FRAGMENTA: End-to-end Fragmentation-based Generative Model with Agentic Tuning for Drug Lead Optimization

• URL: http://arxiv.org/abs/2511.20510v2
• Date: Wed, 26 Nov 2025 02:35:22 GMT
• Title: FRAGMENTA: End-to-end Fragmentation-based Generative Model with Agentic Tuning for Drug Lead Optimization
• Authors: Yuto Suzuki, Paul Awolade, Daniel V. LaBarbera, Farnoush Banaei-Kashani,
• Abstract summary: FRAGMENTA is an end-to-end framework for drug lead optimization.<n>It includes a novel generative model that reframes fragmentation as a "vocabulary selection" problem.<n>It also includes an agentic AI system that refines objectives via conversational feedback from domain experts.
• Score: 1.5866079116942815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecule generation using generative AI is vital for drug discovery, yet class-specific datasets often contain fewer than 100 training examples. While fragment-based models handle limited data better than atom-based approaches, existing heuristic fragmentation limits diversity and misses key fragments. Additionally, model tuning typically requires slow, indirect collaboration between medicinal chemists and AI engineers. We introduce FRAGMENTA, an end-to-end framework for drug lead optimization comprising: 1) a novel generative model that reframes fragmentation as a "vocabulary selection" problem, using dynamic Q-learning to jointly optimize fragmentation and generation; and 2) an agentic AI system that refines objectives via conversational feedback from domain experts. This system removes the AI engineer from the loop and progressively learns domain knowledge to eventually automate tuning. In real-world cancer drug discovery experiments, FRAGMENTA's Human-Agent configuration identified nearly twice as many high-scoring molecules as baselines. Furthermore, the fully autonomous Agent-Agent system outperformed traditional Human-Human tuning, demonstrating the efficacy of agentic tuning in capturing expert intent.

