Lila Fernandez*
Dept. of Medicinal Chemistry, Universidad Nacional de Córdoba, Argentina
Received: 02-Dec-2025, Manuscript No. jomc-25-177986; Editor assigned: 4-Dec-2025, Pre-QC No. jomc-25-177986 (PQ); Reviewed: 14-Dec-2025, QC No jomc-25-177986; Revised: 20-Dec-2025, Manuscript No. jomc-25-177986 (R); Published: 28-Dec-2025, DOI: 10.4172/ jomc.12.018
Citation: Lila Fernandez, Computational and in Silico Drug Modeling. J Med Orgni Chem. 2025.12.018.
Copyright: © 2025 Lila Fernandez, this is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Computational and in silico drug modeling has become an essential component of modern drug discovery and development. This approach uses computer-based methods and mathematical models to simulate and analyze drug–target interactions, predict biological activity, and optimize drug candidates before experimental testing. By reducing reliance on costly and time-consuming laboratory experiments, computational modeling accelerates the drug discovery process and improves efficiency. With advances in computational power, bioinformatics, and artificial intelligence, in silico techniques now play a central role in rational drug design [1].
The drug modeling process typically begins with target identification and structural analysis. Once the three-dimensional structure of a biological target such as a protein or enzyme is known, computational tools can predict how potential drug molecules may bind to it. Molecular docking is one of the most widely used techniques, allowing researchers to estimate binding affinity and identify key interactions between a ligand and its target. This helps prioritize compounds with the highest likelihood of biological activity [2, 3].
Another important method is molecular dynamics simulation, which examines the movement and stability of drug–target complexes over time. This technique provides insight into conformational changes, binding stability, and the dynamic behavior of biomolecules under physiological conditions. Quantitative Structure–Activity Relationship (QSAR) modeling further supports drug discovery by correlating chemical features with biological activity using statistical and machine learning approaches. QSAR models can predict potency, toxicity, and pharmacokinetic properties, guiding compound optimization [4, 5].
Computational approaches are also widely used to evaluate absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Early prediction of these parameters helps eliminate unsuitable drug candidates, reducing late-stage failure rates. Virtual screening of large chemical libraries enables rapid identification of promising lead compounds, significantly expanding the chemical space explored in drug discovery.
Despite their advantages, in silico methods have limitations. Predictions depend heavily on the quality of input data and algorithms, and biological systems are inherently complex. Therefore, computational modeling is most effective when integrated with experimental validation. Ongoing advancements in artificial intelligence, deep learning, and high-performance computing continue to enhance the accuracy and applicability of these methods.
Computational and in silico drug modeling has transformed the landscape of drug discovery by enabling faster, more cost-effective, and rational development of therapeutic agents. By predicting drug behavior and guiding experimental efforts, these approaches reduce risks and improve success rates. As computational technologies continue to evolve, in silico modeling will play an increasingly important role in developing safer, more effective, and personalized medicines for the future.
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