---
title: "TRACEDD: Tool-First Multi-Agent Framework for Explainable Drug Design"
id: "biorxiv-7-tracedd-a-tool-grounded-reasoning-and-agentic-coordination-for-explainable-drug"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-7-tracedd-a-tool-grounded-reasoning-and-agentic-coordination-for-explainable-drug"
content_type: "clinical_feed_article"
specialty: "Pharmacology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.12.751167v1?rss=1"
published_at: "2026-09-18T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# TRACEDD: Tool-First Multi-Agent Framework for Explainable Drug Design
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-7-tracedd-a-tool-grounded-reasoning-and-agentic-coordination-for-explainable-drug
- **Specialty:** [Pharmacology](https://medichelpline.com/clinical-feed/pharmacology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.12.751167v1?rss=1)
- **Published At:** 2026-09-18T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- TRACEDD is a framework that integrates large language models (LLMs) with validated computational tools in a **tool-first** architecture to support explainable, human-verifiable drug discovery workflows. - The system uses a multi-agent design that maps to expert discovery teams and operates through a transparent **Reason-Act-Observe** loop to make decisions traceable and evidence-linked. - TRACEDD decomposes the discovery pipeline into specialized agents for **target validation**, druggability assessment, molecular generation, lead optimization, **ADMET** evaluation, literature evidence integration and retrosynthesis. - The framework emphasizes orchestration of existing domain tools rather than replacing them, preserving scientific rigor by linking each decision to explicit tool invocation and intermediate evidence. - TRACEDD demonstrates an end-to-end workflow capable of handling real-world data variability, including invoking AlphaFold when experimental protein structures are unavailable. - As a representative case, the authors applied the system to **JAK2**, retrieving experimental structures, identifying druggable pockets, and performing de novo molecular generation guided by known JAK2 inhibitors. - Reward signals in the molecular generation and prioritization step include docking scores, predicted **pIC50**, physicochemical properties and **ADMET** predictions; reinforcement learning is used to guide generation based on these signals. - The preprint frames TRACEDD as a foundation for transparent, adaptable, and human-verifiable AI-assisted drug discovery by combining agentic orchestration with domain-specific computational tools. - All authors are employed at Tata Consultancy Services Ltd.; the article is a bioRxiv preprint and has not undergone peer review.
## Clinical Analysis & Structured Key Points
TRACEDD: A Tool-grounded Reasoning and Agentic Coordination for Explainable Drug Design | bioRxiv Skip to main content New Results TRACEDD: A Tool-grounded Reasoning and Agentic Coordination for Explainable Drug Design Sarveswara Rao Vangala , View ORCID Profile Vishnu Vardhan Kasturi , View ORCID Profile Navneet Bung , View ORCID Profile Arijit Roy doi: https://doi.org/10.64898/2026.09.12.751167 Sarveswara Rao Vangala 1 TCS Research (Life Sciences division), Tata Consultancy Services; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vishnu Vardhan Kasturi 1 TCS Research (Life Sciences division), Tata Consultancy Services; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vishnu Vardhan Kasturi Navneet Bung 1 TCS Research (Life Sciences division), Tata Consultancy Services; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Navneet Bung Arijit Roy 2 Tata Consultancy Services Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Arijit Roy For correspondence: roy.arijit3{at}tcs.com Abstract Info/History Metrics Preview PDF Abstract Drug discovery depends on coordinated decisions across target validation, structure analysis, molecular design, developability assessment and synthetic feasibility, but current computational methods often operate as disconnected tools. Here, we introduce TRACEDD (Tool-grounded Reasoning and Agentic Coordination for Explainable Drug Design), a framework that makes three primary contributions: (1) It establishes a 'tool-first' multi agentic architecture where LLMs orchestrate validated computational tools rather than replace them, ensuring scientific rigor. (2) It implements a multi-agent system that mirrors expert discovery teams, enabling transparent and traceable decision-making through a Reason-Act-Observe loop. (3) It demonstrates an end-to-end workflow, from target validation to synthesis planning, that adaptively handles real-world data variability, such as the absence of experimental structures. The framework decomposes discovery into specialized agents for target validation, druggability assessment, molecular generation, lead optimization, ADMET evaluation, literature evidence integration and retrosynthesis, all operating through a Reason Act Observe workflow. Using JAK2 as a representative case, we show that the system can retrieve experimental protein structures, invoke AlphaFold when structures are unavailable, identify druggable pockets and perform de novo molecular generation. Known JAK2 inhibitors are used to define design hypotheses and guide reinforcement learning-based molecular generation, with docking scores/predicted pIC50 and other physicochemical/ADMET properties serving as reward and prioritization signals. The framework demonstrates a tool-first, reasoning-driven approach in which each major decision is linked to explicit tool invocation, intermediate evidence. By combining agentic orchestration with domain-specific computational tools, the system supports transparent, adaptable and human-verifiable molecular design workflows, providing a foundation for more reliable AI-assisted drug discovery. Competing Interest Statement All the authors are employed at Tata Consultancy Services Ltd. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Back to top Previous Next Posted September 18, 2026. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following TRACEDD: A Tool-grounded Reasoning and Agentic Coordination for Explainable Drug Design Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share TRACEDD: A Tool-grounded Reasoning and Agentic Coordination for Explainable Drug Design Sarveswara Rao Vangala , Vishnu Vardhan Kasturi , Navneet Bung , Arijit Roy bioRxiv 2026.09.12.751167; doi: https://doi.org/10.64898/2026.09.12.751167 Share This Article: Copy Citation Tools TRACEDD: A Tool-grounded Reasoning and Agentic Coordination for Explainable Drug Design Sarveswara Rao Vangala , Vishnu Vardhan Kasturi , Navneet Bung , Arijit Roy bioRxiv 2026.09.12.751167; doi: https://doi.org/10.64898/2026.09.12.751167 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Areas All Articles Animal Behavior and Cognition (8010) Biochemistry (18733) Bioengineering (14876) Bioinformatics (44392) Biophysics (22588) Cancer Biology (19713) Cell Biology (26896) Clinical Trials (138) Developmental Biology (13961) Ecology (21001) Epidemiology (2067) Evolutionary Biology (25436) Genetics (16162) Genomics (23498) Immunology (18693) Microbiology (42451) Molecular Biology (18054) Neuroscience (93419) Paleontology (700) Pathology (2977) Pharmacology and Toxicology (5092) Physiology (8111) Plant Biology (15997) Scientific Communication and Education (2095) Synthetic Biology (4559) Systems Biology (10233) Zoology (2389)
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