Generative AI-Driven Protein and Small Molecule Design

Hello everyone,
We would like to invite you to our AI in Pharma MeetUp focused on Generative AI-Driven Protein and Small Molecule Design.

Please save the date: 31 March 2026 for this AI in Biochemistry MeetUp in Munich (Address: Luise-Ullrich-Straße 14, 80636 Munich).

Many thanks to our co-sponsor: Reply Concept

Agenda Overview:
6:30 PM - Door Open, Drinks & Networking

7:00 PM - Welcome Introduction

7:05 PM - Talk 1: Bridging Wet Lab and In silico: AI‑Ready Phage Display Platform for VHH Discovery
Dr. Michael Blank
Senior Director MAP Library Technologies,
BioNTech

7:35 PM - Talk 2: AI-guided Protein Design for Biomedical Applications
Prof. Dr. Thomas Schlichthärle
AG AI-guided Protein Design,
TUM University Munich

8:05 PM - Drinks, Snacks & Networking

Talks Details:

Talk 1
Title: Talk 1: Bridging Wet Lab and In silico: AI‑Ready Phage Display Platform for VHH Discovery

Abstract:
Artificial intelligence (AI) is increasingly contributing to the early discovery of antibodies and Nanobodies (VHHs). However, the performance and generalizability of AI models critically depend on access to large, high‑quality training datasets, which are currently scarce in the antibody discovery field. At BioNTech, we have established a VHH phage display–based discovery platform that is tightly integrated with next‑generation sequencing (NGS) and dedicated bioinformatics workflows. This digitalized experimental framework enables high‑resolution investigation of target‑specific VHH repertoires and the systematic identification of VHHs with desired binding properties. By linking comprehensive sequence–function information, our bioinformatics pipeline not only delivers optimized panels of VHH candidates, but also generates high‑quality datasets suitable for training advanced AI models. This, in turn, enables experimental–computational feedback loops that iteratively refine both the discovery process and the underlying models. In this presentation, we will demonstrate how our integrated VHH phage display and data analytics platform can help close the data gap in Ab/VHH discovery and accelerate the development of robust, generalizable AI approaches in this domain.

Speaker:
Michael Blank, PhD, Senior Director MAP Library Technologies, BioNTech

Biography:
Michael Blank, PhD, is Senior Director MAP – Library Technologies at BioNTech, where he established a high-throughput ligand-discovery platform which is enhanced by NGS and currently linked to ML/AI to discover and engineer VHH antibodies. Trained as an organic chemist, he earned his PhD from the University of Tübingen, pioneering methods to select tumor-targeting aptamers. He automated in vitro selection techniques as Head of Aptamer Technologies at NascaCell, then linked ligand discovery with deep sequencing at Helmholtz Centre Munich. In 2012 he co-founded AptaIT, serving as CSO and developing bioinformatics tools that accelerate ligand identification from immune bleeds and in-vitro selections.

Talk 2
Title: AI-guided Protein Design for Biomedical Applications

Abstract:
Recent advances in AI-guided protein design enable the generation of novel receptor binders with high experimental success rates. We have applied this approach to design new-to-nature FGFR isoform-specific agonists that direct stem cell fate toward arterial over venous identity, and TrkA agonists that retain neurotrophic activity while reducing nociceptive signaling commonly observed in the natural binding partner NGF. These studies demonstrate that signaling bias can be encoded directly into the molecular architecture of de novo designed agonists, and we propose that integrating such growth factor mimetics into stem-cell differentiation workflows will unlock new opportunities in regenerative medicine and disease modeling.

Speaker:
Prof. Dr. Thomas Schlichthärle
AG AI-guided Protein Design,
TUM University Munich

Biography:
Prof. Dr. Thomas Schlichthärle studied Molecular Medicine (B.Sc.) in Tübingen and Molecular Bioengineering (M.Sc.) at TU Dresden. After a research stay at the Wyss Institute for Biologically Inspired Engineering in Boston, he started his PhD at the MPI of Biochemistry in Munich working on DNA-PAINT based super-resolution microscopy of single proteins. He then joined the lab of Prof. David Baker at the University of Washington in Seattle as a postdoctoral researcher, working on synthetic growth factors. In 2025, he was appointed Tenure Track Assistant Professor (W2) for AI-guided Protein Design at TUM.