AI in Proteins, Molecules, Early and Clinical Drug Development

Hello Everyone,
We would like to invite you to our AI in Pharma MeetUp event that would focus on the utilization of AI in Proteins, Molecules and Drug Development.
Save the date (24 June 2025) for this AI in Biochemistry MeetUp event in Munich (Address: Luise-Ullrich-Straße 14, 80636 Munich).

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

7:00 PM - Welcome Introduction

7:05 PM - Talk 1: From Raw Data to Virtual Patients: The Data Science Behind Digital Twins in Life Science
Dr. Elena Startseva, MD, MBA
Global Evidence Lead, Senior Clinical Program Lead
Boehringer Ingelheim

7:35 PM - Talk 2: Generative Reasoning AI Models for enhanced Protein-Ligand Bindings
Alexander Strunk, Msc. Physics
AI Scientist
Evercot AI

8:05 PM - Drinks, Snacks & Networking

Talks Details:
Talk 1
Title: From Raw Data to Virtual Patients: The Data Science Behind Digital Twins in Life Science

Abstract:
Digital twins—virtual representations of biological systems that evolve alongside their real-world counterparts—are rapidly transforming the landscape of life sciences and medicine. In drug development, they offer a powerful way to simulate disease progression, predict treatment outcomes, and optimize clinical trial design—potentially reducing costs, timelines, and patient risk.
This talk explores how digital twins are being applied across the pharmaceutical pipeline and beyond. We will look at how they’re used to create synthetic control arms, support regulatory submissions, and personalize therapies based on patient-specific data.
We’ll also examine the broader implications of this technology: How do regulators evaluate and approve decisions made with the help of virtual patients? What ethical questions arise when we simulate human biology at scale? And how can we ensure transparency, fairness, and accountability in these data-driven systems?
Rather than focusing on the technical mechanics of model building, this talk will highlight the real-world impact of digital twins, the evolving regulatory landscape, and the opportunities and responsibilities for data professionals working in healthcare and biotech. Whether you're analyzing clinical data, designing digital health tools, or shaping policy, digital twins represent a frontier where data science meets medicine—and where virtual models are beginning to shape real-world care.

Speaker:
Dr. Elena Startseva, MD, MBA
Global Evidence Lead, Senior Clinical Program Lead,
Boehringer Ingelheim

Biography:
Dr. Elena Startseva, MBA is a physician and pharmaceutical leader with over 20 years of global experience in clinical and medical development, medical affairs, and portfolio strategy. Currently at Boehringer Ingelheim, she leads evidence generation for late-phase programs in cardiovascular, renal and metabolic therapeutic area. Elena has held senior roles at Novo Nordisk, Sandoz, and InflaRx, and is passionate about applying data-driven innovation—like digital twins—to advance personalized medicine and patient care.

Talk 2
Title: Generative Reasoning AI Models for enhanced Protein-Ligand Bindings

Abstract:
Generative models have significantly advanced structure-based drug design (SBDD) in recent years, unlocking substantial potential for future breakthroughs. Generating small molecules guided by their three-dimensional structural fit and interaction with target proteins is central to structure-based drug design, a key strategy within modern drug discovery. This talk delves into the mechanisms and methodologies underlying these generative approaches, highlighting key techniques and exploring innovative strategies to augment them with reasoning models for improved predictive power and interpretability.

Speaker:
Alexander Strunk, MSc. Physics
AI Scientist, Evercot AI

Biography:
Alexander Strunk is an AI Scientist at Evercot AI, focusing his research on Geometric Deep Learning and Reasoning AI Models. His work bridges advanced mathematical frameworks with cutting-edge machine learning techniques, aiming to develop robust, interpretable, and scalable AI solutions.
Previously, Alexander earned his degree in Physics from Ludwig Maximilian University (LMU) Munich, where he specialized in mathematical physics and differential geometry.