ReviewDrug discovery today2026
A statewide multi-institutional model that de-risks academic drug discovery.
Review in Drug discovery today, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Texas has built a coordinated, multi-institution core network that de-risks academic discoveries while reducing redundant rebuilds across institutions. Through a distributed drug R&D model, autonomous cores collaborate via shared forums and referrals, making handoffs routine and providing industry-grade capabilities within academic settings. By preserving local expertise while enabling cross-core progression when data warrant, the network has delivered measurable outcomes: in a 2024 survey of ten cores, >750 projects entered at least one core, >70 traversed multiple cores, ∼25 patents were filed, >60 startups launched and 15 programs reached the clinic; projects also attracted >US$186M in non-dilutive and >US$400M in dilutive capital. Together, these results demonstrate that collaboration at scale through a distributed core model can deliver meaningful translational impact.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.