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AI-Driven Drug Discovery: A Sustainable Approach in Biosciences




Introduction

The integration of artificial intelligence (AI) in drug discovery is transforming the pharmaceutical industry, offering a sustainable approach to developing new therapeutics. Drug discovery is a complex and resource-intensive process, traditionally involving extensive laboratory experiments and clinical trials. The advent of AI has the potential to revolutionise this field by accelerating the identification of potential drug candidates, optimising clinical trial designs, and reducing the time and cost associated with bringing new drugs to market (Mak & Pichika, 2019).


This article explores the role of AI in drug discovery, focusing on its applications, mechanisms, benefits, disadvantages, and current research. By leveraging AI, drug discovery processes can become more efficient, cost-effective, and environmentally friendly.


Background

AI encompasses various technologies, including machine learning (ML), deep learning, and natural language processing (NLP), which can analyse vast amounts of data and uncover patterns that are not easily discernible by humans. In drug discovery, AI can assist in predicting the efficacy, toxicity, and pharmacokinetics of potential drug compounds, thus streamlining the development pipeline (Vamathevan et al., 2019).


Mechanisms

1. Virtual Screening and Drug Design: AI algorithms can perform virtual screening of large chemical libraries to identify compounds with potential therapeutic effects. Machine learning models predict how different molecules will interact with target proteins, guiding the design of new drugs with higher efficacy and fewer side effects (Chen et al., 2018).

2. Biomarker Discovery: AI tools analyse genomic, proteomic, and metabolomic data to identify biomarkers that can predict disease progression and response to treatment. These biomarkers are crucial for developing targeted therapies and personalised medicine (Ramos et al., 2020).

3. Optimization of Clinical Trials: AI can optimize clinical trial designs by identifying suitable patient populations, predicting patient responses, and monitoring patient adherence. This leads to more efficient trials with higher success rates (Waltz, 2019).


How can utilising AI create a more sustainable future in terms of drug discovery? 

  1. Reducing Environmental Impact 

Traditional drug discovery methods involve extensive laboratory work and high-throughput screening, generating significant chemical waste. AI-driven drug discovery employs virtual screening and in silicon models to predict the efficacy and toxicity of compounds, drastically reducing the need for physical testing and thus minimising chemical waste (Chen et al., 2018).

AI algorithms can also optimise chemical synthesis routes, identifying the most efficient pathways to produce compounds with fewer steps and less waste. This reduces the consumption of raw materials and the production of hazardous by-products (Schneider, 2018).

  1. Conserving Resources

AI-driven drug discovery reduces the reliance on physical resources such as reagents, solvents, and other laboratory consumables by shifting much of the exploratory and predictive work to computational models (Mak & Pichika, 2019).

  1. Promoting Sustainable Practices

The traditional drug development process can take over a decade and billions of dollars to bring a new drug to market. AI significantly shortens this timeline by rapidly identifying promising drug candidates and optimising clinical trial designs, reducing the overall resource expenditure (Vamathevan et al., 2019). 

AI can also analyse vast datasets to identify new therapeutic uses for existing drugs, a process known as drug repurposing. This approach bypasses the need for early-stage development and reduces the time, cost, and resources required to develop new treatments (Zhou et al., 2020).

  1. Ethical and Economic Sustainability

The high cost of traditional drug discovery is a barrier to the development of new drugs, particularly for rare diseases. AI-driven drug discovery lowers the financial burden by increasing efficiency and reducing costs, making it more feasible to develop treatments for a wider range of conditions (Mak & Pichika, 2019). 

By improving the efficiency of drug discovery, AI ensures that financial and scientific resources are allocated more ethically, prioritising the development of drugs that address significant medical needs and providing equitable access to new treatments (Mak & Pichika, 2019).


What are some potential areas that can be improved on? 

  • Data Quality and Availability: The effectiveness of AI models depends on the availability of high-quality, comprehensive datasets, which may not always be accessible.

  • Regulatory Challenges: Integrating AI into drug discovery introduces regulatory hurdles, as agencies must develop new frameworks to evaluate AI-driven methodologies.

  • Ethical Considerations: The use of AI raises ethical concerns related to data privacy, algorithmic bias, and transparency in decision-making processes.


What’s some of the current research being conducted within the area? 

  1. AI for De Novo Drug Design: Research focuses on using generative adversarial networks (GANs) and reinforcement learning to design novel drug molecules from scratch. These AI models can generate candidate compounds with desired properties, significantly speeding up the drug discovery process (Zhavoronkov et al., 2019).

  2. AI in Repurposing Existing Drugs: AI algorithms analyse existing drug databases to identify new therapeutic uses for approved drugs. This approach, known as drug repurposing, can quickly bring treatments to market for emerging diseases, as seen in the search for COVID-19 therapies (Zhou et al., 2020).

  3. AI for Predicting Drug-Drug Interactions: Researchers are developing AI models to predict potential interactions between different drugs, helping to avoid adverse effects in patients taking multiple medications. These models analyse pharmacological data to identify combinations that may pose risks (Mizutani et al., 2021).


Conclusion

AI-driven drug discovery represents a sustainable and transformative approach within the biosciences. By enhancing efficiency, reducing costs, and enabling personalised medicine, AI has the potential to address some of the most pressing challenges in drug development. Continued research and collaboration between AI experts and pharmaceutical scientists are essential to fully realise the benefits of AI in this field.

 

Article prepared by: Chong Yuen Yeng, MBIOS R&D Associate 23/24


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References

  1. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241-1250.

  2. Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, 24(3), 773-780.

  3. Mizutani, T., Koyama, R., & Waki, Y. (2021). Predicting drug-drug interactions using artificial intelligence techniques. Clinical Pharmacology & Therapeutics, 109(5), 1046-1054.

  4. Ramos, E. M., Hoffman, D., Junkins, H. A., Maglott, D., Phan, L., Sherry, S. T., & Feolo, M. (2020). Phenotype-Genotype Integrator (PheGenI): synthesizing genome-wide association study (GWAS) data with existing genomic resources. European Journal of Human Genetics, 28(4), 539-545.

  5. Schneider, G. (2018). Automating drug design: recent advances in artificial intelligence for drug discovery. Drug Discovery Today, 23(5), 1011-1015.

  6. Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., ... & Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463-477.

  7. Waltz, E. (2019). How artificial intelligence is aiding the hunt for new drugs. Nature, 567(7748), 277-278.

  8. Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., ... & Aspuru-Guzik, A. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038-1040.

  9. Zhou, Y., Wang, F., Tang, J., Nussinov, R., & Cheng, F. (2020). Artificial intelligence in COVID-19 drug repurposing. The Lancet Digital Health, 2(12), e667-e676.Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241-1250.

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