Biology subjects

Gampp, O.

Publications and source records attributed to Gampp, O..

3 recordsLinked to original sources

bioRxiv2026

A Unified 3D Generative Model for Synthesizable Structure-Based Drug Design

Traditional screening-based drug discovery is inherently limited by the astronomical scale of the chemical space. Generative modelling offers a compelling alternative to the classical search paradigm and enables rational, bottom-up design of novel and target-specific small molecules. However, its impact has been hampered by challenges in synthetic accessibility of the designed compounds and lack of large-scale experimental validation. Here, we introduce LDDM (Large Drug Discovery Model), a generative framework that supports a range of drug discovery tasks, including constrained and unconstrained docking, fragment linking and growing, and de novo design. We further introduce a programmable design algorithm that enables accurate design of synthetically accessible compounds satisfying various fine-grained objectives. We experimentally validated the designed or optimised ligands for five therapeutically relevant protein targets. In all cases, LDDM achieved high success rates, allowing us to identify molecules with confirmed binding affinity while synthesizing only a small number of generated compounds. The best designs were structurally characterised through NMR spectroscopy and X-ray crystallography, demonstrating high prediction accuracy. Overall, LDDM provides a scalable and flexible platform for the rapid and tailored design of small molecules and non-natural peptides for therapeutic applications.

bioinformatics↗

bioRxiv2025

FragmentScope - exploring the fragment space with learned surface representations

Exploring fragment chemical space for ligand design remains a major challenge in early stage drug discovery. This task is particularly challenging due to the small size, low specificity, and weak binding affinities of low molecular weight (MW) fragments. We present FragmentScope, a computational pipeline that uses learned protein surface fingerprints to guide fragment placement and small molecule generation. By using a contrastive learning model trained on protein-ligand interactions, we built a database of surface-fragment pairs, which enables fast and accurate fragment placement in the target protein pocket. We demonstrate its effectiveness on benchmark datasets, achieving robust placement accuracy. FragmentScope also enables the design of small molecules based on the predicted fragments ensuring synthetic accessibility. We experimentally validated Fragmentscope across 5 different targets with binding assays and structural characterization. Our approach shows high success rates in fragment discovery and yielded promising leads for designed ligands. FragmentScope offers a scalable, structure-guided approach for narrowing chemical space and identifying prospective scaffolds, accelerating the early stages of small molecule design.

bioinformatics↗

bioRxiv2023

Overall biomass yield of multiple nutrient sources

Microorganisms utilize nutrients primarily to generate biomass and replicate. When a single nutrient source is available, the produced biomass increases linearly with the initial amount of the available nutrient. This linear trend can be predicted to high accuracy by "black box models" that consider growth as a single chemical reaction with nutrients as substrates and biomass as a product. Since natural environments typically feature multiple nutrients, we here quantify the effect of co-utilization of multiple nutrients on bacterial biomass production. First, we demonstrate a mutual effect between the metabolism of different nutrient sources where the ability to utilize one is affected by the other. Second, we show that for some nutrient combinations, the produced biomass is no longer linear to the initial amount of nutrients. These observations cannot be explained by the traditional "black box models", presumably because the metabolism of one nutrient affects another, which is not accounted for by these models. To capture these observations, we extent "black box models" to include catabolism, anabolism, and biosynthesis of biomass precursors and phenomenologically add a mutual effect between the metabolism of the nutrient sources. The expanded model qualitatively recaptures the experimental observations and, unexpectedly, predicts that the produced biomass is not only dependent on the combination of nutrient sources but also on their relative initial amounts. We validate this prediction experimentally by demonstrating how measurement of the produced biomass can be used to determine how each nutrient effects the metabolic processes of another.

microbiology↗