Ai-Powered Drug Discovery: Accelerating Biomedical Research Through Computational Algorithms
DOI:
https://doi.org/10.63682/jns.v14i32S.9000Keywords:
Artificial intelligence, drug discovery, biomedical research, artificial intelligence, cost cutting, drug development, quantitative analysisAbstract
Introduction: AI is gaining more attention as a technique for drug discovery with the possible benefits of enhancing accuracy, shortening development, and decreasing expenses. It suggests that although knowledge of networks might help solve several problems in different stages of drug discovery, their effect is still unclear in terms of inter-connectedness for diverse professionals. The following research examines how artificial intelligence can enhance drug discovery processes by examining its achievements, issues, and prospects.
Methods: A cross-sectional quantitative data collection method using an online survey was used with 250 participants comprising biotechnology and pharmaceutical firms, academic researchers, and AI-based healthcare start-ups. The participants were chosen purposively, and only persons with direct experience in drug discovery or AI were selected. Descriptive statistics, regression analysis, and reliability analysis were used to analyze the data to arrive at statistical conclusions. Normality tests were conducted using the Shapiro-Wilk test, while Cronbach's alpha test established the internal consistency of the measures with Likert items.
Results: The Shapiro-Wilk test showed that the 'Impact of AI' and 'Satisfaction with AI integration' were non-normal distributions (p < 0.05). Cronbach's alpha for the selected Likert-scale items was -0.07. The result showing low internal consistency was therefore expected. Quantitative assessments based on visualizations showed that AI personnel and data scientists appreciated AI's impact more than research scientists and pharmacologists. Despite the significant focus on the transforming role of AI as the standard for drug discovery in the future, the problems of cost reduction remain crucial, as well as the issues connected with the integration of AI.
Conclusion: Significant advances in AI can potentially change the nature of drug discovery; however, it is not equally distributed across the industry. Challenges are technical, regulatory, and organizational; however, their successful surmounting will allow them to utilize the Navinfo approach's potential in decreasing costs, increasing accuracy, and shortening the terms of developing new drugs. The industry should undertake more studies and development to enhance the coordinating mechanisms between AI and drug discovery.
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