Advances in models in silico for the fast discovery of pharmaceuticals: transforming pharmaceuticals

Abstract

Introduction. The Discovery and development of new drugs face significant challenges due to prohibitive costs, long development periods, and low success rates. Computational pharmacology, taking advantage of in silico models, emerges as a promising solution, accelerating and economizing this process through the prediction of drug-target interactions and optimization of pharmacokinetic and pharmacodynamic properties. Objective. Explore the fundamental role that in silico models play in the revolution in the Discovery and development of new drugs. Methodology. The research followed a methodological process based on a qualitative approach, on the analytical-synthetic scientific method, through observation techniques and according to its documentary data source, which made it possible to observe the role played by un silico models in relation to drugs. Results. This article reviews the current state of computational pharmacology, highlighting in silico modeling techniques and tools in the identification of compounds with pharmacological potential. Case studies are examined where the application of in silico models has resulted in the successful discovery of new drugs, emphasizing their effectiveness in the drug discovery process. Current challenges and limitations of computational pharmacology are discussed, along with proposed strategies to overcome these obstacles. Furthermore, future directions and technological advances are projected, considering the disruptive role of artificial intelligence and quantum computing in the transformation of the drug Discovery paradigm. Conclusion. This review highlights the importance of continuing to develop and apply in silico models to facilitate new drug Discovery, promising an era of faster and more personalized pharmacological innovation.