Our Contract Research Fields

At AI-Driven Therapeutics GmbH, we offer advanced contract research services to accelerate drug discovery and protein design for biotech and pharma companies. Using AI, we provide tailored solutions to optimize proteins, small molecule docking, and protein-ligand interactions.

Our expertise lies in developing and applying fine-tuned algorithms for modeling complex protein structures with high precision. We focus on protein binding and interface analysis to optimize interactions, identify therapeutic targets, and speed up drug development.

What sets us apart is our deep understanding of both biological and computational aspects of drug design. Our interdisciplinary team combines structural biology, AI, and software engineering to create practical, real-world solutions. With over 20 years of academic research experience, we provide clients with reliable tools to enhance research and streamline workflows.

We are committed to delivering high-quality, efficient contract research, driving innovation and reducing risk for faster, more successful outcomes.

Protein Design

Protein design is the process of creating new proteins or modifying existing ones to perform specific functions, often for therapeutic (new antibodies, vaccines or biologics) or industrial (Engineered enzymes, bio-pesticide and specific binders). This field combines principles from biology, chemistry, and bioinformatics to tailor proteins with desired properties, such as stability, specificity, and activity. Protein design is crucial for a variety of applications, including drug discovery, enzyme development, and biomanufacturing.

The process involves predicting and optimizing the 3D structures of proteins based on their amino acid sequences. Computational methods, such as molecular modeling, molecular dynamics simulations, and AI, are increasingly used to accelerate and improve the design process. These tools help to explore a vast number of possible protein structures and interactions, narrowing down the options to those most likely to achieve the desired outcomes.

By applying cutting-edge techniques like machine learning and AI-driven algorithms, protein design can be made more efficient, precise, and scalable. This enables the development of more effective therapeutics, such as customized biologics, vaccines, or enzyme-based treatments. At AI-Driven Therapeutics GmbH, we leverage AI to significantly reduce time and costs in protein design, creating tailored solutions that meet the unique needs of our clients in the pharmaceutical and biotechnology sectors.

Example Publications:
Torres-Paris C, Song HJ, Engelberger F, Ramírez-Sarmiento CA, Komives EA. The Light Chain
Allosterically Enhances the Protease Activity of Murine Urokinase-Type Plasminogen Activator.
Biochemistry. 2024 Jun;63(11):1434–44. DOI:10.1021/acs.biochem.4c00071

Engelberger F, Zakary JD, Künze G. Guiding protein design choices by per-residue energy
breakdown analysis with an interactive web application. Front Mol Biosci. 2023;10:1178035.
DOI:10.3389/fmolb.2023.1178035

Modeling and Interface Analysis

Modeling and interface analysis is a crucial component in the understanding and optimization of protein-ligand interactions, particularly in drug discovery and protein engineering. It involves creating detailed, accurate computational models of proteins, small molecules and/or peptides, and their interactions to predict how these entities bind and function together in a biological system.
At the heart of modeling is the construction of three-dimensional structures of proteins, often based on known sequences or experimental data, and using simulation techniques to predict how these proteins fold and interact with ligands. This helps in identifying the key binding sites, predicting the dynamics of interactions, and designing molecules that can bind more effectively to the protein of interest.
Interface analysis further enhances this process by focusing on the specific regions where proteins and ligands interact. By examining the physical and chemical properties of the binding interface—such as hydrophobicity, electrostatic charge, and hydrogen bonding—researchers can better understand the mechanisms of binding and optimize the design of more potent molecules.
At AI-Driven Therapeutics GmbH, we combine the knowledge of application and fine-tuning of modeling algorithms that enhance the understanding of protein binding and interactions. Our advanced AI-driven approach enables the optimization of potential treatments by providing detailed analysis of protein interfaces, ultimately improving the efficacy of therapeutic targets and accelerating the drug discovery process.

