Primary open-angle glaucoma (POAG) is a significant cause of blindness worldwide (Kwon et al., 2009). It is characterized by progressive optic nerve damage, resulting in gradual vision loss and is often correlated with elevated intraocular pressure (IOP). This disease results in the loss of retinal ganglion cells (RGCs) responsible for visual signal transmission to the brain. The equilibrium between aqueous humor production by the ciliary body and drainage through the trabecular meshwork, along with contribution from the uveoscleral or nonconventional pathway, modulates steady-state IOP (Llobet et al., 2003). Disruptions in this equilibrium can lead to sustained elevated pressure within the eye, jeopardizing optic nerve health (Goel et al., 2010; Llobet et al., 2003; Tamm, 2009).

Because elevated IOP is the only modifiable risk factor, reducing IOP remains the primary treatment strategy for POAG; however, existing approaches may result in undesirable side effects and do not necessarily address the underlying disease causes (Beidoe and Mousa, 2012; Llobet et al., 2003; Shah et al., 2013). For instance, Vyzulta (latanoprostene bunod, 0.024%), an FDA-approved prostaglandin analog for POAG, has been associated with permanent pigmentation of the eyelids, lashes, and iris (Weinreb et al., 2015). Furthermore, many of these medications necessitate frequent dosing due to their short duration of action, resulting in increased cost to patients, suboptimal patient adherence to prescribed treatment regimens, and potentially leading to more pronounced side effects. Current approved therapeutics are predicated on IOP reduction. However, some patients develop normal tension glaucoma (NTG), a subset of glaucoma that does not exhibit elevated IOP but is still characterized by progressive vision loss due to RGC axon death, indicating that the pathophysiology of glaucoma is complex and multi-faceted (Leung and Tham, 2022). Hence, there is a need to discover novel drugs with fewer side effects, prolonged duration of action, and alternative treatment targets to combat this debilitating disease more effectively.

Novel drug discovery research entails intricate and formidable challenges, requiring comprehensive optimization during pre-clinical phases and stringent clinical trials to establish both efficacy and safety profiles. Despite these efforts, the approval rates for novel drugs in the United States remain disappointingly low, with less than 20 % of candidate compounds obtaining regulatory approval (Takebe et al., 2018). This, coupled with the substantial research and development costs ranging from $300 million to $3 billion, highlights the need for transformative approaches in the drug discovery pipeline (Wouters et al., 2020). Fortunately, recent advancements in computational techniques and the availability of expansive public datasets are revolutionizing the field and position data science as a pivotal component in drug discovery. These computational approaches offer promising solutions and are becoming increasingly valuable in various industries, including pharmaceuticals, materials science, and beyond. In fact, the world's top 10 pharmaceutical companies by revenue, which include Pfizer, Johnson & Johnson, Roche, Merck, AbbVie, Novartis, Bristol Myers Squibb, Sanofi, AstraZeneca, and GlaxoSmithKline, have uniformly enlisted the expertise of senior scientists actively contributing to groundbreaking computational pharmacology initiatives, as corroborated by the career pages on their official websites. This collective embrace of computational method signifies a paradigm shift in the industry, as these esteemed companies recognize the transformative potential of advanced computational methodologies in development of novel therapeutics against diseases (Abramov et al., 2022; Pognan et al., 2023; Raza et al., 2022).

Advanced in silico technologies have three key advantages that make them increasingly valuable in experimental eye research. Firstly, systems genetics can rely on big data analytic tools to provide valuable insights into the complex interplay between genes, pathways, and diseases, accelerating the identification of novel drug targets and reducing the time spent on traditional bench-top experiments (Mulligan et al., 2017). It aids in target identification and validation, elucidating disease mechanisms through gene ontology and protein-protein interaction network analyses (Mulligan et al., 2017; Zhou et al., 2019). Secondly, molecular modelling tools empower researchers to design drugs in a rational and informed manner by predicting the binding affinity and interactions between potential drug candidates and target molecules and tissues. Moreover, the integration of large-scale molecular modeling, specifically molecular dynamics (MD) simulation, holds immense potential in accelerating the identification of promising compounds while reducing time and costs associated with traditional experimental methods. Together with other modelling tools, MD simulation allows real-time exploration of drug-target interactions, calculation of protein folding, prediction of ligand flexibility, and computation of atomic-level conformational changes in protein structures (Halder et al., 2023; Li et al., 2023). This optimization of drug properties, such as efficacy, selectivity, and pharmacokinetics, increases the likelihood of successful drug development (Kontoyianni, 2017). Thirdly, advancements in artificial intelligence (AI) enable the development of AI-based predictive models that can revolutionize drug discovery and clinical evaluation of ocular diseases. AI algorithms are capable of not only predicting drug ADME (absorption, distribution, metabolism, and excretion) properties but also analyzing data with unprecedented accuracy, leading to optimized drug candidates and faster data analysis (Cascini et al., 2022; Srivastava et al., 2023). Lastly, embracing these advancements ensures researchers remain at the forefront of innovative approaches in ocular pharmacology, providing a competitive advantage in the quest for new drug discoveries and improved patient care.

The integration of these in silico techniques offers a synergistic approach in glaucoma research, driving innovation and advancements in the field. This article provides a general protocol suitable for investigators at all levels, highlighting the harmonious use of these techniques. Furthermore, as computer hardware and AI technologies continue to evolve, it is inevitable that in silico techniques will adapt and further progress. This article will address the current state and future directions of these techniques based on the most recent literature up to July of 2023.