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.

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.

PyTDC (Therapeutics Data Commons) is an open-science platform providing comprehensive, AI-ready datasets and benchmarks for the entire drug discovery and development pipeline. It supports single-instance prediction (e.g., ADME, toxicity), multi-instance prediction (DTI, DDI, PPI), and novel molecule generation (MolGen). Use this skill for benchmarking machine learning models and predicting complex biomedical properties.

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.