Role of Artificial Intelligence in Drug Discovery and Design: From Foundational Principles to Emerging Applications in Antiviral Therapeutics
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Artificial Intelligence (AI) has significantly transformed drug discovery by enhancing efficiency, reducing costs, and accelerating timelines, particularly in research related to antiviral drugs. Traditional drug discovery methods are not able to compete with rapidly occurring viral mutations, since these are often time-consuming and labor-intensive. Hence, they have been replaced with AI techniques, capable of handling massive datasets, predicting molecular interactions, and optimizing drug candidates rapidly. AI can be used to identify novel drug molecules, drug targets, and repurposed drugs. Furthermore, it can also be used to predict chemical properties, as well as pharmacokinetic, pharmacodynamic, and toxicology profiles by analyzing large datasets. In the early stages of drug discovery, AI aids in target identification and validation by analyzing the genomic, proteomic, and chemical data to predict disease-relevant proteins. In virtual screening and hit identification, AI replaces high-throughput screening with rapid in silico analysis. Generative chemistry approaches utilize reinforcement learning to design novel, drug-like molecules rapidly. Through off-target profiling using models such as DeepTox, AI reduces adverse effects by forecasting unintended protein interactions and drug-drug interactions, improving safety profiles. Its predictive capabilities at each development stage—from molecular screening to clinical trials—have not only accelerated the pace of antiviral drug discovery but have also reduced overall costs significantly, thus proving essential during global pandemics like COVID-19. AI can be implemented at each step of drug discovery and development, from identifying drug molecules and conducting virtual screening to lead optimization and designing clinical trials, as well as interpreting the data obtained from the trials. Antiviral drugs for viral diseases, such as COVID-19, dengue, influenza, hepatitis, and Ebola, developed using AI are mentioned in this study. It also highlights the significance of AI in healthcare, particularly in novel drug development. There is also a dark side to AI, and concerns are rising about the accuracy and quality, as well as the legal and ethical aspects of fact-driven by datasets.
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