In silico Drug Design: some concepts & tools - Chemoinformatics - Virtual screening - Repositioning
Chemoinformatics: Virtual screening, scoring, hit2lead, repositioning
Virtual screening can be done using different types of information, for example, known small bioactive ligands, in this case it is usually called ligand-based virtual screening, or else use information about the 3D structure of the target, often a protein, in this case it is called structure-based virtual screening. Note that for ligand-based approach, structural information can also be used and that for structure-based screening, knowing bioactive molecules also help to for instance calibrate the scoring function. In some situations, it is possible to combine these two main screening strategies. Also, one should not forget about fragment screening and de novo drug design (de novo approaches usually exploit information from the 3D structure of the receptor to build a compound inside a binding pocket while screening takes directly molecules in a collection and see if they fit in the pocket, both approaches can be combined, for instance during the optimization phase). See below, the section Comments about virtual screening. For structure-based screening, in general the scoring is performed with empirical scoring functions, force field based scoring functions or knowledge-based scoring functions. As scoring is a weak point in screening, consensus approaches can be used, or scoring functions tune to a target or a target family. Because flexibility is also a difficult point, several strategies have been proposed, the use of soft scoring, ensemble-based approaches, induced fit approaches (difficult for screening) and molecular simulation based on force fields and in general depicting intra and intermolecular interactions with or without water molecules. Ligand-based approaches often involve QSAR and pharmacophore modeling using known bioactive ligands (but pharmacophore models can also be developed using information from the binding pocket). In general to build QSAR models to design drugs, it is important to have a bioactive compound set that encompasses a wide range of affinity (eg, 4 orders of magnitude) and to have a minimum number of about 20 compounds (indeed more is much better), molecular descriptors should be selected with great care...and negative molecules. Many different types of machine learning algorithms can be tested here, from decision tree to deep learning...
Computational approaches for drug repositioning are in general based on similarity between drugs, between proteins or side effect phenotypes. Reverse or inverse docking can also be used. Searches in databases and literature mining methods are thus very important here also.
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