It’s important to lay strong foundations for successful drug discovery at this first stage of the process. Our integrated target identification and validation platform combines AI with expert insights, and rigorous lab validation to guide targets through robust evaluation, ready for hit discovery.
Validated, high-quality hits, delivered through integrated technologies and expert collaboration, give you a confident starting point for faster drug discovery.
Turning promising leads into clinical candidates with speed, precision, and the scientific expertise to generate high-quality data and deliver real patient impact.
Delivering integrated, modality-agnostic drug discovery to tackle complex biology, accelerate development, and advance innovative therapies with confidence.
Advancing next-generation ADCs through payload-focused design, integrated expertise, and collaborative innovation to deliver safer, more selective therapies.
Driving biologics innovation through integrated design, structural biology, and multidisciplinary expertise to accelerate next-generation therapies from concept to clinic.
Combining deep therapeutic expertise with translational insight to design strategies, reduce risk, and accelerate discovery programs toward clinical success.
Accelerating oncology drug discovery through integrated expertise, innovative modalities, and translational insight to deliver candidates with real clinical impact.
Driving immunology and inflammation drug discovery through tailored assays, translational models, and integrated expertise for faster clinical success.
Advancing CNS drug discovery through integrated models, translational biomarkers, and multidisciplinary expertise to overcome complexity and accelerate therapeutic innovation.
Designing and advancing differentiated small-molecule therapies for obesity and diabetes through integrated expertise, mechanistic insight, and translational strategies.
Inobrodib, an exciting, first-in-class oral anti-cancer drug in clinical development by CellCentric, was collaboratively designed, synthesised and supported on its pre-clinical journey by an integrated project team at Sygnature Discovery. Inobrodib is now showing promising results in Phase I and II trials for multiple myeloma and other cancer types.
It’s important to lay strong foundations for successful drug discovery at this first stage of the process. Our integrated target identification and validation platform combines AI with expert insights, and rigorous lab validation to guide targets through robust evaluation, ready for hit discovery.
Validated, high-quality hits, delivered through integrated technologies and expert collaboration, give you a confident starting point for faster drug discovery.
Turning promising leads into clinical candidates with speed, precision, and the scientific expertise to generate high-quality data and deliver real patient impact.
Delivering integrated, modality-agnostic drug discovery to tackle complex biology, accelerate development, and advance innovative therapies with confidence.
Advancing next-generation ADCs through payload-focused design, integrated expertise, and collaborative innovation to deliver safer, more selective therapies.
Driving biologics innovation through integrated design, structural biology, and multidisciplinary expertise to accelerate next-generation therapies from concept to clinic.
Combining deep therapeutic expertise with translational insight to design strategies, reduce risk, and accelerate discovery programs toward clinical success.
Accelerating oncology drug discovery through integrated expertise, innovative modalities, and translational insight to deliver candidates with real clinical impact.
Driving immunology and inflammation drug discovery through tailored assays, translational models, and integrated expertise for faster clinical success.
Advancing CNS drug discovery through integrated models, translational biomarkers, and multidisciplinary expertise to overcome complexity and accelerate therapeutic innovation.
Designing and advancing differentiated small-molecule therapies for obesity and diabetes through integrated expertise, mechanistic insight, and translational strategies.
Inobrodib, an exciting, first-in-class oral anti-cancer drug in clinical development by CellCentric, was collaboratively designed, synthesised and supported on its pre-clinical journey by an integrated project team at Sygnature Discovery. Inobrodib is now showing promising results in Phase I and II trials for multiple myeloma and other cancer types.
Using advanced AI and proven modeling techniques to predict protein structures with confidence, accelerating structure-based drug design and discovering novel therapeutic possibilities.
Protein structure prediction has become a powerful tool for understanding small molecule interactions and guiding project strategies.
AI-driven advances such as AlphaFold now deliver highly accurate 3D models directly from sequence data, giving researchers new hypothetical insights for novel targets.
At Sygnature Discovery, we leverage a suite of complementary tools including AlphaFold Multimer, OmegaFold, and are currently evaluating Boltz-2 on our internal cluster, to generate high-confidence structural predictions. These models support every stage of drug discovery, from guiding ligand design to providing templates for molecular replacement in protein crystallography when solving novel structures.
By combining advanced algorithms with extensive drug discovery expertise, we translate these predictions into actionable strategies that accelerate progress and reduce risk.
AI-Driven Structure Prediction
AlphaFold enables accurate 3D modeling from sequence data. AlphaFold 2.0 marked a breakthrough for modelling non-membrane bound proteins and single protein domains, while AlphaFold Multimer extended capabilities to protein-protein interactions. Confidence scores for different regions make these models highly informative for drug discovery. Open-source tools such as Boltz-2 have further expanded possibilities, also supporting small-molecule affinity prediction and providing a complementary input to our structure-based design workflows.
The Continued Importance of Homology Modelling
Alongside new tools for protein structure prediction, homology modelling remains an important and versatile tool. This is particularly important where a selected functional state or protein conformation is sought such as an agonist bound GPCR model or a kinase in the DFG-out form and with a particular αC-helix or activation loop positioning.
