Abstract: A computational system, which may be an artificial intelligence (AI) system, allows for the electronic representation, modeling, generation, rendering, simulating, and querying of polymeric materials by encoding architectural features as nodes and embedding a directed graph of the polymeric materials within a database system. The embedding of chemical data from multiple nodes within the directed graph data structure enables a scalable data model for containing materials data within computational systems. The computational system may be used for predictive modeling of new polymeric materials and for rendering and/or simulating stochastic polymeric ensembles.

Abstract: Techniques are provided for predicting antibiotic resistance from functional omics data and recommending complementary combinations of antibiotics. According to an embodiment, computer implemented method can comprise identifying, by a system operatively coupled to at least one processor, one or more proteins that have one or more functional domains associated with at least one code selected from a coding system for a set of phenotypes, and modelling, by the system, the one or more proteins as a functional capacity vector. In some implementations, the method can further include selecting the coding system and/or the at least one code based on a phenotype of interest. The method can further comprise employing, by the system, the functional capacity vector to identify one or more antibiotic compounds to which an organism within the set of phenotypes is resistant or susceptible, and/or to predict complementary antibiotic combinations.