Special Issue "Artificial Intelligence and Computer Aided Drug Design"
Deadline for manuscript submissions: closed (30 April 2019).
Interests: Virtual screening and CADD; Machine learning in molecular simulation data; Protein conformational changes related to drug interactions; In-silico protein–ligand binding, kinetics and thermodynamics; AI in pharmaceutical and clinical data analysis
Artificial Intelligence (AI) is gaining more and more importance in the pharmaceutical sector, deeply transforming the drug discovery process. There are many potential benefits of applying AI techniques to improve the development of new molecules and the identification of new targets, cutting R&D costs and time.
In drug discovery, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the huge amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs, which can be repurposed for an alternative use in medicine.
Moreover, with the help of AI, it becomes easier to run clinical tests, diagnose diseases and provide the most effective treatment for a particular disease. As it can interpret test results, AI can also look through various sources including publications to correctly diagnose critical ailments.
In this Special Issue of the International Journal of Molecular Sciences, we would like to discuss new approaches based on AI in the drug discovery process and in the repositioning of old molecules, together with their impact on the pharmaceutical pipeline. The goal is to provide an overview of the sectors where AI might play a crucial role in the pharmaceutical world in the next years.
Prof. Andrea Danani
Manuscript Submission Information
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- Computer aided drug design
- Machine learning, deep learning
- Big data
- Virtual screening
- Drug discovery and repurposing
- Drug-target interaction