A step-by-step tutorial on using PaDELPy to calculate molecular fingerprints and build a Random Forest model for predicting molecular activity in drug discovery.
A beginner-friendly guide to how molecular dynamics (MD) simulations work, their applications in structural bioinformatics and materials science, and the insights they provide into complex molecular systems.
Guidelines for choosing appropriate MD simulation lengths in protein–ligand systems, with key convergence metrics like RMSD, RMSF, and interaction fingerprints.
Overview of supervised and unsupervised learning approaches, highlighting key techniques, applications, and differences in the context of cheminformatics and drug discovery.
Overview of freely available chemical and bioactivity databases—including ChEMBL, BindingDB, PubChem, PDBbind, and BRENDA—that fuel machine learning and cheminformatics research in drug discovery.
Step-by-step tutorial on retrieving, curating, and classifying ChEMBL bioactivity data for machine learning applications in drug discovery.
