Fuzzy Clustering: Identification of Similar Compounds for Virtual Screening in Rational Drug Design

CRAVERO, FIORELLA; MARTINEZ, MARÍA JIMENA; DIAZ, MÓNICA F.; PONZONI, IGNACIO

Buenos Aires

Conferencia; IV International Society for Computational Biology Latin America Bioinformatics Conference (ISCB-LA 2016); 2016

International Society for Computational Biology (ISCB) y Asociación Argentina de Bioinformática y Biología Computacional (A2B2C)

Background Given a new compound, identify compounds of the training set that are structurally similar to this one it is the first step of a possible strategy to define the Applicability Domain (AD) of a model. To determine this similarity, the data should be grouped. We are interested in fuzzy clustering algorithms whose novelty resides in allowing an element belonging to more than one group using a degree of membership.As a next and last step of the process, using a series of statistical tests, the capacity of the predictor should be evaluated on the new compound. Determine the applicability domain of a model allows establishing the limits inside which the prediction of a compound will be reliable. The definition of the chemical domain of a predictive model allows a more practical use of it, as it will prevent spending time with compounds that will not be applicable. More specifically, in QSAR/QSPR (Quantitative Structure-Activity Relationship) modeling estimate the level of certainty to predict a new compound based on how similar it is with respect to the compounds used to build the model, is a crucial step.Results The results obtained by applying fuzzy clustering techniques for a variety of physicochemical properties allow evaluating the advantages and limitations of this AD strategy.Conclusions In this study the essential contribution is to identify the similarity of the compounds by using fuzzy clustering techniques and to validate this strategy in predicting relevant molecular properties to rational drug design.