New methods for relevant applications in drug design
This special issue belongs to
|Research & Reviews: A Journal of Bioinformatics|
Deadline for Manuscript Submission
|March 31st, 2023|
Deadline for Publication
|April 15, 2023|
Special Issue Description
De novo drug design is a computational method for creating novel molecular structures from atomic building blocks with no prior knowledge of their relationships. Over the last three decades, software-based drug discovery and development methods have played a significant role in the development of bioactive compounds.
These methods are conventional methods, including structure-based and ligand-based designs, which are based on the properties of a biological target's active site or known active binders, respectively. Artificial intelligence, including machine learning, is a developing field that has had a positive impact on drug discovery.
Deep reinforcement learning is a subset of machine learning in which artificial neural networks are combined with reinforcement-learning architectures. This method has been used successfully to develop novel de novo drug design approaches by utilizing a variety of artificial networks such as recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders.
Other than AI there are other novels software-based methods such as molecular modeling, structure-based drug design, structure-based virtual screening, ligand interaction, and molecular dynamics that are regarded as powerful tools for studying drug pharmacokinetic and pharmacodynamic properties, as well as the structural activity relationship between a ligand and its target.
*Artificial intelligence *Ligand targets drug discovery. *De novo drug design *Machine learning *Computational methods
Manuscript Submission information
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