Related papers

• MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning [53.37068897861388]
  MedSAM-Agent is a framework that reformulates interactive segmentation as a multi-step autonomous decision-making process.<n>We develop a two-stage training pipeline that integrates multi-turn, end-to-end outcome verification.<n>Experiments across 6 medical modalities and 21 datasets demonstrate that MedSAM-Agent achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-02-03T09:47:49Z)
• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• ARM: Role-Conditioned Neuron Transplantation for Training-Free Generalist LLM Agent Merging [51.409102048965394]
  Agent-Role Merging (ARM) is an activation-guided, role-conditioned neuron transplantation method for model merging in LLM agents.<n>ARM improves existing merging methods from static natural language tasks to multi-turn agent scenarios.
  arXiv  Detail & Related papers  (2026-01-12T08:31:53Z)
• Democratizing Drug Discovery with an Orchestrated, Knowledge-Driven Multi-Agent Team for User-Guided Therapeutic Design [0.0]
  OrchestRA is a human-in-the-loop multi-agent platform that unifies biology, chemistry, and pharmacology.<n>Unlike static code generators, our agents actively execute simulations and reason the results to drive iterative optimization.
  arXiv  Detail & Related papers  (2025-12-25T11:03:04Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning [84.70211451226835]
  Large Language Model (LLM) Agents are constrained by a dependency on human-curated data.<n>We introduce Agent0, a fully autonomous framework that evolves high-performing agents without external data.<n>Agent0 substantially boosts reasoning capabilities, improving the Qwen3-8B-Base model by 18% on mathematical reasoning and 24% on general reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-11-20T05:01:57Z)
• AutoLabs: Cognitive Multi-Agent Systems with Self-Correction for Autonomous Chemical Experimentation [0.10999592665107412]
  AutoLabs is a self-correcting, multi-agent architecture designed to autonomously translate natural-language instructions into executable protocols.<n>We present a comprehensive evaluation framework featuring five benchmark experiments of increasing complexity.<n>Our results demonstrate that agent reasoning capacity is the most critical factor for success.
  arXiv  Detail & Related papers  (2025-09-30T01:51:46Z)
• STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning [54.28691219536054]
  We introduce STARec, a slow-thinking augmented agent framework that endows recommender systems with autonomous deliberative reasoning capabilities.<n>We develop anchored reinforcement training - a two-stage paradigm combining structured knowledge distillation from advanced reasoning models with preference-aligned reward shaping.<n>Experiments on MovieLens 1M and Amazon CDs benchmarks demonstrate that STARec achieves substantial performance gains compared with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-08-26T08:47:58Z)
• FROGENT: An End-to-End Full-process Drug Design Agent [19.025736969789566]
  Powerful AI tools for drug discovery reside in isolated web apps, desktop programs, and code libraries.<n>To address this issue, a Full-pROcess druG dEsign ageNT, named FROGENT, has been proposed.<n>FROGENT utilizes a Large Language Model and the Model Context Protocol to integrate multiple dynamic biochemical databases, tool libraries, and task-specific AI models.
  arXiv  Detail & Related papers  (2025-08-14T15:45:53Z)
• MCP-Orchestrated Multi-Agent System for Automated Disinformation Detection [84.75972919995398]
  This paper presents a multi-agent system that uses relation extraction to detect disinformation in news articles.<n>The proposed Agentic AI system combines four agents: (i) a machine learning agent (logistic regression), (ii) a Wikipedia knowledge check agent, and (iv) a web-scraped data analyzer.<n>Results demonstrate that the multi-agent ensemble achieves 95.3% accuracy with an F1 score of 0.964, significantly outperforming individual agents and traditional approaches.
  arXiv  Detail & Related papers  (2025-08-13T19:14:48Z)
• DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration [24.65716292347949]
  DrugAgent is a multi-agent framework that automates machine learning (ML) programming for drug discovery tasks.<n>Our results show that DrugAgent consistently outperforms leading baselines.
  arXiv  Detail & Related papers  (2024-11-24T03:06:59Z)
• DrugAgent: Multi-Agent Large Language Model-Based Reasoning for Drug-Target Interaction Prediction [8.98329812378801]
  DrugAgent is a multi-agent system for drug-target interaction prediction.<n>It combines multiple specialized perspectives with transparent reasoning.<n>Our approach provides detailed, human-interpretable reasoning for each prediction.
  arXiv  Detail & Related papers  (2024-08-23T21:24:59Z)
• Fleet of Agents: Coordinated Problem Solving with Large Language Models [10.167121757937062]
  Fleet of Agents (FoA) is a principled framework utilizing large language models as agents to navigate through dynamic tree searches.<n>FoA spawns a multitude of agents, each exploring the search space autonomously, followed by a selection phase.<n>FoA achieves the best cost-quality trade-off among all benchmarked methods and FoA + LMA3.2-11B surpasses the Llama3.2-90B model.
  arXiv  Detail & Related papers  (2024-05-07T09:36:23Z)
• Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis [55.30328162764292]
  Chemist-X is a comprehensive AI agent that automates the reaction condition optimization (RCO) task in chemical synthesis.<n>The agent uses retrieval-augmented generation (RAG) technology and AI-controlled wet-lab experiment executions.<n>Results of our automatic wet-lab experiments, achieved by fully LLM-supervised end-to-end operation with no human in the lope, prove Chemist-X's ability in self-driving laboratories.
  arXiv  Detail & Related papers  (2023-11-16T01:21:33Z)
• Retrieval-based Controllable Molecule Generation [63.44583084888342]
  We propose a new retrieval-based framework for controllable molecule generation. We use a small set of molecules to steer the pre-trained generative model towards synthesizing molecules that satisfy the given design criteria. Our approach is agnostic to the choice of generative models and requires no task-specific fine-tuning.
  arXiv  Detail & Related papers  (2022-08-23T17:01:16Z)
• Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective [9.371378627575883]
  Domain-aware machine learning (ML) models have been increasingly adopted for accelerating small molecule therapeutic design. We present the most recent breakthrough achieved by each of the components, and how such autonomous AI and ML workflow can be realized.
  arXiv  Detail & Related papers  (2021-02-10T00:43:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.