WeDaita BioMedAIFrom Target to Candidate — in One Platform
An Agentic AI Platform to Accelerate Drug Discovery — three integrated agent modules, DBRA, ADDA, and CTIA, working in sequence across the full discovery pipeline.
Sequential, interconnected, compounding -- each agent feeds the next.
Three agent modules that cover the complete drug discovery workflow -- from target to candidate.
Solves the Haystack Problem -- automating evidence synthesis across 11+ databases (PubMed, UniProt, OMIM, DisGeNET and more) to generate and validate novel drug targets in hours, not months.
Bridges the Translation Gap -- automating protein, antibody, and small molecule drug design to reduce preclinical failure rates with generative chemistry and property guardrails.
Addresses Late-Stage Attrition -- AI-assisted protocol design, digital twin trial simulation, predictive patient recruitment, and portfolio optimization.
Each capability is exposed as an MCP server — a standardized, agent-callable interface. AI agents compose across services dynamically, routing each step of a workflow to the right backend.
Scientific Intelligence Layer
The core scientific MCP server. 163 tools across 41 modules covering biomedical literature, genomics, drug discovery, protein pathways, clinical data, and patent intelligence — all queryable in natural language.
Structured Data Access
Provides agents with structured access to internal and external datasets. Supports schema introspection, parameterized queries, pagination, and streaming for large result sets.
File & Artifact Management
Unified file and artifact layer for agent-generated content. Agents read inputs, write results, and retrieve versioned artifacts — reports, structures, datasets — without direct infrastructure access.
Orchestration & Pipelines
Lets agents trigger and monitor multi-step scientific workflows — from target validation pipelines to compound screening runs. Supports DAG-style step chaining with real-time status feedback.
AI Model Inference
Exposes internal and third-party AI models as callable tools. Agents invoke structure prediction, property scoring, embedding generation, or custom fine-tuned biomedical classifiers on demand.
Serverless Compute
Secure serverless execution layer for agent-authored code. Runs Python scripts, analysis notebooks, and custom compute tasks in isolated sandboxes with resource limits and automatic scaling.
We are not a chatbot.
We use a conversational interface and natural language to execute rigorous scientific work — with full source traceability and zero hallucination. Every insight is backed by evidence you can verify.
Real-time literature queries across PubMed, Semantic Scholar, CrossRef, and arXiv. Every claim traceable to primary publications with AI-enhanced relevance ranking.
Compound databases, drug-gene interactions, binding affinities (Ki/IC50/Kd), ADME property prediction, and cross-database compound cross-referencing.
Genetic variant annotation, clinical significance, GWAS associations, tissue expression from GTEx and ARCHS4 (270K+ samples), and GEO datasets.
Protein function, PPI networks, tissue-level protein expression, pathway membership, enzyme kinetics, 3D structure prediction, and druggability scoring.
Target-disease associations, pharmacogenomics guidelines, clinical trial search, adverse event reports, somatic cancer mutations, and phenotype-gene links.
Biomedical knowledge graph reasoning (Insilicom) for direct entity-entity relations with evidence, and Diffbot GraphRAG LLM for factual querying with citations.
Full-text patent search, application prosecution history, IPR/PGR proceedings, disambiguated inventor/assignee analytics, and patent litigation history.