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| # 🗺️ 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. | |
| --- | |
| ## 🏗️ Phase 1: Infrastructure & Core Framework ✅ COMPLETE | |
| **Goal:** Establish the "modality-agnostic" foundation so tools can be plugged in without rewriting core logic. | |
| - [x] **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` | |
| - [x] **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 | |
| - [x] **Configuration Schema** (`bioflow/core/config.py`): | |
| - `NodeConfig`: Single pipeline node definition | |
| - `WorkflowConfig`: Complete workflow definition | |
| - `BioFlowConfig`: Master system configuration | |
| - YAML-compatible dataclasses | |
| - [x] **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 | |
| - [x] **Sample Workflows** (`bioflow/workflows/`): | |
| - `drug_discovery.yaml`: Encode → Retrieve → Predict → Filter | |
| - `literature_mining.yaml`: Cross-modal literature search | |
| --- | |
| ## 🧪 Phase 2: Parallel Tool Implementation ✅ COMPLETE | |
| The team works on their respective modules using the core interfaces. | |
| ### **1. OBM Integration** ✅ | |
| - [x] `OBMEncoder` - Unified multimodal encoder (`bioflow/plugins/obm_encoder.py`) | |
| - [x] `TextEncoder` - PubMedBERT/SciBERT (`bioflow/plugins/encoders/text_encoder.py`) | |
| - [x] `MoleculeEncoder` - ChemBERTa/RDKit (`bioflow/plugins/encoders/molecule_encoder.py`) | |
| - [x] `ProteinEncoder` - ESM-2/ProtBERT (`bioflow/plugins/encoders/protein_encoder.py`) | |
| - [x] Lazy loading for efficient memory usage | |
| - [x] Dimension projection for cross-modal compatibility | |
| ### **2. Qdrant Retriever** ✅ | |
| - [x] `QdrantRetriever` implements `BioRetriever` interface (`bioflow/plugins/qdrant_retriever.py`) | |
| - [x] HNSW indexing with cosine/euclidean/dot distance | |
| - [x] Payload filtering (species, experiment type, modality) | |
| - [x] Batch ingestion support | |
| - [x] In-memory, local, or remote Qdrant connections | |
| ### **3. DeepPurpose Predictor** ✅ | |
| - [x] `DeepPurposePredictor` implements `BioPredictor` (`bioflow/plugins/deeppurpose_predictor.py`) | |
| - [x] DTI prediction with Transformer+CNN architecture | |
| - [x] Graceful fallback when DeepPurpose unavailable | |
| - [x] Batch prediction support | |
| --- | |
| ## 🔗 Phase 3: The Unified Workflow ✅ COMPLETE | |
| **Goal:** Connect the tools into a coherent discovery loop. | |
| - [x] **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 | |
| - [x] **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 | |
| - [x] **Data Ingestion Utilities** (`bioflow/workflows/ingestion.py`): | |
| - JSON/CSV file loaders | |
| - SMILES/FASTA file parsers | |
| - Sample data generators for testing | |
| - [x] **Evidence Traceability**: | |
| - Automatic PubMed/UniProt/PubChem/DrugBank link generation | |
| - Metadata preservation through pipeline | |
| **Verification:** `python scripts/verify_phase3.py` - All 5 tests pass ✅ | |
| --- | |
| ## 📊 Phase 4: UI/UX & Deployment ✅ COMPLETE | |
| **Goal:** Build an intuitive, modern interface for the BioFlow platform. | |
| - [x] **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` | |
| --- | |
| ## 🚀 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. | |