Computer-aided drug design (CADD) methodologies have proven to be very effective, greatly enhancing the efficiency of small molecule drug discovery and development processes. These methods include quantitative structureactivity relationship and pharmacophore models, quantitative structure-property relationship models, as well as in silico docking studies. While docking studies very often correctly identify the binding mode of a ligand, they have reduced success in predicting binding affinities. Development of improved and more efficient strategies for scoring binding affinity is a very active area of research. Here we review the utility of computational intelligence approaches such as artificial neural networks, fuzzy logic, and evolutionary computation to the calculation of improved docking scores.
Keywords: Computational intelligence, evolutionary algorithms, artificial neural networks, fuzzy logic, docking scores, in silico docking, high-throughput screening, virtual screening
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