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    An MCP server that enables single-cell RNA sequencing analysis through natural language, supporting data processing, visualization, and analysis tasks without requiring coding knowledge.
    52
    12
    BSD 3-Clause
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    Provides chemical informatics endpoints for converting between chemical names and SMILES, processing molecule structures, and comparing molecules, with MCP compatibility.
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    MIT
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    Enables protein structure prediction using the Chai-1 model via Docker, with tools for small peptides, FASTA-based predictions, MSA-enhanced predictions, batch processing, and job management.
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    A framework that integrates Brain-Computer Interface technology with the Model Context Protocol to enable real-time neural signal processing and AI-powered interactions for healthcare, accessibility, and research applications.
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    MIT
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    MCP server for biopharma software infrastructure offering FDA 21 CFR Part 11 audit trails, CDISC SDTM/Allotrope data exports, 4PL dose-response curve fitting, and HIPAA PII/PHI redaction through 10 Zod-validated tools.
    10
    MIT
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    Enables running GROMACS molecular dynamics simulations through natural language, with tools for topology building, solvation, simulation management, and trajectory processing via the Model Context Protocol.
    31
    MIT
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    Enables AI assistants to perform quality control analysis on high-throughput sequencing data using FastQC and MultiQC. It supports single-file and batch processing of FASTQ/FASTA files and generates comprehensive, interactive summary reports.
    MIT
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    Enables RNA structure analysis, sequence evaluation, and inverse design using geometric deep learning models. Supports both quick computational analysis and long-running batch processing for generating RNA sequences that fold into target structures.
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    Enables protein stability prediction ($DeltaDelta$G and $Delta$Tm) and systematic mutation analysis using the SPIRED-Stab deep learning model. It supports single variant analysis, batch processing, and job monitoring via Docker-based inference.
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    Enables protein sequence analysis and structure prediction by extracting ESM-2 embeddings and batch processing FASTA files via Docker. It provides tools for large-scale embedding extraction, job monitoring, and model management within an MCP-compatible environment.