Discover Resources

PubMed MCP (cyanheads) is a third-party MCP server connecting AI clients to biomedical literature search, article metadata, available full text, MeSH vocabulary and citation tools.

Literature & Research · Drug Discovery

A third-party MCP server that connects assistant clients to Materials Project through mp_api, with tools for material searches, crystal structures, electronic properties, and other materials data.

Materials Discovery · Scientific Data

RCSB MCP (cnyambura) is a third-party MCP server that exposes RCSB PDB entry and polymer metadata, structure downloads, and custom Data API queries to an MCP client.

Drug Discovery · Scientific Data

UniProt MCP (cyanheads) is a third-party MCP server that connects AI clients to UniProt protein search, annotations, identifier mapping, proteomes, taxonomy, and amino-acid sequences.

Drug Discovery · Scientific Data

RDKit MCP Server (TandemAI) connects MCP-capable language models to RDKit tools, with an OpenAI-powered CLI client and an evaluation suite for tool outputs and agent responses.

Cheminformatics

Chemistry42 is a small-molecule discovery platform combining generative design, retrosynthesis, ADMET and selectivity prediction, and physics-based prioritization for hit identification and lead optimization.

Molecular Generation · Drug Discovery

Makya is Iktos’s generative AI SaaS platform for de novo molecular design, combining project objectives, synthesis constraints, and ligand- or structure-based modeling to guide compound selection.

Molecular Generation · Drug Discovery

molSimplify generates inorganic coordination and intermolecular complexes for computational screening, with bundled neural networks for selected properties of octahedral transition metal complexes.

Molecular Generation · Computational Chemistry

ChemML is a Python suite for chemical and materials data analysis, mining, and modeling, with a modular design and documented work on graph neural networks, AutoML, and explainability.

Molecular Property Prediction · Materials Discovery

DeepMol is a Python toolkit for molecular machine learning, connecting compound standardization and featurization with model training, evaluation, interpretation and reusable pipelines.

Molecular Property Prediction

AutoDock Vina is an open-source molecular docking and virtual screening program with Vina and AutoDock4.2 scoring, multi-ligand docking, macrocycle support and Python 3 bindings.

Drug Discovery

FEgrow supports interactive ligand-series construction for free-energy preparation, with documented active-learning examples and Dask-based acceleration for molecular design workflows.

Molecular Generation · Drug Discovery

MS2DeepScore predicts molecular structural similarity from pairs of tandem mass spectra using a Siamese neural network, with tools for spectral embeddings, inference and custom model training.

Spectroscopy

Spec2Vec is a Python package for MS/MS spectral similarity scoring, using Word2Vec embeddings learned from mass fragments and neutral losses to compare query and reference spectra.

Spectroscopy

Matchms is a Python toolkit for importing, cleaning and comparing tandem mass spectra, with configurable processing pipelines, multiple similarity measures and sparse score storage.

Spectroscopy

ChemDataExtractor v2 is a Python toolkit for extracting chemical entities, properties, spectra and tabulated data from scientific literature, with readers for HTML, XML and PDF.

Literature & Research

PySINDy is a Python package for inferring dynamical equations from measurement data using sparse system identification, with optional optimization methods for constrained and Bayesian regression.

Process Control · Digital Twins

SysIdentPy is a Python toolkit for identifying nonlinear dynamic systems with NARMAX-family models, combining structure selection, parameter estimation, simulation and forecasting.

Process Control · Predictive Maintenance

GEKKO is a Python modeling and optimization toolkit for differential algebraic systems, supporting process simulation, parameter estimation, mixed-integer optimization, and nonlinear predictive control.

Process Optimization · Process Control

do-mpc is a Python toolbox for nonlinear and robust model predictive control, moving horizon estimation, and simulation, with support for differential algebraic models and uncertainty.

Process Control · Digital Twins