Optimization Algorithms for Chemoinformatics and Materialinformatics

Modeling complex phenomena in chemoinformatics and material-informatics can often be formulated as single-objective or multi-objective optimization problems (SOOPs or MOOPs). For example, the design of new drugs or new materials is inherently a MOOP since drugs/materials require the simultaneous optimization of multiple parameters.
In this chapter, we present several algorithms based on global stochastic optimization. These algorithms are applicable to multiple tasks in chemoinformatics and material-informatics including the following: (1) representativeness analysis, namely the selection of a representative subset from within a parent data set. (2) Derivation of quantitative structure–activity relationship models. Such models are used in multiple areas to predict activities from structures and to provide insight into factors (e.g., descriptors) governing activities. (3) Outlier removal to clean a parent data set from objects (e.g., compounds) that may demonstrate abnormal behavior.
The performances of the new algorithms were evaluated using different data sets and multiple measures and were found to outperform previously reported methods.
Due to the modular nature of the algorithms, they could be combined into machinelearning workflows. In the final section, we provide an example of one such workflow and apply it to the development of predictive models in pharmaceutical and material sciences.