The drug discovery process, traditionally a lengthy and costly endeavor, is being revolutionized by integrating innovative approaches. This review delves into how modern techniques accelerate drug discovery and development, significantly reducing costs. We focus on the robust synergy of bioinformatics, artificial intelligence (AI), and high-throughput screening (HTS). Bioinformatics aids in the identification and validation of drug targets by analyzing vast genomic and proteomic datasets. AI enhances lead compound identification and optimization through predictive modeling and machine learning (ML) algorithms, slashing the time required for these stages. HTS facilitates the rapid screening of vast compound libraries to pinpoint potential drug candidates. AI-based approaches, such as HTS and predictive modeling, enhance early-stage decision-making, minimize trial-and-error experimentation, and contribute to cost-efficiency across the pipeline. Moreover, advancements in computational chemistry and molecular dynamics simulations provide deeper insights into drug-target interactions, further accelerating the design of effective and selective drugs. In drug discovery, drug candidates are tested in laboratory and live animal settings to assess their effectiveness, pharmacokinetics, and safety. By integrating these preclinical methods, the efficiency and success of drug discovery can be significantly improved, leading to more effective and safer drugs. This review underscores the important role of these technologies in contemporary drug development and explores their promising implications for future research and clinical applications.
Beşli et al. (2025) studied this question.