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.
We combine AI-driven design and predictive modeling with hands-on collaboration to help you discover better drug candidates faster, turning complex data into clear,
AI is transforming drug discovery, and at Sygnature Discovery, AI is embedded in every stage of our drug discovery workflows.
We use it for large-scale data mining and literature analysis, generative design that explores vast chemical spaces, protein homology modeling, machine learning models that guide multiparameter optimization, and predictive ADMET profiling. Our AI-driven platform evolves continuously, integrating emerging technologies to expand scientific capabilities and reinforce our position as a trusted CRO partner.
We combine cutting-edge technology with collaborative science to help you move from idea to candidate faster and with greater confidence.
Generative AI enabled Drug Discovery at Sygnature
We’ve built SygDesign, our proprietary AI-powered compound design tool, to accelerate Hit-to-Lead and Lead Optimization programs. SygDesign enables chemists to design and assess their ideas in real time on an intuitive secure cloud-based platform.
We also utilize industry leading de novo drug design solutions such as Makya (Iktos) to accelerate the exploration of massive chemical spaces. Makya supports synthetic tractability for rapid library synthesis in Hit-to-Lead, using commercially available building blocks and known synthetic routes. For bespoke synthesis in Lead Optimization, Makya offers full de novo design capabilities. Design in Makya can also be driven by structural and pharmacophore constraints, using docking or 3D shape similarity within a reinforcement learning workflow, enabling more targeted and efficient molecule generation.
Our approach is further guided by machine learning (ML) models predicting potency and ADMET properties, addressing multiparameter optimization (MPO) throughout the DMTA cycle. We have applied Generative AI (GenAI) to multiple H2L and Lead Optimization projects for tasks such as core hopping, linker design, macrocyclisation and library design.
We integrate machine learning, bioinformatics, and advanced AI tools to accelerate discovery, from predictive models that guide multiparameter optimization to generative design and protein structure insights, giving you clearer decisions and faster progress.
Machine Learning (ML)
We apply machine learning best practices to bespoke custom predictive models for a wide range of properties. These models provide a systematic approach to the Multi Parameter Optimization (MPO) challenges in drug discovery, maximizing the impact of experimental data.
Models can be built using internal project data, supplemented by public domain data where appropriate, and are securely accessible to project teams through our KnimeHub integration. These models predict potency and ADMET profiles, including solubility, clearance, and BBB permeability supporting informed decisions across the DMTA cycle.
Target Identification and Validation
During target identification and validation, we employ bioinformatics platforms that incorporate large language models (LLMs) for literature mining, gene/proteomics analysis, homology modelling and 3D binding pocket characterization. Our approach combines major public domain databases with commercial software and bespoke Python interfaces for seamless integration.
Our computational chemists have access to additional AI tools including MedChemica for molecular matched pair analysis and OmegaFold (from OpenEye Orion) and AlphaFold (hosted on our in-house high-performance cluster) for homology modelling.
Key Benefits
We leverage AI at every stage of discovery, helping you confidently accelerate timelines from compound to clinic.
AI integrated across all stages
Our in-house and commercial teams have adopted AI based tools & techniques to fit seamlessly across all stages of drug discovery for faster, smarter decision making.
Generative AI exploration of massive chemical spaces
We utilize Generative AI compound design tools to efficiently search and sample massive chemical spaces to develop novel ideas to support Hit to lead, Lead optimization while strengthening IP
Machine learning approach to the Multi Parameter Optimization (MPO)
Our ML led systematic approach to the Multi Parameter Optimisation (MPO) challenge in drug discovery maximizes impact and value of experimental data by building predictive models to support DMTA cycles and triage design ideas.
Leading generative artificial intelligence (GenAI) algorithms for de novo design are being used routinely within our medicinal chemistry programmes to enable rapid idea generation to address project objectives. We have successfully deployed Makya, a leading software platform for de novo molecular design from Iktos onto projects to address our client’s needs. These tools… Read More
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FAQs
AI and ML accelerate drug discovery through generative compound design, predictive ADMET modeling, protein structure prediction, and large-scale data mining. These tools fit seamlessly into all stages of the DMTA cycle.
Generative AI creates novel chemical structures and optimizes designs by exploring vast chemical spaces. We use our in-house tool SygDesign and industry-leading platforms like Makya (Iktos) to support lead optimization and multiparameter optimisation (MPO).
Machine learning builds bespoke predictive models for potency and ADMET properties (e.g., solubility, clearance, BBB permeability), maximizing experimental data impact and guiding compound selection throughout the DMTA cycle.
Seamless AI integration across all drug design stages, efficient chemical space exploration via Generative AI, and systematic ML approaches to MPO improving predictive accuracy and decision-making.
No. AI accelerates design and prediction, but experimental validation remains essential. AI complements, not substitutes laboratory data to ensure reliability.
Related Solutions
We bring together a dynamic blend of new talent and experienced pharmaceutical industry experts from a round the world, delivering excellence across every stage of discovery.
Transforming complex biological data into actionable insights through integrated expertise, bespoke workflows, and collaborative strategies for smarter drug discovery.
Transforming early leads into clinical-quality candidates through integrated expertise, rapid decision-making, and optimized strategies for safety, potency, and exposure.
