Revolutionizing Peptide Therapeutics with Precision and Innovation
Welcome to our cutting-edge SWARSH-P Suite, a state-of-the-art platform meticulously optimized for the seamless design of peptide-based therapeutics. Adopting a hybrid approach, SWARSH-P is built on a solid foundation comprising three pillars:
1. Comprehensive Experimental Data Integration
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Reliable datasets on protein-protein and protein-peptide interactions.
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Supports complex structures.
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Experimentally validated, developable, non-natural amino acids for enhanced functionality.
SWARSH-P represents the pinnacle of technology in peptide-based therapeutic design, delivering precision and efficacy.
Overview of our peptide-based rational drug design process using the SWARSH-P platform
A. Target Identification:
Deep learning and physics-based methods for identifying druggable sites. It also includes the ability to recognize targets previously considered undruggable.
B. Peptide Generation and Optimization:
Generative AI combined with physics-based approaches for peptide sequence generation. Computational refinement of physicochemical properties, toxicity mitigation, and affinity enhancement.
C. Structural Enhancements:
Optional peptide cyclization and incorporation of novel or experimentally validated non-natural amino acids, powered by advanced Generative-AI and physics-based tools.
D. Experimental Validation and Lead Optimization Support:
We offer our support during your wet lab SAR efforts to accommodate refinements ensuring the peptide drug's desirable efficiency.
Experience the future of therapeutic innovation with SWARSH-P: your partner in designing peptide-based therapeutics with unparalleled precision and reliability.
3. AI-Powered Innovation
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Objective-driven machine learning and generative AI tools trained on diverse and high-quality datasets.
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Designed for precise and innovative peptide design.
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Fine-tuned to incorporate experimentally developable and scalable non-natural amino acids seamlessly.
2. Advanced Analytical Framework
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A suite of physics-based and robust statistical techniques for inter-molecular interaction analysis and optimization.
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No new tools and assay techniques are required.