Publications
CADD to design PICsComputer Aided Drug Design in the Development of Proteolysis Targeting Chimeras
Proteolysis targeting chimeras represent a class of drug molecules with a number of attractive properties, most notably a potential to work for targets that, so far, have been in-accessible for conventional small molecule inhibitors. Due to their different mechanism of action, and physico-chemical properties, many of the methods that have been designed and applied for computer aided design of traditional small molecule drugs are not applicable for proteolysis targeting chimeras. Here we review recent developments in this field focusing on three aspects: de-novo linker-design, estimation of absorption for beyond-rule-of-5 compounds, and the generation and ranking of ternary complex structures. In spite of this field still being young, we find that a good number of models and algorithms are available, with the potential to assist the design of such compounds in-silico, and accelerate applied pharmaceutical research.
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Current Challenges in PIC designCurrent Challenges in Small Molecule Proximity-Inducing Compound Development for Targeted Protein Degradation Using the Ubiquitin Proteasomal System
Bivalent proximity-inducing compounds represent a novel class of small molecule therapeutics with exciting potential and new challenges. The most prominent examples of such compounds are utilized in targeted protein degradation where E3 ligases are hijacked to recruit a substrate protein to the proteasome via ubiquitination. In this review we provide an overview of the current state of E3 ligases used in targeted protein degradation, their respective ligands as well as challenges and opportunities that present themselves with these compounds.
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Ternary Complex PredictionBayesian Optimization for Ternary Complex Prediction (BOTCP)
Proximity-inducing compounds (PICs) are an emergent drug technology through which a protein of interest (POI), often a drug target, is brought into the vicinity of a second protein which modifies the POI's function, abundance or localisation, giving rise to a therapeutic effect. One of the best-known examples for such compounds are heterobifunctional molecules known as proteolysis targeting chimeras (PROTACs). PROTACs reduce the abundance of the target protein by establishing proximity to an E3 ligase which targets the protein towards degradation via the ubiquitin-proteasomal pathway. Design of PROTACs in silico requires the computational prediction of the ternary complex consisting of POI, PROTAC molecule, and the E3 ligase. Here, we present a novel machine learning-based method for predicting PROTAC-mediated ternary complex structures using Bayesian optimization.
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Protein-protein interactionsProtein-protein interaction prediction for targeted protein degradation
Recent developments in machine-learning-based molecular fragment linking have demonstrated the importance of Protein-protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, computer-aided approaches have been developed as screening tools, but these tools are primarily based on sequence information and are therefore limited in their ability to address spatial requirements and have thus far not been applied to targeted protein degradation.
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Linker GenerationDecoupled coordinates for machine learning-based molecular fragment linking
Recent developments in machine-learning-based molecular fragment linking have demonstrated the importance of informing the generation process with structural information specifying the relative orientation of the fragments to be linked.
A significant impact on the quality of the generated linkers is demonstrated numerically. The amount of reliable information within the different types of degrees of freedom is investigated. Ablation studies and an information-theoretical analysis are performed. The presented benefits suggest the application of a complete and decoupled relative coordinate system as a standard good practice in linker design.
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Technology OverviewIdentification of Drug-Target Interactions using Xanthos Match Maker™
In this short whitepaper, we have tried to explain our Xanthos Match Maker™ tool for DTI prediction and virtual screening. Our solution not only can achieve a significant performance on a benchmark dataset but also can provide a reasonable prediction on out-of-distribution data. Moreover, the Celeris One platform can cope with the low amount of input data and extract maximum information from sparse input data.
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Case StudyIdentification of Novel West Nile Virus NS2B/NS3 Protease Warheads
In this whitepaper we showcase the utilities of Xanthos Match Maker™ in rapidly and accurately predicting biomolecular interactions between drug/target pairs. Based on a deep learning approach, the presented drug-repurposing case study has arguably demonstrated promising as well as competitive results.
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