Early-stage medicinal chemistry and drug discovery are heavily fragmented. Researchers, students, and biotech startups frequently have to juggle disconnected tools: drawing 2D molecules in one tool, computing descriptors on web servers, uploading candidate molecules to cloud APIs for toxicity predictions, and tracking modifications across chaotic spreadsheets.

Crucially, cloud-based discovery forces researchers to expose proprietary, patent-sensitive molecular structures to external servers. We built ROGVEDA to solve this: an all-in-one, privacy-first, 100% offline-capable in-silico drug discovery and computational screening workbench.

WHAT ROGVEDA DOES:
ROGVEDA unifies the complete molecular research lifecycle into a single local-first application:

• Interactive 2D and 3D Molecular Modeling: Draw and edit structures with an embedded, customized Ketcher editor, compute instant vector thumbnails with client-side WebAssembly RDKit, and view 3D energy-minimized conformers in real-time using WebGL (3Dmol.js).
• Deterministic Cheminformatics: Calculates 17+ physicochemical and topological descriptors (MW, LogP, TPSA, Lipinski Rule of 5 violations, HBD/HBA, rotatable bonds) and assesses synthetic accessibility (Ertl SA Score 1–10) with reactive functional group alerts.
• Calibrated Machine Learning Safety and Efficacy Suite: Evaluates molecules across 6 bioactivity and toxicity endpoints (hERG Cardiotoxicity, Anti-cancer, Anti-inflammatory, Anti-microbial, Anti-oxidant, and Tox21 Mitochondrial Membrane Potential disruption). Every prediction ships with a calibrated confidence score via CalibratedClassifierCV.
• High-Throughput Batch Screening: Screen libraries of up to 500 molecules at once via CSV upload with row-level error isolation and battery/thermal power-aware pacing.
• Chemical Space and Similarity Search: Explore structural analogs via 2048-bit Morgan Fingerprint Tanimoto similarity and inspect compound distribution in precomputed 2D UMAP chemical space projections.
• Private Offline AI Copilot: Integrates with local offline LLMs (via LM Studio or llama.cpp) to synthesize structured chemistry findings, explain property trade-offs, and detect contradictions without sending any chemical data to the cloud.

WHAT MAKES IT UNIQUE:

• 100% Offline-First: Runs entirely on localhost. Proprietary chemical structures, formulas, and hypotheses never leave the user’s machine.
• Rigorous Machine Learning Standards: Trained on 230,000+ compounds from ChEMBL and Tox21 using strict Bemis-Murcko scaffold splits to prevent data leakage, paired with sigmoid probability calibration.
• Executive PDF and HTML Reporting: Export complete analytical screening reports, modification histories, and 3D visual captures in a single click.