Pharmaceutical compounds, commonly known as medications, are recognized chemical entities capable of inducing biological alterations within living organisms, ranging from the activation to the inhibition of proteins associated with various diseases. Their purpose extends to the treatment, diagnosis, enhancement, and prevention of medical conditions. In instances where existing medicinal solutions prove inadequate, the initiation of de novo drug discovery projects becomes imperative. These endeavors embark on the quest for entirely new therapeutic agents, aiming to uncover innovative compounds that can address unmet medical needs and broaden the scope of available treatments.
In the realm of drug discovery, computational methods have emerged as powerful tools, revolutionizing traditional, time-consuming processes. This interdisciplinary approach integrates principles from chemistry, biology, informatics, and computer science to accelerate the identification and design of novel pharmaceutical compounds. Computational methods employ algorithms and simulations to predict and analyze the interactions between drugs and biological targets, significantly expediting the identification of potential candidates.
Drug design, an inventive process rooted in the understanding of biological targets, involves crafting new medications. This intricate process revolves around designing molecules that align in shape and charge with the biomolecular target, aiming for a precise and effective therapeutic outcome.
The integration of computer systems has emerged as a catalyst for transforming drug design and discovery, mitigating costs, and enhancing efficiency. This approach, known as Computer-Aided Drug Design (CADD), leverages the proliferation of databases, the increased computational power of systems, and the evolution of computational methods across scientific disciplines. CADD encompasses diverse theoretical and computational approaches that have become integral to modern drug design and discovery.
Some of main subjects in this topic are available as follows:
- Computational drug design
- Bioactivity prediction of compounds
- Adverse drug reaction predictions
- Drug-target binding affinity prediction
- Drug repurposing
- Drug-target association prediction
- QSAR
- Molecular docking studies
Reference:
Computational Omics Lab, Centre of Bioinformatics, University of Allahabad, Prayagraj, Indi.(2020). Computational Approaches for Drug Target Identification. https://link.springer.com/chapter/10.1007/978-981-15-6815-2_8
Katsila, T., Spyroulias, G. A., Patrinos, G. P., & Matsoukas, M.-T. (2016). Computational approaches in target identification and drug discovery. Computational and Structural Biotechnology Journal. https://doi.org/10.1016/j.csbj.2016.04.004
Kaushik, A. C., Kumar, A., Bharadwaj, S., Chaudhary, R., & Sahi, S. (2018). Bioinformatics Techniques for Drug Discovery.
