"Blackmagic Design" matching MCP servers:

Enables LLM agents to design proteins, predict structures, score interfaces, and run molecular dynamics simulations through a unified interface.

A Claude Code plugin and MCP server for end-to-end clinical trial design.

An MCP server that gives LLM agents access to computational protein design tools.

Enables anaerobic digester design from feedstock characterization to dynamic simulation using a complete mADM1 model with 63 components, including AI-powered state generation and thermodynamic pH solving.

An intelligent MCP server for the automated design and sizing of aerobic biological wastewater treatment systems using membrane bioreactor (MBR) technology. It leverages ASM2d kinetics to perform mass balance calculations, flowsheet selection, and generate detailed engineering reports.

MCP server exposing REINVENT4 generative molecular-design operations as validated, LLM-callable tools for drug discovery, including sampling, transfer learning, reinforcement learning, and molecular analysis.

Provides read-only tools for planning, writing, and auditing lesion network mapping and connectome-based localization papers, including design classification, methods checklists, phrasebooks, limitations, and manuscript auditing.

Enables AI-assisted aquaculture manuscript writing through six specialized agents for literature review, drafting, results, abstract, copyediting, and integrity checks, all accessible from MCP-compatible chat clients.

Enables AI-assisted molecular biology experiment design with tools for qPCR primer design, cloning strategy optimization, TaqMan probe design, and multiplex compatibility analysis.

Enables AI assistants to rapidly gather and synthesize structural, chemical, conservation, and literature data about protein binding pockets for drug-target triage. Useful as a reconnaissance step before computational binder design.

Enables design-correct, conversational analysis of NHANES public-use data by automating file discovery, merging, weight selection, pooling, survey variance estimation, and mortality-linked Cox models.

Enables MCP clients to discover and call typed tools for protein backbone design, sequence design, complex folding, and binder design via BioNeMo NIMs, with a deterministic simulator by default and optional live routing.

Enables molecular design and simulation through 45 chemistry tools including pKa calculations, geometry optimization, conformer searches, docking, protein cofolding, and ADMET predictions powered by Rowan's computational chemistry platform.

Enables read-only and preview MCP workflows for GenBank sequence management, primer design, and visualization, with optional write access for importing new records and saving design results.

Enables natural language control of anaerobic digestion modeling using the internationally recognized ADM1 standard. Supports wastewater treatment simulation for process design and optimization with AI-powered feedstock analysis, multi-reactor configurations, and professional report generation.

Exposes over 100 deterministic medical research engines to any MCP-capable agent, covering statistical tests, survival analysis, bioinformatics pipelines, primer/sequence design, modeling, and PubMed literature search. Every response is computed with real algorithms and tagged with an engine_status of real, unavailable, or error, so agents never receive fabricated numbers.

MCP server for biological protein design, folding, and affinity prediction using Refua tools, with optional support for ADMET, clinical simulation, preclinical planning, wet-lab automation, and more.

Chemical safety intelligence for AI-assisted experiment design. 18 MCP tools for compatibility checks, hazard analysis, PPE, storage, waste disposal, mixing order, exposure limits, transport classification, regulatory compliance (8 jurisdictions), and signed audit reports. Supports 4,200+ chemicals in 5 languages.

Model Context Protocol server for protein binder design using BindCraft via Docker, enabling structure prediction, sequence optimization, and scoring.

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.

Enables protein modeling and design using the Rosetta suite via Docker, including structure refinement, mutation stability analysis, docking, and loop modeling through natural language commands.

Enables AI-powered protein design, including binders, peptides, and custom proteins, with GPU-accelerated Docker deployment and async job management.