Targeted literature searches are a fundamental part of writing clinical manuscripts that will meet the standards of high-quality journals and contribute meaningfully to evidence-based practice. When physicians write clinical manuscripts, utilizing a targeted literature search can identify high-quality, relevant, and current evidence. While a general literature review is useful, a targeted literature search is specific to the clinical question and should be completed through frameworks established, such as PICO (Population, Intervention, Comparator, Outcome) and PRISMA [1].
How to Integrate Artificial Intelligence in Drug Discovery: The Future of Pharmaceuticals
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How to Integrate Artificial Intelligence in Drug Discovery: The Future of Pharmaceuticals
With the integration of data, computational power, and algorithms, Artificial Intelligence (AI) is changing the traditional drug discovery and development model. This synergistic collaboration enhances the efficiency, accuracy, and success of drug research while reducing development times and costs. Together with machine learning (ML) and deep learning (DL), AI has made great strides in many areas of drug research, including drug characterization, target discovery and validation, small molecule drug design, and clinical trial acceleration. Specifically utilizing molecular generation approaches. [1]
This article outlines the effective use of how to integrate artificial intelligence in drug discovery future of pharmaceuticals, including the role of AI in drug discovery and the benefits of AI in drug discovery.
1.The Role of AI in Drug Discovery
1.1. Data-Driven Insights: Using AI for Predictive Modeling
- AI accepts large-scale data (genomic, proteomic, chemical) and can identify patterns faster than traditional algorithms.
- AI algorithms, for example, machine learning (ML) or deep learning (DL), can make predictions about molecular activity, toxicity, and efficacy.
1.2. Drug Target Identification and Validation
- AI accelerates the identification of potential.
- Finding the right drug targets is an important part of the drug discovery process as it defines a biological pathway and molecular mechanisms, and can be targeted to produce some therapeutic effect.
- Identification of targets is critically enabled by machine learning algorithms that enable evaluation of complex datasets of genomic, proteomic, and clinical data, to identify potential disease-associated targets and prioritize the targets that warrant further investigation.[2]
1.3. Virtual Screening and Compound Optimization
AI plays a crucial role in virtual screening for chemical libraries, optimizing compounds for better bioavailability and reducing side effects.
2. Benefits of AI in Drug Discovery
2.1. Efficiency and Speed
AI enhances efficiency and speed in drug discovery by automating time-consuming tasks like screening and data analysis.
2.2. Cost Reduction
AI enhances the success rate of failed drug trials by utilizing predictive analytics to reduce the financial burden.
3. AI Applications and Tools in Different Phases of Drug Discovery
| Phase | AI Application | AI Tools Used |
|---|---|---|
| Target Identification | AI helps in identifying potential biological targets for drug development by analysing genomic and proteomic data. | Insilico Medicine, BenevolentAI |
| Compound Screening | Virtual screening through AI-driven simulations to identify promising drug candidates from large libraries. | Atomwise, DeepChem |
| Preclinical Testing | AI models predict drug toxicity, bioavailability, and side effects based on molecular structure. | Schrodinger, Labcorp Drug Development |
| Clinical Trials | AI improves patient selection and monitors real-time data during trials to predict drug efficacy and safety. | IBM Watson Health, Tempus |
4. Different Applications of AI in Drug Discovery and Development
Figure 1: Uses of artificial intelligence (AI) in the various subfields in the pharmaceutical industry, from drug discovery to management of pharmaceutical products. Image adapted from Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1), 80–93. https://doi.org/10.1016/j.drudis.2020.10.010
5. Challenges and Limitations of AI in Drug Discovery
Despite the promising potential, there are several challenges to integrating AI in drug discovery:[4]
- Data Quality and Availability: With AI models, it is important to have a large and high-quality dataset to train the models effectively. Access to this kind of data is often not available when conducting pharmaceutical research.[5]
- Interpretability: Many AI models, especially deep learning models, are treated as “black boxes,” meaning that they are hard to reason about and to understand their decision process. This “black box” effect can present an additional challenge for regulatory approval.
- Regulatory Hurdles: The pharmaceutical industry is heavily regulated, and the use of AI in drug discovery will have to adhere to regulatory standards. There can be concerns about how AI-based decisions would be validated and accepted by regulators, such as the FDA.
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Conclusion
Pharmaceutical and drug discovery are being transformed through the utilization of AI. Pharmaceutical manufacturers are able to employ machine learning, deep learning, and all other forms of AI for drug discovery in a number of different ways; faster, cheaper, and ultimately for improving patient outcomes. As AI technology continues to improve, drug discovery will transform across the lifespan of drug discovery and commercialization.
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References
- Serrano, D. R., Luciano, F. C., Anaya, B. J., Ongoren, B., Kara, A., Molina, G., Ramirez, B. I., Sánchez-Guirales, S. A., Simon, J. A., Tomietto, G., Rapti, C., Ruiz, H. K., Rawat, S., Kumar, D., & Lalatsa, A. (2024). Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics, 16(10), 1328. https://doi.org/10.3390/pharmaceutics16101328
- Brinkhaus, H. O., Rajan, K., Schaub, J., Zielesny, A., & Steinbeck, C. (2023). Open data and algorithms for open science in AI-driven molecular informatics. Current opinion in structural biology, 79, 102542. https://doi.org/10.1016/j.sbi.2023.102542
- Kant, S., Deepika & Roy, S. Artificial intelligence in drug discovery and development: transforming challenges into opportunities. Pharm. Sci.1, 7 (2025). https://doi.org/10.1007/s44395-025-00007-3
- Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023). The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals (Basel, Switzerland), 16(6), 891. https://doi.org/10.3390/ph16060891
- Kokudeva, M., Vichev, M., Naseva, E., Miteva, D. G., & Velikova, T. (2024). Artificial intelligence as a tool in drug discovery and development. World Journal of Experimental Medicine, 14(3), 96042. https://doi.org/10.5493/wjem.v14.i3.96042
- Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1), 80–93. https://doi.org/10.1016/j.drudis.2020.10.010