Unlocking the Potential of Artificial Intelligence in Pharma Research and Development: Insights from Investor and Researcher Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The integration of artificial intelligence into drug discovery processes represents a major innovation in pharmaceutical research and development. This study investigates the role of AI investments in enhancing research efficiency, addressing implementation challenges, and shaping stakeholder perspectives. Via a structured explanatory research design, the study applies a quantitative methodology based on survey data collected from researchers, investors, and pharmaceutical executives across the USA and United Kingdom. The questionnaire examined respondents’ experiences with artificial intelligence tools, investment patterns, and perceived research outcomes. Statistical methods such as logistic regression and chi-square tests were employed to analyze correlations between investment strategies and research efficiency. Findings indicate that while artificial intelligence improves productivity – in predictive modeling and data analysis – barriers such as high infrastructure costs, inadequate training, and regulatory uncertainty persist. Notably, 70% of participants plan to increase AI investments within the next five years, and 80% regard artificial intelligence as essential or very important to the future of drug discovery. However, successful implementation appears to correlate with firm size and access to technical resources, suggesting disparities in AI readiness across the industry. Recommendations include expanding artificial intelligence training programs, strengthening infrastructure, and fostering closer collaboration between investors and researchers. Ethical considerations, including data privacy and regulatory compliance, are also emphasized. The pilot study provides foundational insights for a full-scale investigation and offers practical guidance for optimizing artificial intelligence integration in pharmaceutical research and development.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it