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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