In the same decade, the first substantial databases were established, including ZINC and PubChem (2004) [21, 22]. This was followed by the creation of DrugBank in 2006 [23] and ChEMBL in 2008 [20, 24]. However, signi ficant advances in the field of drug discovery only materialized in more recent times. In 2015, Atomwise introduced AtomNet, the pioneering deep learning neural network for structure-based drug design, utilizing three-dimensional representations of chem­ical interactions. The system identified chemical features like aromaticity, sp carbons, and hydrogen bonding, akin to how image recognition networks compre­hend spatially proximate features. AtomNet subsequently played notable importance in predicting novel candidate biomolecules for various disease targets, notably contributing to treatments for the Ebola virus and multiple sclerosis [25]. The culmination of progress in the field occurred in 2020, marked by two notable events: the discovery of halicin, an antibiotic [26] through deep learning models, and the introduction of the first planned machine learning drug candidate for the treatment of cancer and cardiovascular disease into preclinical testing [27]. Figure 4.1 illustrates the chronological progression of ML in the field of drug discovery, depicting key milestones.

target identification to the optimization of clinical trials. Broadly, ML methods can be categorized into two major groups: supervised learning and unsupervised learning [14].