Cancer remains one of the leading causes of mortality worldwide, driven by its complex and multifactorial origins. The numerous factors contributing to cancer onset complicate the identification of specific triggers, posing significant challenges for treatment. Despite advancements in therapeutic options, no cure guarantees complete remission, and treatment strategies vary depending on the individual and disease stage. Current modalities, including radiation therapy, chemotherapy, and surgery, are often limited by efficacy and adverse side effects. Cancer immunotherapy has emerged as a promising alternative, targeting immune checkpoints—key regulators of immune cell activity. Immune checkpoint molecules such as programmed cell death protein 1 (PD-1), lymphocyte-activation gene 3 (LAG-3), T-cell immunoglobulin and mucin-domain containing-3 (TIM-3), and T-cell immunoreceptor with Ig and ITIM domains (TIGIT) have become critical therapeutic targets. Monoclonal antibody-based drugs designed to block these pathways have demonstrated significant clinical success. However, the clinical translation of antibody-based immune checkpoint inhibitors remains limited due to immunogenicity, immune-related side effects, and high production costs. Additionally, their large molecular size restricts tumor tissue penetration, and their relatively long half-life can cause serious side effects by prolonging drug retention and complicating elimination. To overcome these limitations, advancements in computational drug discovery—including virtual screening, molecular docking, and molecular dynamics simulations—enable the efficient identification of potential small-molecule inhibitors that can bind to immune checkpoint targets and disrupt their interactions. These in silico techniques have become essential tools in modern drug development, offering rapid, cost-effective, and high-throughput screening methods for identifying promising drug candidates. In this study, we utilized in silico drug screening using FDA-approved drug libraries which were selected against a next-generation immune checkpoint TIGIT through structure-based virtual screening and molecular docking analysis. Additionally, the screened compounds demonstrated favorable drug-like properties, as assessed by ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis. Collectively, this study represents the potential of computational approaches to accelerate drug screening process. Using these approaches, we identified the lead compounds that can target TIGIT molecule which can be potentially used for cancer treatment.

1) Identify potential novel TIGIT inhibitors recruited from drug library databases: These identified compounds will represent promising candidates for further development as cancer immunotherapies. 2) Accelerating drug development by employing a drug repurposing strategy: Traditional drug discovery is time-consuming and costly; therefore, this high-throughput approach can accelerate the translation of research findings into clinical applications. 3) Enhanced efficacy of cancer immunotherapy and quality of life: By targeting TIGIT immune checkpoint, this research has the potential to enhance the efficacy of cancer immunotherapy, potentially leading to improved patient outcomes, including increased survival rates and enhanced quality of life. 4) Advancement of computational drug discovery: This study will contribute to the refinement and validation of computational drug discovery strategies, including structure-based virtual screening, molecular docking, ADMET analysis, molecular dynamics simulation. The successful application of these in silico techniques will demonstrate their utility in accelerating the drug discovery process, not only for cancer but also potentially for other diseases.