Dr Alexander Hillisch

Vice President and Head of Computational Molecular Design Bayer AG

Alexander Hillisch is a Vice President and Head of Computational Molecular Design at Bayer AG, Wuppertal, Germany. His team supports small molecule and biologics drug discovery in cardiology with computational chemistry, chemoinformatics, machine learning, in silico ADMET and structural bioinformatics techniques. From 1998 to 2003 he headed a research group at EnTec GmbH, Jena, Germany, a subsidiary of Schering AG, Berlin. There he was project manager in preclinical research and involved in the computer-aided design and pharmacological characterization of drugs against gynecological diseases and cancer. He conducted his Ph.D. thesis at the Institute of Molecular Biotechnology (IMB), Jena in the area of biophysics (NMR, FRET) and molecular modeling. Alexander Hillisch received his Ph.D. in Biochemistry with Prof. Peter Schuster in 1998 and his diploma in Pharmacy in 1995 from the University of Vienna, Austria. He is co-author of ~50 research papers, 2 books and 63 pharmaceutical compound patents which led to 6 clinical development candidates. Alexander teaches “Molecular pharmacology and Drug Design” at the University of Cologne from which he received a honorary professorship in 2010. He is a member of the board of directors at the Structural Genomics Consortium (SGC, Toronto & Oxford), and at the scientific advisory board of Cresset and EUROPIN.

Day One - 05 July 2022

10:00 AM Holistic Workflow: de novo Structure Based Design of Small Molecules

A holistic de novo drug design approach, developed in collaboration with Schrödinger is presented which is enabling Bayer to gain efficiencies and ultimately discover drugs faster. It has and will continue to open the door to more possibilities in drug discovery to discover novel molecules – molecules that may have otherwise never been discovered with tried-and-true experimental efforts.

This session will address:

  • ML models for predictions based on experimental ADMET and chemical synthesis data
  • Active learning free energy perturbation approach
  • Application examples from active drug discovery projects