A Critical Review on Computational Techniques through in silico Assisted Drug Design

Advancements in computational techniques have revolutionized the field of drug design, offering a powerful arsenal of tools collectively known as in silico methods. This review provides an overview of the diverse computational techniques employed in the in silico assisted drug design process. From molecular docking and molecular dynamics simulations to Quantitative Structure-Activity Relationship (QSAR) models and artificial intelligence-based approaches, these methods play a pivotal role in expediting drug discovery and optimization. The utilization of molecular docking facilitates the prediction of ligand-receptor interactions, aiding in the identification of potential drug candidates. Molecular dynamics simulations contribute by unraveling the dynamic behavior of biomolecular complexes, offering insights into their stability and flexibility. QSAR models, relying on mathematical correlations between molecular descriptors and biological activities, enable the prediction of compound behaviors, guiding the optimization of lead compounds. The integration of machine learning and artificial intelligence further enhances drug design workflows. Deep learning algorithms, such as neural networks, have demonstrated remarkable capabilities in predicting complex biological activities and uncovering hidden patterns within large datasets. High-throughput screening, coupled with in silico methodologies, allows for the rapid exploration of vast chemical spaces, accelerating the identification of promising drug candidates. Despite these advancements, challenges persist, including the accurate representation of biological systems, the validation of computational predictions and the ethical implications of relying solely on in silico methods. This review critically evaluates the current state of computational techniques in silico assisted drug design, highlighting their strengths, limitations and potential future directions. The integration of computational techniques in drug design has significantly reshaped the landscape of pharmaceutical research. As these methods continue to evolve, bridging the gap between computational predictions and experimental validations, the synergy between in silico and in vitro approaches holds immense promise for the rapid and effective development of novel therapeutic agents.