A three-tier QSAR validation framework: internal statistics, cross-validation, and external checks, with the thresholds used for papers and regulatory submissions.
A concise overview of how molecules and chemical knowledge are represented for machines and humans alike, from SMILES strings to LLM-based agents.
Your docking pose is only as trustworthy as your starting coordinates. Here is a systematic guide to navigating the PDB, avoiding common pitfalls, and future-proofing your workflow for the coming mmCIF era.
A comprehensive walkthrough of cheminformatics, machine learning, molecular docking, ADMET prediction, and molecular dynamics simulations as the modern toolbox for computer-aided drug discovery.
A beginner-friendly walkthrough of PyTorch Geometric's point cloud tutorial - covering the Data object, transforms, dynamic graph construction, PointNet++ message passing, and graph-level classification.
What agentic coding changes for computational drug discovery: reliability, the shift of the bottleneck onto validation, and where domain judgment still decides the science.
A practical, beginner-friendly introduction to the Deep Graph Library (DGL) and how to use it to featurize protein–ligand complexes for machine learning in drug discovery.
A practical guide to three advanced 3D fingerprinting methods (PLEC, SPLIF, and E3FP) and how to choose between them when featurizing docking poses for ML-based drug discovery models.
Conceptual overview of the key energetic contributions governing protein–ligand binding in molecular docking, including desolvation, entropy, water displacement, electrostatics, and scoring function behavior.
Practical choices that change MD results before the simulation starts: solvent box shape, protein termini, and how to pick a representative conformation.
