Molecular Interactions and Binding Prediction in Drug Discovery

Hello everyone,
We would like to invite you to our AI in Pharma MeetUp focused on Molecular Interactions and Binding Prediction in Drug Discovery

Please save the date: 19 May 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:
7:00 PM - Door Open, Drinks & Networking

7:30 PM - Welcome Introduction

7:35 PM - Talk 1: Target Preference Mapping: A Connectivity-Free Approach to Ligand and Protein Interaction Prediction
Dr. Sergey Vilov
Machine Learning Engineer ,
Khumbu AI

8:05 PM - Talk 2: A Structured Map of Antibody Development: Opportunities for AI
Dr. Martin Schatte
Head of Protein Science,
Antibody Development,
Lead Discovery Center (LDC) GmbH

8:35 PM - Drinks, Snacks & Networking

Talks Details:

Talk 1
Title: Target Preference Mapping: A Connectivity-Free Approach to Ligand and Protein Interaction Prediction

Abstract:
Structure-based drug discovery has achieved remarkable success in protein structure prediction, yet the translation from protein structure to functional molecular binders remains fundamentally limited. Existing machine learning and docking approaches rely heavily on historical ligand data, chemical connectivity, and affinity measurements, resulting in strong bias, poor generalization, and limited applicability to novel chemistry, protein–protein interactions, and dynamic systems.
Target Preference Mapping (TPM) is a connectivity-free, affinity-independent framework that learns interaction preferences directly from structural microenvironments. TPM decomposes protein binding sites into local spatial voxels and predicts probabilistic preferences for ligand atom electronic states without encoding ligand structure or bonding. This abstraction enables the model to learn non-bonded chemistry principles, directionality, and conformational s