Dimethyltensor

Perform protein-ligand docking and virtual screening for drug design.

Use when working with RDKit for cheminformatics in Python. Covers molecular I/O, property calculation, Lipinski filters, fingerprints, similarity, 3D conformer generation, reactions, fragmentation, substructure search, MCS, stereochemistry, and tautomers.

Analyze molecular dynamics trajectories with MDAnalysis for various structural insights.

Facilitates pharmacophore modeling for drug discovery, covering various feature types and workflows.

Perform Matched Molecular Pair Analysis (MMPA) for SAR extraction and bioisostere discovery.

Master cheminformatics with Daylight theory, covering SMILES, SMARTS, SMIRKS, and molecular fingerprints.

Use when working with TorchDrug for graph-based drug discovery and molecular ML. Covers molecular property prediction, protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, and GNN architectures on chemical data.

Facilitate quantum chemistry and DFT calculations with a comprehensive Python-based toolkit.

Convert molecular file formats and generate 3D coordinates with OpenBabel's pybel API and obabel CLI.

Analyze and predict drug-target binding kinetics with advanced modeling techniques.

Utilize molecular mechanics force fields for MD simulations with OpenMM and OpenFF.

Utilize ASE for atomistic simulations including structure building and molecular dynamics.

Nextflow enables writing, debugging, and optimizing scalable computational pipelines for bioinformatics and HPC workflows.

Framework for in silico peptide screening and evolutionary optimization.

Compute free energy differences for drug discovery with high accuracy.

Facilitate active learning and closed-loop molecular optimization for drug discovery.

Create interactive 3D molecular visualizations in Jupyter notebooks using py3Dmol.

Last-resort skill for retrieving peer-reviewed literature when solutions are unclear or hallucination risk is high.

Graph-based toolkit for reaction informatics, enabling advanced chemical analysis and synthesis planning.

Facilitate fragment-based drug design with tools for library creation and ligand efficiency analysis.

Build 3D protein structures from sequences using homology modeling techniques.

A flexible framework for structuring computational chemistry tasks and generating hypotheses.

Use when working with DeepChem for molecular machine learning, drug discovery, quantum chemistry, materials science, or bioinformatics. Handles molecular datasets, featurization strategies, model training/evaluation, and predictions on chemical data.

Design and evaluate generative models for de novo drug and molecule design.

Run coarse-grained molecular dynamics simulations with MARTINI 3 for efficient modeling of complex systems.

Calibrate uncertainty estimates in QSAR/ML models for reliable predictions.

Utilize the EASE framework for analyzing polar organic reaction mechanisms and synthesis problems.