Generative adversarial networks (GANs) are a cutting-edge technique in drug development that provides a fresh method for optimising and designing molecules. Under this paradigm, GANs transform the conventional drug development pipeline by learning from existing datasets to produce molecular structures with desired features. This work emphasises how artificial intelligence might transform and accelerate the discovery of novel therapeutic compounds and create data-driven drugs. The use of GANs in drug discovery is examined in this chapter, with a focus on their contributions to de novo drug design, property prediction, and molecular generation. GANs speed up the exploration of chemical space and make it easier to find promising therapeutic candidates by enabling the construction of diverse and chemically viable molecular structures. The chapter goes into further detail about the assessment criteria that are essential for determining the caliber, variety, and usefulness of molecules generated by GANs.
Top1. Introduction
This introduction reports the critical role that GANs play at the nexus of artificial intelligence (AI) and medical diagnostics, examining their uses, difficulties, and significant contributions to the advancement of contemporary healthcare. For a considerable while now, medical diagnosis has been a difficult and complicated process that greatly depends on the knowledge of medical specialists (Hussain et al., 2020), developments in imaging technology, and the accessibility of large and varied datasets (Tripathi et al., 2022). However, training reliable and effective diagnosis models is severely hampered by the lack of labelled data, particularly in rare disorders. Because of their capacity to produce synthetic data, GANs offer a strong answer to this problem (Padalkar et al., 2021). GANs aid in data augmentation by producing synthetic yet realistic medical images, increasing the variety and amount of datasets accessible for machine learning model training.
GANs are quite good at emulating the features and patterns found in actual medical images (Mangalampalli et al., 2023). This skill becomes especially important when it is logistically difficult or costly to gather a large and diverse sample (Sree, 2023). Within GANs, the generator network creates artificial medical images that closely mimic real patient data. By adding more examples to the dataset, machine learning models including those used in medical diagnosis can be trained on a wider range of cases, which could enhance their ability to make generalisations and improve diagnostic precision (Lin et al., 2020).
GANs greatly improve images for use in medical diagnostics in addition to enhancing datasets. Medical imaging, which includes X-rays, computed tomography (CT), and magnetic resonance imaging (MRI), frequently faces problems with low resolution, noise, and artefacts. GANs can be used to produce high-resolution, noise-reduced, and artifact-minimized versions of medical pictures through adversarial training. Improved pictures help medical practitioners diagnose patients more accurately and provide them a better knowledge of minute information that they might otherwise miss (Abbasi et al., 2022).
A key component of successful healthcare interventions is early disease identification. Because they are so good at detecting anomalies, GANs help with this. The discriminator network in GANs gains proficiency in identifying common patterns after being trained on a sizable sample of typical medical pictures. Any departure from these ingrained patterns is recognised as an anomaly by the GAN (Xu et al., 2021) which functions as a precursor to possible illnesses. When compared to more conventional diagnostic techniques, this capacity to recognise minute aberrations shows potential for early diagnosis of illnesses (Khang & Abdullayev, 2023).
Numerous modalities are frequently used in medical imaging, and each one offers a distinct perspective on various facets of a patient's health (Bian et al., 2019). Medical images can be converted from one modality to another with the help of GANs, which enable cross-modality translation (Sree et al., 2009). GANs, for example, can convert CT scans to MRI pictures and vice versa. By improving the interchange of medical imaging data, this feature helps medical professionals correlate data from various modalities to provide a more thorough diagnosis.
GANs are essential in customising medical models for specific patients as the era of personalised medicine emerges (Sree & Nedunuri, 2020). Through the utilisation of the abundant patient data, GANs are able to produce customised models that take into consideration the distinct qualities and variances present in every person's health profile. More precise and focused medical interventions could result from this personalised strategy, which would optimise treatment programmes according to each patient's unique health dynamics (Tong et al., 2021).