arXiv AI
PharmAgents2: Towards Autonomous End-to-End Drug Discovery
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DrugHarness: An Evolvable Agent Harness for Computational Drug Discovery
arXiv:2610.23791v1 Announce Type: new Abstract: Computational drug discovery requires agents to compose scientific operations and adapt to changing evidence and constraints. Here we introduce DrugHar...
arXiv AI
Jun 18
TxBench-PP: Analyzing AI Agent Performance on Small-Molecule Preclinical Pharmacology
arXiv:2606. 19245v1 Announce Type: new Abstract: Artificial intelligence (AI) agents promise to accelerate drug discovery by compressing interpretation and decision-making loops, but practical deployment requires trusted evaluation on realistic program decisions.
arXiv AI
Oct 7
FRAGMENTA: Efficient End-to-end Fragmentation-based Generative Model with Agentic Tuning for Drug Lead Optimization in Small Data Regime
arXiv:2511.20510v3 Announce Type: replace Abstract: Molecule generation from extremely limited training data is a key challenge in drug discovery. Existing fragment-based methods are more suitable th...
arXiv AI
Oct 12
From Benchmark to Bench: Can Agents Survive Real-World Drug Discovery?
The paper introduces MAGI, an open modular agent that orchestrates molecular‑design tools by setting objectives, launching optimization, interpreting structure–activity relationships, and adjusting strategy. In nine retrospective lead‑optimization campaigns from three pharmaceutical companies, MAGI produced valid molecules via either direct LLM generation or delegation to REINVENT 4; LLM proposals stayed closer to local chemistry and achieved comparable or higher activity with fewer operations, while REINVENT explored broader chemical space. The success of a campaign depended on the accuracy of predictive models rather than the generation route, and a blinded evaluation showed chemists could not distinguish MAGI’s outputs from expert‑generated compounds, though the SAR reasoning was deemed plausible yet incomplete. The study positions MAGI as a coordination layer that can be plugged into existing workflows, but notes that the current ceiling for real projects is set by scorer applicability rather than tool orchestration.
arXiv AI
Oct 15
MADDIE: Multi-Agent Drug Discovery Engine
arXiv:2610.16975v1 Announce Type: new Abstract: Drug discovery remains a resource-intensive and time-consuming process, involving billions of dollars and a decade of development to advance a drug to...
arXiv AI
Jul 10
DrugGen 2: A disease-aware language model for enhancing drug discovery
arXiv:2607. 08404v1 Announce Type: cross Abstract: Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglecting the influence of disease context on target behavior and therapeutic outcomes.