Skill UI

DiffDock is a cutting-edge deep learning tool leveraging diffusion models to predict the 3D binding poses of small molecule ligands to protein targets. It supports structure-based drug discovery, enabling single-complex docking, batch virtual screening, and lead optimization. Users can input protein structures (PDB) or sequences, along with diverse ligand formats (SMILES, SDF, MOL2). The tool outputs predicted poses and confidence scores, making it crucial for identifying potential drug candidates, though it does not predict binding affinity.

Ginkgo Cloud Lab provides remote access to an advanced autonomous lab infrastructure, enabling users to submit and manage complex molecular biology protocols. It supports diverse applications including protein expression and purification (cell-free, E. coli, Pichia), quantitative assays (HiBiT, A280, LabChip), nucleic acid synthesis (IVT mRNA/circRNA), and characterization techniques (Thermal Shift, Echo-MS, SPR). The platform features an AI agent (EstiMate) for custom workflow feasibility and pricing.

Medchem is a powerful Python library designed for molecular filtering and prioritization in drug discovery. It allows users to apply rigorous, literature-derived drug-likeness rules (e.g., Lipinski, Veber, CNS), detect structural alerts (PAINS, NIBR), calculate complexity metrics, and combine multiple criteria using a custom query language. It is essential for compound library triage and hit-to-lead optimization.

Perform comprehensive molecular dynamics (MD) simulations using high-performance engines like OpenMM and analysis tools like MDAnalysis. This skill allows users to model the time evolution of biological systems, covering system preparation (solvation, force field assignment), energy minimization, NVT equilibration, and advanced analysis (RMSD, free energy surfaces). Ideal for structural biology, drug binding studies, and biophysics research.

Molfeat is a comprehensive Python library designed for molecular featurization. It converts chemical structures (SMILES/RDKit) into numerical feature vectors, supporting over 100 featurizers like ECFP, MACCS, and deep embeddings (ChemBERTa). It unifies featurization pipelines, making it ideal for QSAR modeling, virtual screening, and integrating into deep learning ML workflows.

This comprehensive skill provides a standardized pipeline for reconstructing the evolutionary history of biological sequences (genes, proteins, genomes). It guides users through essential steps, including multiple sequence alignment using MAFFT, maximum likelihood tree inference with IQ-TREE 2, and visualization with ETE3. Ideal for research in viral phylodynamics, molecular clock studies, and large-scale comparative genomics.

This skill provides programmatic access to the PrimeKG, a comprehensive knowledge graph integrating over 20 primary databases. It facilitates multiscale biological research by mapping complex relationships between genes, drugs, diseases, and phenotypes. Use it for drug repurposing, identifying novel disease targets, and analyzing molecular mechanisms across various biological scales.

RDKit is a powerful, open-source cheminformatics library providing extensive Python APIs for molecular analysis and structural manipulation. It supports molecular I/O (SMILES, SDF, InChI), calculating physicochemical descriptors (MW, LogP), generating fingerprints, performing substructure searching, and analyzing stereochemistry. This toolkit is essential for advanced research in drug discovery, computational chemistry, and materials science.

TorchDrug is a comprehensive PyTorch-based ML toolbox designed for molecular and protein science. It leverages Graph Neural Networks (GNNs) to tackle complex tasks including molecular property prediction, protein structure modeling, drug-target binding prediction, knowledge graph reasoning, and de novo molecular generation. It provides robust capabilities for accelerating drug discovery research.

Rowan is a cloud-native workflow platform accessed via Python API, designed for molecular simulation, medicinal chemistry, and structure-based drug design. It enables programmatic batch screening of properties (like pKa, descriptors), conformer generation, molecular docking, and protein cofolding without requiring local HPC/GPU infrastructure. Ideal for scalable, multi-step chemistry pipelines in drug discovery.