Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:11:15.102648Z
Paper Citation Record · LEDGER
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle
As of 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 3 inbound Pith citation observations for arXiv:2507.09023.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T16:11:28.623850Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
19 of 19 outbound references displayed
- verified exact0
- verified fuzzy19
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
Observation 1918a0c4-0c09-42ca-b7d3-6649eac95aa4 · outbound
This paper cites Artificial intelligence in drug development.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Artificial intelligence in drug development
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:17.428196Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c0f735aa-f5c6-41ee-8e3e-5f230c3195d3 · outbound
This paper cites Academic drug discovery units in the uk: Progress and challenges.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Academic drug discovery units in the uk: Progress and challenges
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:17.275380Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 58efb7b2-1494-47ec-b20c-3c56b1131db1 · outbound
This paper cites Harnessing network pharmacology in drug discovery: an integrated approach.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Harnessing network pharmacology in drug discovery: an integrated approach
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:17.163543Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 585b1751-a7d5-44a6-9abb-f177eefb8d40 · outbound
This paper cites The strategies and politics of successful design, make, test, and analyze (dmta) cycles in lead generation.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle The strategies and politics of successful design, make, test, and analyze (dmta) cycles in lead generation
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:17.129897Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation bcef0d53-76d5-4800-a8f0-b74e4b0ad71b · outbound
This paper cites Overcoming dmta cycle challenges: A unified ai-driven system for efficient drug design.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Overcoming dmta cycle challenges: A unified ai-driven system for efficient drug design
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:16.999847Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation cf2dabc2-7763-4bdb-adc1-4e840d03acbf · outbound
This paper cites Aug- menting dmta using predictive ai modelling at astrazeneca.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Aug- menting dmta using predictive ai modelling at astrazeneca
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:16.907187Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3fcb6027-5857-4c06-90bb-f0bbc1a1aa78 · outbound
This paper cites Computational chemistry in drug lead discovery and design.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Computational chemistry in drug lead discovery and design
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:16.771457Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b71e8442-d6a1-49fd-a535-b1bed65f1f2f · outbound
This paper cites Deep learning methods for small molecule drug discovery: A survey.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Deep learning methods for small molecule drug discovery: A survey
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:16.619876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 17b82380-5bb6-4420-ab35-79b873a79c4e · outbound
This paper cites Identifying rna-small molecule binding sites using geometric deep learning with language models.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Identifying rna-small molecule binding sites using geometric deep learning with language models
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:16.450208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 88e0c54c-1510-47e4-90b2-a7d91712d775 · outbound
This paper cites Computational drug design: a guide for computational and medicinal chemists.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Computational drug design: a guide for computational and medicinal chemists
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:16.321930Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 57ef052e-1fd6-4f5e-bc24-af7e7a2a3959 · outbound
This paper cites Current status of computational approaches for small molecule drug discovery, 2024.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Current status of computational approaches for small molecule drug discovery, 2024
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:16.247281Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 1bb11e5c-2095-4801-870c-b9646006ff7f · outbound
This paper cites Computational methods in drug discovery.Beilstein journal of organic chemistry, 12(1):2694–2718, 2016.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Computational methods in drug discovery.Beilstein journal of organic chemistry, 12(1):2694–2718, 2016
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:16.048660Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation bd312066-5dd8-4cd9-8d18-d22d3ebd5bee · outbound
This paper cites Uncovering bottlenecks and optimizing scientific lab workflows with cycle time reduction agents, 2025.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Uncovering bottlenecks and optimizing scientific lab workflows with cycle time reduction agents, 2025
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:15.906133Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ce514170-bbbe-40a3-8a9e-73d50bc67ca9 · outbound
This paper cites The role of agentic ai in shaping a smart future: A systematic review.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle The role of agentic ai in shaping a smart future: A systematic review
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:15.829556Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation d443a985-5833-4a26-aaf9-d05bd51feb4d · outbound
This paper cites Industrial agentic ai and generative modeling in complex systems.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Industrial agentic ai and generative modeling in complex systems
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:15.696132Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b8bed849-92f8-481f-8c4e-1adf950bc648 · outbound
This paper cites From prompt to platform: an agentic ai workflow for healthcare simulation scenario design.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle From prompt to platform: an agentic ai workflow for healthcare simulation scenario design
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:15.572022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 662f03c2-f224-4009-9c0f-850be76dd2cc · outbound
This paper cites Comprehensive review of artificial general intelligence agi, agentic ai and genai: Current trends and future directions.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Comprehensive review of artificial general intelligence agi, agentic ai and genai: Current trends and future directions
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:15.481752Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation cb373dfd-54e9-428f-abe1-35484cdfe6cc · outbound
This paper cites Accelerating drug discovery with artificial: a whole-lab orchestration and scheduling system for self-driving labs, 2025.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle Accelerating drug discovery with artificial: a whole-lab orchestration and scheduling system for self-driving labs, 2025
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:15.339083Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 32f035b3-b2e1-4142-8abb-3cf892612afc · outbound
This paper cites What would you like to do today?.
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle What would you like to do today?
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:11:15.261277Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Pith citing papers
Observation 94962245-e8cb-40ac-9c41-6016700041c9 · inbound
Technical Implementation of Tippy: Multi-Agent Architecture and System Design for Drug Discovery Laboratory Automation cites this paper.
Technical Implementation of Tippy: Multi-Agent Architecture and System Design for Drug Discovery Laboratory Automation Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:11:28.623850Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d28a890e-ab4c-400c-98a3-d424601170f1 · inbound
FLARE: Agentic Coverage-Guided Fuzzing for LLM-Based Multi-Agent Systems cites this paper.
FLARE: Agentic Coverage-Guided Fuzzing for LLM-Based Multi-Agent Systems Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T20:05:45.690657Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3db628a7-eff4-49a5-9629-a50ed5f15773 · inbound
MolLingo: Molecule-Native Representations for LLM-Powered Scientific Agents cites this paper.
MolLingo: Molecule-Native Representations for LLM-Powered Scientific Agents Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T12:53:26.812959Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.