Advances in Elucidating Disease Mechanisms and Designing Therapeutics Using Computer-Aided Simulations and Modeling
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 2046
Special Issue Editor
Interests: nanoscale simulations; computational chemistry; supramolecular self-assembly; drug design; ligand docking; GPCRs
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Drug design for discovering new therapies for a large range of illnesses has been one of the most prominent and quickly developing areas of chemistry. Traditionally, it has relied on the guess work of chemists, which incurs high costs due to experiments involving synthesis, cellular assays, and animal models. Recent advances in computer-aided modeling can help circumvent these costs by predicting which drugs will be effective and avoiding those that will be ineffective in experiments. In addition, the elucidation of the disease mechanism through the protein structure and dynamics can also aid in designing new therapeutics. The development of machine learning algorithms can predict and design new drugs for specific types of illnesses, thus making computer-aided drug design even more efficient. This open access Special Issue will bring together research on the development and application of computer-aided drug-discovery methods in collaboration with experimental research. The main aim of this Special Issue is to develop collaboration between computer-aided drug design methods and experimental studies to design news therapeutics more efficiently.
Topics of this Special Issue may include, but are not limited to:
Fragment-based drug design, G-protein signaling, enzyme kinetics, G-protein receptors, cellular signaling, biostructures, ligand docking, machine learning for drug design, and lead optimization.
Dr. Pavel Rehak
Guest Editor
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Keywords
- X-ray diffraction
- electron scanning microscopy
- protein crystallization
- protein structure
- drug design approaches
- G-protein-coupled receptors
- G-protein signaling
- enzyme kinetics
- ligand docking
- lead optimization
- machine learning
- quantum computing
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