Drug Discovery Masterclass: Molecular Docking from Scratch- recorded courses
Master computer-aided drug design (CADD) pipelines to simulate and analyze complex ligand-protein interactions. Deploy computational modeling workflows from the ground up to identify high-affinity therapeutic compounds.
Course Description
Welcome to an advanced self-paced masterclass designed to launch your career in computational pharmacology and structural bioinformatics. Modern pharmaceutical innovation relies heavily on computational simulation to accelerate preclinical development timelines and minimize wet-lab trial costs. This comprehensive recorded course equips you with the foundational skills to execute modern computer-aided drug design protocols from scratch. Throughout this training, you will learn to retrieve, clean, and prepare raw protein target structures and small-molecule chemical libraries. You will explore the biophysical principles behind molecular docking algorithms, scoring functions, and active site identification. Furthermore, the curriculum focuses on evaluating molecular binding affinities, predicting drug-likeness parameters, and assessing toxicological profiles online. By blending structural biology principles with modern AI-driven virtual screening methods, you will understand how machine learning models optimize hit-to-lead selection. Step directly into the world of rational drug design, gain a competitive edge in translational research, and revolutionize your approach to biomedical discovery.
What You'll Learn
Navigate structural biology databases to retrieve and prepare 3D macro-molecular target receptors.
Curate, optimize, and generate structural conformations for small-molecule ligand libraries.
Execute target-blind and site-specific molecular docking protocols using industry-standard open-source software.
Interpret thermodynamic scoring functions, binding energies, and non-covalent intermolecular bonds.
Curriculum
-
Module 1: Principles of Structural Biology, Protein Data Bank (PDB) Curation, and Target Preparation.
Lesson -
Module 2: Small-Molecule Chemistry, 3D Geometry Optimization, and Ligand Library Formatting.
Lesson -
Module 3: Active Site Identification, Grid Box Configuration, and Molecular Docking Execution.
Lesson -
Module 4: Evaluating Thermodynamic Scoring Functions, Binding Energies, and 2D/3D Interaction Visualizations.
Lesson -
Module 5: Virtual Screening Implementations and ADMET Profiling Foundations for AI-Driven Lead Prioritization.
Lesson
