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Introducing CARATE: Finally Speaking Chemistry Through Learning Hidden Wave-Function Representations on Graph-Based Attention and Convolutional Neural Networks

Julian M. Kleber

Computer-aided drug design is stepping into a new era. Recent developments in statistical modelling, including deep learning, machine learning and high-throughput simulations, enable workflows and deductions unachievable 20 years ago. The key interaction for many small molecules in the context of medicinal chemistry is via biomolecules. The interaction between a small molecule and a biological system therefore manifests itself at multiple time and length scales. While the human chemist may often grasp the concept of multiple scales intuitively, most computer technologies do not relate multiple scales so easily. Numerous methods that try to tackle multiple scales in the field of computational sciences have been developed. However, up to now it was not clear that the problem of multiple scales is not only a mere issue of computational abilities but even more a matter of accurate representation. Current representations of chemicals lack the descriptiveness necessary for today’s modelling questions. This work introduces a novel representation of small and large molecules. The representation is obtained by the novel biochemical and pharmaceutical encoder (CARATE). In the following work, the regression and classification abilities of the learned representation by CARATE are evaluated against benchmarking datasets (zinc, alchemy, mcf-7, molt-4, yeast, enzymes, proteins) and compared to other baseline approaches. CARATE outperforms other graph-based algorithms on classification tasks relating to large biomolecules and small molecules, as well as on quantum chemical regression tasks of small molecules.