From Data to Drug : A Practical Journey in Computer Aided Drug Design & Optimization
Accelerate Therapeutic Innovation with AI-Driven Molecular Modeling Master the Digital Pipeline from Target Identification to Lead Optimization
Course Description
In an era where bringing a single drug to market costs billions, Computer-Aided Drug Design (CADD) has become the industry’s secret weapon for efficiency. This course offers a hands-on, practical journey through the modern drug discovery pipeline, bridging the gap between raw biological data and clinical-ready drug candidates. You will dive deep into Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD), utilizing industry-standard software to simulate real-world pharmaceutical R&D. We go beyond traditional methods by integrating Artificial Intelligence (AI) and Machine Learning (ML) to predict bioactivity, optimize ADMET properties, and conduct ultra-large-scale virtual screenings. By the end of this program, you will be equipped to transform vast chemical datasets into optimized lead compounds, significantly reducing the time and cost of laboratory experimentation.
What You'll Learn
Target Identification: How to select and validate biological targets using genomic and proteomic databases.
Molecular Modeling: Mastering 3D protein structure prediction and energy minimization.
Docking Simulations: Executing protein-ligand interactions to predict binding affinity.
AI/ML in Discovery: Training models for Bioactivity Prediction and De Novo Drug Design.
In Silico ADMET: Using AI to predict the safety, toxicity, and metabolic profiles of candidates.
Advanced Dynamics: Analyzing molecular movement using Molecular Dynamics (MD) Simulations.
Curriculum
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Module 1: Target Identification & Structural Bioinformatics Foundations
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Overview of the computational drug discovery pipeline: hit identification to lead optimization.
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Accessing structural databases: retrieving targeted data records from NCBI, UniProt, PubChem, and the Protein Data Bank (PDB).
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Sequence manipulation for target validation: establishing alignments and structural homology using BLAST and MEGA.
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Setting up computational operating environments: working with Linux commands and baseline cloud tools for high-throughput scaling.
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Module 2: Molecular Modeling, Visualization & High-Throughput Docking
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Chemical informatics basics: drawing and generating 2D/3D chemical structures with ChemSketch and ChemDraw.
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Advanced macromolecular visualization: managing protein and ligand coordinates using PyMOL and UCSF Chimera.
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Target site exploration: predicting active pocket grids, binding cavities, and interaction points.
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High-throughput molecular docking: executing grid configurations and structural binding assays via AutoDock Vina and PyRx platforms.
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Module 3: AI-Driven Cheminformatics, QSAR Modeling & Lead Optimization
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Introduction to Machine Learning in drug design: processing molecular features using Pandas, NumPy, and Scikit-learn.
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Quantitative Structure-Activity Relationship (QSAR) workflows: calculating fingerprints and molecular descriptors with RDKit.
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Regression and classification modeling: training algorithms to predict drug-target binding constants and therapeutic behaviors.
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Pharmacokinetic profiling: applying automated in silico filters for Lipinski’s Rule of 5 and ADMET toxicity profiling.
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Introduction to Molecular Dynamics (MD) tracking: analyzing structural trajectory baselines (RMSD, hydrogen bonding networks).
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