Computational Pharmacology in Drug Discovery
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pharmacology".
Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 1295
Special Issue Editor
Interests: computational biology; structural bioinformatics; computational virology; computational systems biology; theoretical and computational approaches for studies of protein dynamics; mechanisms of allosteric regulation; network modeling of biomolecular systems; machine learning; allosteric drug discovery and engineering of allosteric functions; AI and machine learning approaches for exploring allosteric landscapes and drug design
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Special Issue Information
Dear Colleagues,
Computational pharmacology is an interdisciplinary field that uses computational techniques to study the behavior of drugs in the human body. It combines knowledge from physics, biology, chemistry, pharmacology, and computer science to better understand the complex interactions between drugs and their receptors in the body. Computational pharmacology is an invaluable tool for pharmacological research, providing a level of accuracy and detail that cannot be achieved with traditional methods. It has been used to identify new drug candidates, predict drug–drug interactions, and to better understand the mechanisms of drug action. Computational pharmacology is based on mathematical and computer models that allow researchers to simulate the behavior of drugs and their interactions with other molecules. It can be used to predict and analyze the safety and efficacy of a drug before it is tested in clinical trials. The primary benefit of computational pharmacology is the ability to quickly and accurately predict the pharmacological effects of a drug, enabling researchers to reduce the time and costs associated with drug development. Computational models are used to simulate drug–receptor interactions and to analyze the structure and function of biomolecules. This allows researchers to identify potential drug targets and better understand the pharmacological effects of a drug. In addition to aiding in drug discovery and development, computational pharmacology can also be used to analyze the potential toxicity of a drug. By simulating the behavior of drugs and their interactions in the body, researchers can identify potential side effects and safety risks before a drug is tested in humans.
Computational pharmacology and AI are two powerful tools that have revolutionized the way drugs are discovered and developed. The combination of these two technologies has allowed researchers to develop powerful algorithms and computer models to simulate the effects of potential drugs on the human body. This has enabled them to rapidly identify potential drug candidates and develop them much more efficiently than ever before. In addition, AI has allowed for the development of more accurate and detailed models of how drugs interact with their targets, which can help scientists better understand the mechanisms of action of new drugs. For instance, AI can be used to predict the potential side effects of a drug, allowing researchers to adjust the formulation of the drug before it is tested in clinical trials. AI can also be used to analyze large datasets of clinical data to identify potential biomarkers for drug efficacy and safety, allowing researchers to make informed decisions about which drugs should be developed.
Prof. Dr. Gennady Verkhivker
Guest Editor
Manuscript Submission Information
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