The Importance Of AI In The Development Life Sciences

The life sciences industry (which for the purposes of this article are seen as encompassing medicine, biotechnology and pharmaceuticals), is undergoing a profound transformation driven by artificial intelligence ("AI").

While the field has long been characterised by technological innovation, the complexity and volume of biological data in the twenty-first century make AI uniquely indispensable. In Browne Jacobson's view, the integration of AI into life sciences research and practice is not merely useful but essential, both for accelerating discovery and for enabling more personalised healthcare. This article outlines the importance of AI with a focus on three key areas: drug discovery, personalised medicine and the emerging role of digital twins.

Challenge of data complexity in modern Life Sciences

The central challenge in contemporary life sciences lies in data complexity. The human genome alone contains more than three billion base pairs and health outcomes are shaped by intricate interactions among genetic, environmental and behavioural factors (Venter et al., 2001).

Traditional statistical approaches, while valuable, are often inadequate for capturing such multidimensional relationships. AI, particularly through machine learning and deep learning, provides a means of uncovering patterns and associations that would otherwise remain invisible and its ability to process large, heterogeneous datasets quickly is becoming fundamental to scientific progress.

AI in drug discovery: Accelerating development and reducing costs

One domain where AI's impact is especially visible is drug discovery. The traditional path from drug target identification to regulatory approval often spans more than a decade and costs billions of dollars (DiMasi et al., 2016). One of the greatest obstacles lies in predicting which molecules will interact beneficially with biological targets while avoiding toxicity. AI has begun to revolutionise this process. For example, DeepMind's AlphaFold system achieved a breakthrough in protein structure prediction, providing accurate models for nearly all known proteins (Jumper et al., 2021).

This accomplishment solved a decades-old challenge and now provides researchers with critical insights into molecular interactions that can inform better drug design – as well as providing Mr Jumper with a Nobel prize in chemistry, alongside Demis Hassabis of DeepMind.

Generative AI for novel drug candidate design

In parallel, companies such as Insilico Medicine are applying generative AI to design novel drug candidates. Its platform has successfully produced preclinical compounds in record time, demonstrating the potential to reduce both cost and risk in the earliest stages of drug development (Zhavoronkov et al., 2019).

Although not all AI-generated molecules will succeed in clinical trials, the acceleration of early discovery makes it feasible to explore therapies for diseases that are currently underfunded or overlooked. In this sense, AI is not only important but transformative in reshaping the economics of pharmaceutical innovation.

Personalised medicine and AI: Tailoring cancer treatment to individuals

A second area of profound importance is personalised medicine, particularly in oncology. Cancer is a heterogeneous set of diseases, each driven by distinct molecular mechanisms. Effective treatment requires tailoring therapies to the individual patient's tumour profile, but integrating genomic, histopathological and clinical data at scale has always proved to be a very difficult thing to achieve.

However, AI systems have emerged as very promising tools to address this complexity. For example, clinical decision support platforms like IBM Watson for Oncology have been trialled to match patients with evidence-based treatment plans by analysing vast bodies of medical literature in conjunction with molecular profiles (Somashekhar et al., 2018).

AI-Powered digital pathology: Improving diagnostic accuracy

Equally significant is the role of AI in digital pathology. Deep learning algorithms can detect subtle features in histopathological images that are imperceptible to human pathologists, allowing more accurate classification of tumour subtypes and prognostic predictions (Coudray et al., 2018).

In some studies, AI systems have demonstrated performance on par with or superior to human experts, raising the prospect of integrating algorithmic insights into routine diagnostics. This development could not only improve clinical outcomes but also ensure more consistent standards of care globally.

Digital twins in healthcare: Simulating patient outcomes for preventive medicine

A rapidly emerging application of AI in life sciences is the creation of digital twins—virtual replicas of biological systems that can simulate and predict health outcomes. In medicine, a digital twin of an individual patient could integrate genomic, physiological and lifestyle data to model disease progression or forecast responses to specific treatments (Björnsson et al., 2020).

For example, digital twins of the cardiovascular system have been used to predict how patients will respond to surgical interventions or pharmacological therapies, enabling clinicians to test multiple scenarios before making treatment decisions. This approach holds enormous promise for preventive healthcare, clinical trial optimisation and truly individualised therapy. By allowing continuous refinement based on real-world patient data, AI-driven digital twins could bridge the gap between population-level research and personalised care, which is a truly remarkable achievement.

AI as a catalyst for global research collaboration

Beyond these specific examples, AI also serves as a catalyst for collaboration between world leading experts and institutions. Many AI projects in the life sciences rely on large, shared datasets, encouraging open science and global research cooperation. Moreover, AI helps bridge historically siloed fields, linking computational methods with experimental biology and clinical practice. Such integration is vital for tackling complex health challenges, from neurodegenerative diseases to global pandemics.

However, the adoption of AI is not without risks. Biases in training data may exacerbate existing health inequities, while the use of sensitive medical information raises significant concerns about privacy and data governance (Char et al., 2018). Furthermore, although AI can generate hypotheses and guide decision-making, experimental validation remains essential. AI should be regarded as an augmentation of human expertise rather than a replacement and careful regulation and ethical oversight are required to ensure its responsible development and use.

Summary

In conclusion, artificial intelligence is emerging as a defining force in the development of the life sciences industry. By transforming drug discovery, enabling personalised medicine, and introducing digital twin technologies, AI addresses some of the sector's most pressing challenges: cost-intensive innovation, the need for individualised care and predictive modelling.

Its capacity to analyse vast datasets, reveal hidden patterns and accelerate discovery makes it an indispensable tool for advancing both science and patient health. While limitations and ethical challenges must be managed, the potential benefits are substantial. For these reasons, we believe that AI is not simply an asset but a necessity for the continued growth and development of the life sciences industry.

References

• Björnsson, B., et al. (2020). Digital twins to personalize medicine. Genome Medicine, 12(1), 4.
• Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—addressing ethical challenges. The New England Journal of Medicine, 378(11), 981–983.
• Coudray, N., et al. (2018). Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nature Medicine, 24(10), 1559–1567.
• DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, 47, 20–33.
• Jumper, J., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589.
• Somashekhar, S. P., et al. (2018). Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumour board. Annals of Oncology, 29(2), 418–423.
• Venter, J. C., et al. (2001). The sequence of the human genome. Science, 291(5507), 1304–1351.
• Zhavoronkov, A., et al. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.