureMiningPipeline: Cross-modal literature search
  • ProteinDesignPipeline: Protein homolog discovery
  • Batch ingestion and simple search APIs

• Data Ingestion Utilities (bioflow/workflows/ingestion.py):

  • JSON/CSV file loaders
  • SMILES/FASTA file parsers
  • Sample data generators for testing

• Evidence Traceability:

  • Automatic PubMed/UniProt/PubChem/DrugBank link generation
  • Metadata preservation through pipeline

Verification: python scripts/verify_phase3.py - All 5 tests pass ✅

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📊 Phase 4: UI/UX & Deployment ✅ COMPLETE

Goal: Build an intuitive, modern interface for the BioFlow platform.

• Next.js Frontend (ui/):
  • Next.js 16 app router + Tailwind + shadcn/ui
  • Dashboard pages: Discovery, 3D Visualization, Workflow Builder
  • /app/api/* proxy routes to the FastAPI backend
  • Optional mock fallbacks for molecules/proteins list routes

Launch:

• Full stack (Windows): launch_bioflow_full.bat
• Manual:
  • Backend: python -m uvicorn bioflow.api.server:app --host 0.0.0.0 --port 8000
  • UI: cd ui && pnpm dev

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🚀 Phase 5: Open-Source Alignment

• Strict Open-Source Compliance: remove proprietary integrations and keep only OSS models/tools.
• Open Protein/Peptide Options: integrate open models (e.g., ESM-2 / ProGen2) behind BioGenerator.
• Open Retrieval + Evidence: improve evidence traceability (PubMed/UniProt/ChEMBL) and evaluation.