Example publications:

Schermeng T, Liessmann F, Ambrosius CK, Meiler J, Beck-Sickinger AG. Binding mode of cyclic chemerin-9 peptide and chemerinS157 protein at CMKLR. Chembiochem. 2024 Oct;e202400695. DOI:10.1002/cbic.202400695

Useini A, Engelberger F, Künze G, Sträter N. Structural basis of the activation of PPARγ by the plasticizer metabolites MEHP and MINCH. Environ Int. 2023 Mar;173:107822. DOI:10.1016/j.envint.2023.107822

Sala D, Engelberger F, Mchaourab HS, Meiler J. Modeling conformational states of proteins with AlphaFold. Curr Opin Struct Biol. 2023;81:102645. DOI: 10.1016/j.sbi.2023.102645

Richter PK, Blázquez-Sánchez P, Zhao Z, Engelberger F, Wiebeler C, Künze G, et al. Structure and function of the metagenomic plastic-degrading polyester hydrolase PHL7 bound to its product. Nat Commun. 2023 Apr;14(1):1905. DOI:10.1038/s41467-023-37415-x

Liessmann F, Künze G, Meiler J. Improving the Modeling of Extracellular Ligand Binding Pockets in RosettaGPCR for Conformational Selection. Int J Mol Sci. 2023;24(9). DOI:10.3390/ijms24097788

Blázquez-Sánchez P, Engelberger F, Cifuentes-Anticevic J, Sonnendecker C, Griñén A, Reyes J, et al. Antarctic Polyester Hydrolases Degrade Aliphatic and Aromatic Polyesters at Moderate Temperatures. Appl Environ Microbiol. 2022 Jan;88(1):e0184221. DOI:10.1128/AEM.01842-21

Small Molecule Docking

Small molecule docking is a computational technique used to predict the binding mode and analyze improving changes of a small molecule (such as a drug candidate) when it interacts with a target protein. This process is critical in drug discovery, as it helps to identify how potential drug molecules can bind to specific proteins or receptors, influencing their biological activity.

The docking process involves simulating the molecular interactions between the small molecule and the target protein, typically through the use of algorithms that consider both the molecular geometry and the energetic interactions. These simulations help in predicting the most likely binding sites and orientations, as well as the strength of the interaction.

In the context of drug discovery, docking can be used to screen large libraries of compounds, identifying promising candidates that might inhibit or activate the target protein’s function. This accelerates the search for novel therapeutics and reduces the need for costly and time-consuming experimental screening. Our team has a track record of developing and utilizing advanced docking and screening techniques to enhance the docking process and accuracy of drug design, supporting our clients in discovering the next generation of effective treatments.

Example publications:
Schermeng T, Liessmann F, Ambrosius CK, Meiler J, Beck-Sickinger AG. Binding mode of cyclic chemerin-9 peptide and chemerinS157 protein at CMKLR. Chembiochem. 2024 Oct;e202400695. DOI:10.1002/cbic.202400695

Useini A, Engelberger F, Künze G, Sträter N. Structural basis of the activation of PPARγ by the plasticizer metabolites MEHP and MINCH. Environ Int. 2023 Mar;173:107822. DOI:10.1016/j.envint.2023.107822

Sala D, Engelberger F, Mchaourab HS, Meiler J. Modeling conformational states of proteins with AlphaFold. Curr Opin Struct Biol. 2023;81:102645. DOI: 10.1016/j.sbi.2023.102645
Richter PK, Blázquez-Sánchez P, Zhao Z, Engelberger F, Wiebeler C, Künze G, et al. Structure and function of the metagenomic plastic-degrading polyester hydrolase PHL7 bound to its product. Nat Commun. 2023 Apr;14(1):1905. DOI:10.1038/s41467-023-37415-x

Liessmann F, Künze G, Meiler J. Improving the Modeling of Extracellular Ligand Binding Pockets in RosettaGPCR for Conformational Selection. Int J Mol Sci. 2023;24(9). DOI:10.3390/ijms24097788

Blázquez-Sánchez P, Engelberger F, Cifuentes-Anticevic J, Sonnendecker C, Griñén A, Reyes J, et al. Antarctic Polyester Hydrolases Degrade Aliphatic and Aromatic Polyesters at Moderate Temperatures. Appl Environ Microbiol. 2022 Jan;88(1):e0184221. DOI:10.1128/AEM.01842-21