Protein Flexibility and Conformational States
Protein structure prediction also supports protein crystallography by offering templates for molecular replacement when solving novel structures.
Evaluating Model Quality and Structural Utility
Core strengths in leveraging structural data and assessing its utility are crucial. One example as to why this is important can be seen using a model of LSD1 which was generated using AlphaFold Multimer. While an excellent model of the binding site is achieved, the binding site is auto inhibited by a separate region of the protein. There is no evidence that this is a physically meaningful effect, and the structure has to be modified to remove this prior to use in drug design.
Why Choose Sygnature
With a long track record of successful protein structure prediction across diverse targets, Sygnature unites cutting-edge modeling with extensive drug discovery experience. We offer a full suite of tools for protein structure prediction, protein-ligand and protein-protein complex modeling, and structure-guided drug design.
Sygnature’s expertise lies in rigorously evaluating model quality and applying structural insights effectively to support drug discovery. Whether starting from homology models or AlphaFold-type predictions, we assess models in context, considering function al homologues, SAR consistency, and physiological conditions. Our teams refine and assess structures within their broader biological and chemical context to ensure they are truly fit for decision-making.
This capability is strengthened by our large, collocated team of computational chemistry and bioscience experts, who work seamlessly with multidisciplinary project teams to leverage predicted and experimental structural data effectively. With no internal drug programs, our sole focus is your success, providing dedicated expertise, outcome-focused insights, and structural modelling that drives drug discovery projects forward.
AlphaFold multimer generated model of the IL3:IL3RA complex compared with the crystal structure.
Excellent agreement is seen in the protein-protein interface and model is sufficiently high quality to underpin a structure-based drug design discovery project. The PDB structure of the complex (code: 5UWC8) is shown with the IL3R portion in orange and the IL3 in magenta. The corresponding region modelled by AlphaFold Multimer is shown in yellow and cyan. Outside of the core domains, the predictions are more variable, but this region is not supported by available crystallographic data and is not the most relevant area for drug discovery.
Conformational plasticity e.g., in MEK1.
The image shows a comparison of ligand induced conformations in MEK1. The ATP analogue is shown for orientation, but other ligands are omitted. The 3VVH9 crystal structure is in blue and the 5HZE10 structure is shown in orange. MEK1 can adopt an allosteric pocket not seen in most other kinases.
Model of LSD1.
LSD1 with spurious auto-inhibition shown by peptidic sequence in cyan.
Ting Qin, Sergio Ernesto Ruiz Hernandez, Jason Shiers, Matthew Crittall, Andrew Novak, Colin Sambrook Smith Abstract Within a growing drug discovery company, scientists acquire (either through in house…
Andrzej T Slominski, Tae-Kang Kim, Radomir M Slominski, Yuwei Song, Shariq Qayyum, Wojciech Placha, Zorica Janjetovic, Konrad Kleszczyński, Venkatram Atigadda, Yuhua Song, Chander Raman, Cornelis J…
Journal Papers
Related Capabilities
We help you design smarter compounds and make faster decisions by combining AI- powered modeling, extensive scientific expertise, and secure, collaborative data access to solve complex challenges and accelerate your drug discovery journey.
Using advanced AI and proven modelling techniques to predict protein structures with confidence, accelerating structure-based drug design and discovering novel therapeutic possibilities.
Providing Relative Binding-Free Energy Calculation using Non-Equilibrium Switching (NES) powered by OpenEye’s Orion® platform. Delivering precise binding affinity predictions to help you rank compounds with confidence and prioritize synthesis decisions for the most promising candidates early in the discovery process.
Utilizing cutting-edge technologies and proprietary software to uncover hidden interactions, predict ligand stability, and make confident design choices that accelerate your drug discovery program.
Combining AI-driven design, bespoke ML models and LLM-powered bioinformatics with hands-on collaboration to help you discover Optimized drug candidates faster.
From binding sites to druggability, our computational chemistry and protein science teams help you find the right starting points faster, combining structural insight with cutting-edge tools to design compounds that move your project forward with confidence.
Leveraging the properties of known active molecules to rapidly generate and optimize new compounds through ligand-driven insights, AI-powered tools, and collaborative expertise to uncover new chemical space and accelerate discovery.
Helping you design better compounds with greater confidence. Utilizing quantum-level insights and dynamic simulations we predict affinity, guide optimization, and help you prioritize the right candidates faster.
We apply structure prediction and design across multiple protein families and therapeutic areas.
We evaluate models against structural and functional homologues, their consistency with known SAR data, and physiological context, including cellular or tissue compartment pH, and the role of cofactors and metal ions.
No. Experimental structures remain essential for confidence and fine detail. Where feasible, high-resolution data in complex with lead compounds significantly improves efficiency and success rates of the drug discovery project.
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Peak Proteins, NuChem Sciences, and SB Drug Discovery have now fully integrated with Sygnature Discovery.
