From Data to Drug : A Practical Journey in Computer Aided Drug Design & Optimization
Accelerate small molecule discovery workflows using state-of-the-art Computer-Aided Drug Design (CADD) platforms. Master industry-standard computational pipelines and AI-driven hit-to-lead optimization strategies for precision medicine.
Course Description
In the modern pharmaceutical landscape, transitioning from raw biological data to a viable therapeutic molecule requires advanced structural computational techniques. This practical, career-oriented webinar presented by Dr. Omics Labs bridges the gap between traditional pharmacology and digital health innovation. Participants will embark on a comprehensive journey exploring Computer-Aided Drug Design (CADD) workflows used to streamline target identification and lead compound optimization. The training session focuses heavily on removing bottleneck constraints during virtual screening, molecular docking, and pharmacophore modeling experiments. By integrating AI models and machine learning algorithms, learners will understand how to accurately predict ADMET properties and binding affinities. This computational biology approach drastically reduces the time and astronomical costs associated with early-stage wet-lab chemistry discovery. Ultimately, this masterclass provides actionable insights for life science professionals looking to excel in the lucrative data-driven drug discovery market.
What You'll Learn
The end-to-end framework of modern Computer-Aided Drug Design (CADD) and structural optimization pipelines.
How to search, extract, and clean target protein structures from global public repositories like the PDB.
Practical strategies for executing high-throughput virtual screening of expansive chemical libraries.
The application of machine learning algorithms to predict absorption, distribution, metabolism, excretion, and toxicity profiles.
Techniques for interpreting binding energies, molecular dynamics trajectories, and ligand-receptor interactions.
Curriculum
-
Foundations of rational drug design, target identification, and modern chemoinformatics databases.
Lesson -
Preparing target biomolecules and small-molecule ligand libraries for computational processing.
Lesson -
Principles of structural molecular docking, scoring functions, and interaction analysis.
Lesson -
Lead optimization strategies and AI-driven pharmacophore modeling for structural enhancement.
Lesson -
Evaluating predictive ADMET properties and running cloud-based toxicology validation simulations.
Lesson
