Developing a framework for understanding and enabling open source drug discovery
Bibliographic record
Abstract
Open source drug discovery is increasingly being sought as a solution for managing product development complexities. Three drivers encouraging the use of the open source strategy include: upstream knowledge-based complexities associated with complementary assets, technological complexities given the scale of research and interdependencies between disciplines and downstream commercialization complexities. While literature currently discusses the need for open source strategies and their outcomes, we have reached a critical stage for a framework to cohesively understand how the drivers affect the open source models chosen as well as the governance strategies to ensure a successful outcome both in terms of knowledge access and product development. In this paper, an initial framework is designed with a focus on the type of participant as impacting the motivation to participate in an open source initiative, the objective of any open source strategy as impacting the structural model adopted and the structure of knowledge produced as impacting its management. It is anticipated that this framework should then provide an opportunity to develop governance rules for open source drug discovery initiatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.022 | 0.031 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".