DrugDiscoveryForschung

DrugDiscoveryForschung is an interdisciplinary field of research dedicated to identifying and developing new therapeutic agents. It combines elements of medicinal chemistry, pharmacology, biology, and data science within a German-speaking and international context. The aim is to translate biological insights into viable drug candidates through integrated discovery workflows that span target identification, hit discovery, lead optimization, and preclinical evaluation.

Key methodologies include target validation, high-throughput screening, fragment-based and structure-based drug design, medicinal chemistry optimization, and ADMET profiling; supported by computational approaches such as cheminformatics, molecular docking, machine learning, and systems biology.

Process and organization: typically conducted in academia-industry collaborations or biotech setups; cross-functional teams; iterative cycles; early consideration of safety, regulatory requirements, and scalable synthesis; use of both in vitro and in vivo models; use of biomarkers for translational readouts.

Impact and challenges: essential for addressing unmet medical needs; fosters innovation; challenges include high attrition rates, cost, data sharing, reproducibility, regulatory obstacles, and ethical considerations; ongoing trends include open science, data standards (ontologies, FAIR data), and integration of AI-driven discovery.