Presentacion individual ingles

Productos Farmacéuticos, Biotecnológicos y afines.

Candela García Vioque

AI in drug discovery

START!

INDEX

Introduction

Pros and cons

What is AI?

Companies

References

Areas of aplication

Conclusion

Introduction

The traditional drug discovery process is time-consuming (taking between 10 and 15 years), has a high failure rate, and is costly. However, AI offers tools to accelerate and optimize this process, enabling the identification of promising compounds, designing molecules, and predicting how they will interact with biological targets. This makes AI one of the most interesting advances for the pharmaceutical industry.

WHAT IS AI?

Artificial intelligence better known as AI, is a set of technologies that enable computers to perform a variety of advanced functions, including the ability to see, understand and translate spoken and written language, analyze data, make recommendations, and more. AI is the backbone of innovation in modern computing, unlocking value for individuals and buisnesses.

How AI works?

Main areas of aplication

1. dESIGN OF NEW DRUGS.

In this case, AI uses generative algorithms to design novel molecules that could become new medications. This uses tools such as machine learning models like Generative Adversarial Networks and molecular design alagorithms.

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2. Toxicity and side effect prediction

Here, AI can predict potential adverse effects of compounds before they reach clinical trials, using computational models.

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3. Optimization of clinical trials

In this case, AI identifies patient populations, predicts outomes, and optimizes study designs to reduce costs and time.

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Main areas of aplication

4. Identification of therapeutic Targets

In this case, AI analyzes large genomic and protein datasets to identify proteins or genes associated with a specific disease.

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5. Drug reprosing

Here, AI analyze existing data to find new uses for already approved drugs.

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Pros and cons

PROS

• Adaptability
• Cost
• Precision
• Speed

CONS

• Ethical and regulatory concerns
• Lack of transparency
• Over-reliance on data

COMPANIES THAT USE ai

CONCLUSION

As I have shown, AI is a tool that is here to stay, and we will increasingly see it in our daily lives. In the pharmatheutical industry, it is already playing a significant role, simplifying the process of discovering new drugs, among other aplications I have explained. When used correctly, AI will be an extremly valuable tool.

REFERENCES

• Google Cloud. (n.d.). What is artificial intelligence? Google Cloud. Retrieved November 27, 2024, from https://cloud.google.com/learn/what-is-artificial-intelligence?hl=en

• Shah-Neville, W. (2024, January 5). Five AI drug discovery companies you should know about. Labiotech.eu. https://www.labiotech.eu/best-biotech/ai-drug-discovery-companies/

• Wang, F., & Preiss, J. (2021). Artificial intelligence in drug discovery: Applications, opportunities, and challenges. National Center for Biotechnology Information. Retrieved November 27, 2024, from https://pmc.ncbi.nlm.nih.gov/articles/PMC10302890/#:~:text=By%20using%20AI%20algorithms%20to,the%20needs%20of%20individual%20patients.

• DataCamp. (2024, November 27). AI in pharmaceuticals: How AI is transforming the pharmaceutical industry. DataCamp. https://www.datacamp.com/blog/ai-in-pharmaceuticals
• Vial. (n.d.). AI-designed drugs vs. traditional drug discovery: Pros and cons. Vial. Retrieved November 27, 2024, from https://vial.com/blog/articles/ai-designed-drugs-vs-traditional-drug-discovery-pros-and-cons/

Thanks for listening!

For example

In BenevolentAI is used to identify baricitinib (drug for the rheumatoid arthritis) as a potential treatment for COVID-19.

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In 2020 artificial intelligence designed a compound for fibrosis treatment that entered clinical trials in just 12 months.

For example

In tbe company Insilico Medicine AI is use to identify new therapeutic targets for pulmonary fibrosis.

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Atomwise uses AI to analyze millions of compounds and filter out those with high toxicity potential.

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Companies like Deep 6 AI use machine learning to quickly identify eligible patientes for clinical trials.