Integration of Artificial Intelligence, QSAR, and In Silico Approaches in Modern Drug Discovery

Abstract

The process of discovering new drugs involves intricate steps that demand significant time, resources, and often face high failure rates along with rising expenses. Combining Artificial Intelligence (AI), Quantitative Structure-Activity Relationship (QSAR) analysis, and sophisticated computational methods offers a promising solution to these issues. Techniques powered by AI, such as machine learning algorithms and deep neural networks, allow for streamlined processing of vast chemical and biological data sets, speeding up processes like target selection, virtual compound screening, and novel drug molecule creation. QSAR methodologies, encompassing classical, 2D, 3D, and group-based approaches, provide quantitative insights into structure activity relationships, whereas in silico tools such as molecular docking, molecular dynamics simulations, pharmacophore modeling, and ADME/Tox prediction enhance the evaluation and optimization of drug candidates. The integration of these methodologies through hybrid modeling approaches significantly improves predictive accuracy, reduces experimental burden, and accelerates the drug discovery pipeline. Real-world examples, such as AI-guided discovery of kinase inhibitors, antiviral agents developed amid the COVID-19 crisis, and innovative anticancer compounds, showcase the tangible benefits of these computational methods. Applications targeting diseases such as cancer, viral infections, and malaria, along with research on pyrimidine-based compounds, further validate the value of combined in silico strategies in practical settings. However, persistent hurdles, including inconsistent data quality, limited model transparency, and regulatory hurdles, continue to demand attention. Future perspectives emphasize the development of explainable AI, integration of multi-omics data, and the emergence of automated and cloud-based drug discovery platforms. Overall, the synergy of AI, QSAR modeling, and computational techniques establishes a powerful, adaptable platform ready to transform contemporary drug discovery and advance innovative therapies.