Introduction

Drug discovery is a time-consuming and costly endeavour; the mean cost of bringing a drug to
market is estimated between $314 million to $2.8 billion (Wouters et al., 2020) and takes an average
of 12 years (Moingeon et al., 2022). Although the impact of AI implementation is hard to quantify,
its use will undoubtedly increase the efficiency, accuracy and success of this process, reducing
pharmaceutical companies’ costs and saving countless lives.

Current drug design process and limitations

The current drug design process involves target identification, prediction of the target’s druggability,
drug screening, lead identification and subsequent pre-clinical and clinical trials (Zhuang &
Ibrahim, 2021). Traditionally, identifying lead compounds relies on labour-intensive techniques
such as trial and error and high-throughput screening (HTS) (Blanco-González et al., 2023). HTS
involves rapidly testing a library of chemical compounds that are screened using an assay to
identify ‘hits’ (usual hit rates of 0-0.1%) that show promising activity for the target molecule. The
appeal of AI arises from its ability to identify patterns and trends in vast HTS datasets that may not
be apparent to human researchers (Blanco-González et al., 2023), enabling the prediction for the
activity of novel compounds more accurately (Blanco- González et al., 2023).

AI and its Capabilities in the Pharmaceutical Industry

AI involves several method domains, such as reasoning, knowledge representation, solution search
and the fundamental aspect of machine learning (ML) (Paul et al., 2021). ML uses algorithms to
recognise patterns within a set of data and predict future data, whilst also showing great strength in
solving complex mathematical problems (Selvaraj et al., 2022). Within ML, lies a sub-field known
as deep learning (DL), focussing on neural networks that are capable of learning hierarchical
representations of data (Selvaraj et al., 2022). Artificial neural networks (ANNs) are composed of
three layers (input layer, hidden layer and output layer); within each layer exists a single computing
unit (known as neurons) that performs non-linear transformations of data (Selvaraj et al., 2022).
Molecular modelling and drug design rely heavily on ANNs; their ability to resolve complex issues
associated with statistical modelling in HTS shows promise in their use in medicinal chemistry
(Selvaraj et al., 2022). Furthermore, ANNs perform excellently in generating numerical values to
interpret non-linear relationships and predict the success of certain drugs (Selvaraj et al., 2022).

Case Study Using Deep Learning AI (Zhuang & Ibrahim, 2021)

Zhuang and Co. investigated the use and effectiveness of a DL model to identify and validate highefficiency drug compounds for the treatment of SARS-CoV-2 viral cells. The team used the preexisting DL Model DenseNet to perform transfer learning, in which a pre-trained model in a
specific domain is refined to be used in a different domain. They made use of 2 datasets from
Recursion: RxRx1 and RxRx19a. They initially trained the model with RxRx1, a dataset based on
human renal cortical cell (HRCE) treatments of 1138 classes of siRNA. Three different types of
HRCE cells were used: mock cells (the control), cells with a deactivated SARS-CoV-2 virus and
active SARS-CoV-2 cells. They then applied transfer learning a second time using RxRx19a, a
dataset with similar characteristics to RxRx1, but involving cell treatments of a range of compounds
from a library of drugs, generating 305,520 morphological images. A Softmax layer was applied
each time to produce probability scores, allowing for statistical analysis. They compared and
validated their results using other models as well as FDA-approved drugs, arriving at similar
conclusions. Although this study is not necessarily applicable to all research due to the limitation of
obtaining similar datasets, the results are encouraging and highlight the use of AI in speeding up the
process of drug selection and prioritisation.

Challenges and Limitations associated with AI:

Despite the potential benefits of AI use, several challenges must be considered. AI-based
approaches require the input of large volumes of information and data for training purposes, which
can often be hard to obtain due to private ownership of data by pharmaceutical companies in a
competitive market (Schneider et al., 2020). This issue is partly resolved by the continued
digitalisation of data and research but remains a primary barrier to the use of AI models.
Furthermore, there is a concern over the quality of data used and the potential for bias (Vora et al.,
2023), as well as worry over data privacy and security. To negate these problems, increased AI
regulation is imperative, ensuring AI models are trained with diverse and representative data, as
well as implementing strong data privacy protocols.

Conclusion

As increasing amounts of data become accessible for AI training purposes, the use of these
techniques has the potential to revolutionise the drug discovery process, making it faster, and
cheaper and reducing failure rates. Increased regulation of these models is necessary before the
healthcare industry can smoothly integrate them into practice and research. Overall, better-regulated
AI with the combined use of human research expertise, has the power to optimise the drug
discovery process.

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