Steps towards de Novo 3D Ligand and Protein Design via Deep Learning (eBook)

Master's Thesis from the year 2019 in the subject Computer Science - Bioinformatics, grade: 1,3, University of Tubingen (Faculty of Science / Department of Bioinformatics), language: English, abstract: Since 2013 generative neural networks are used for tasks like generating audio or image data. However, there is no publication which uses their capabilities for de novo ligand and or protein design yet. In this work, a generative neural network is introduced – the PG-VUGAN (progressively growing variational U-NET generative adversarial network) with which it is intended to fill this knowledge-gap.

The PG-VUGAN consumes a rich molecular image (RMI) of either the ligand or the pocket and can generate its complementary counterpart. This is practically demonstrated for de novo ligand design in this paper. The RMI is a new image-based format for molecular structures, which is specifically designed for being performantly processed by convolutional neural networks. Its suitability is demonstrated by developing a state-of-the-art binding-affinity regressor. Summing up, a first step towards artificially generated ligands and proteins via generative neural networks was made.

Protein-ligand interactions control cellular processes and are therefore essential for all living beings. Hence, generating complementary ligands for a protein-structure or vice-versa the prediction of complementary protein-structures for ligands is a desirable intent of science. Possible use-cases for de novo ligand and protein design can be found in all fields of biotechnology and reach from drug discovery and individual medicine up to the creation of artificial enzymes.

Designing these molecules from scratch is challenging; and yet, the technology for de novo design is in its early stages. The reason is, that existing tools rely on the assumptions of experts and on mathematical approximations with which their real physical nature can only be simulated partly. Artificial neural networks promise to pass these limitations.