Recent Advances in Drug Design

Dear Colleagues,

Pharmaceutical companies and labs in academia invest in advanced drug discovery methods in order to reduce time and cost for research and development. Rational drug design methods can guide to the “correct analogs” before synthesis for drug development. Structure-based drug design (SBDD) uses the experimental/theoretical-modelled structure of the protein target, while ligand-based drug design (LBDD) is based on the physicochemical properties and structure of known binders and decoys datasets, applied in 2D- or 3D-QSAR. Both SBDD and LBDD methods aim at description of an accurate “pharmacophore model” that will be used as a template for compounds screening. Hardware innovations on computer science such as multithreading computing and GPU accelerated processing units have led to the development of algorithms that allow the efficient calculation of descriptors and similarity indices used in LBDD or the description of conformational space using advanced docking and molecular dynamics simulation method which are useful in SBDD. However, this Special Issue will focus on recent improvements on water mapping algorithms for SBDD and machine learning algorithms (neural networks, support vector machine, etc/) from researchers involved in computer science, which will suggest molecular descriptors of binding phenomena and tools from graph theory for mapping the binding area, aiming at filtering the best candidate molecules during a virtual screening procedure. Additionally, manuscripts that mainly describe computational studies should be accompanied by experimental validation.

Dr. George Lambrinidis
Guest Editor

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