Natural and Life Sciences
Helmholtz Zentrum München
Benjamin Schubert is a team leader at Helmholtz Zentrum München. His goal is to develop more effective and safe vaccines and biotherapeutics using AI methods. During his PhD at the University of Tübingen, Dr. Schubert designed algorithms that support every step of vaccine development, from antigen identification to selection and vaccine assembly, enabling a more streamlined and resource-efficient development. In a recent collaboration, Dr. Schubert could experimentally demonstrate that his algorithm truly improves vaccine efficacy beyond human designs. During his postdoc at Harvard Medical School, Dr. Schubert designed an AI-based method to modify biotherapeutics to prevent immunological responses that otherwise would have negative effects on efficacy and safety. Initial experimental evaluations of computationally re-designed biotherapeutics were encouraging and demonstrated the prospects of his approach to improving the safety and efficacy of biotherapeutics.
Dr. Schubert is interested in how machine learning can be used to determine expressive latent representations of amino acid sequences to predict biophysical properties accurately and generate new sequences with optimized design criteria. To this end, he is developing novel generative and supervised deep neural networks and combines those architectures with techniques from multiple instances and multi-task learning. Integrated into discreet optimization problems, Dr. Schubert is using these models to solve often arising biotherapeutic engineering tasks such as arranging peptides optimally to improve vaccine efficacy or finding the best alterations of a drug to reduce potential side effects. Such optimization problems often have multiple design criteria that need to be fulfilled simultaneously. Therefore, Dr. Schubert is also developing new strategies to find optimal solutions to discrete multi-objective optimization problems.