Adapting artificial intelligence into the evolution of pharmaceutical sciences and publishing: Technological darwinism
Bibliographic record
Abstract
This article explores the application of Artificial Intelligence (AI) in pharmaceutical research and development, particularly in the use of language models such as Chat Generative Pre-Trained Transformer (GPT). The evolution of technology and scientific publishing is discussed, highlighting the need for adaptability and change. The advancements in pharmaceutical science over the years are outlined, including the contribution of techniques such as combinatorial chemistry and pharmacogenomics. The potential benefits of AI models in pharmaceutical research and development are explored, such as assisting in literature reviews, data analysis, modeling, and interpretation, as well as aiding in the drug discovery and development processes. However, the limitations and potential biases of these tools are also recognized, and the importance of using them appropriately and in conjunction with human expertise and critical thinking is emphasized. The article also discusses the need for pharmaceutical researchers and practitioners to carefully evaluate the output of AI models, checking for accuracy and potential biases, and to incorporate this information into their published work thoughtfully. Guidelines and policies regarding the use of AI models in scientific publications are suggested, highlighting the importance of rigor and transparency in research. Authors are encouraged to stay up-to-date with current research and guidelines and collaborate closely with journal editors and reviewers to ensure appropriate and transparent use of AI models. The article concludes with a reflection on the ongoing evolution of technology and the need for researchers to stay updated with the latest advancements and best practices to succeed. The importance of adaptability and change is highlighted, and Charles Darwin's theory of evolution and natural selection is used to emphasize the relevance of this concept in the context of AI and pharmaceutical research. Overall, the article presents a thought-provoking and informative discussion of the potential benefits and limitations of AI language models in pharmaceutical research and development and the need for appropriate and effective use of these tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".