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EMT-CADDA Fellowship Programme

EMT-CADDA Fellowship Programme

Computational and Structural Pathways in Modern Drug Discovery

Empowering Africa's Drug Discovery Future – From Molecule to Medicine

100+
Participants
12
Modules
8-12
Weeks

Hosted by: Aksumia Global Learning Solution (AGLS)

About EMT-CADDA

The Ermias Mergia Terefe Initiative for Computational Drug Discovery in Africa (EMT-CADDA) is a flagship training program dedicated to strengthening Africa's human resource capacity in computational drug discovery.

This transformative initiative targets health science students, computer science students, and early-career researchers, equipping them with the skills and knowledge to navigate the full spectrum of modern drug discovery.

Molecular Understanding

Participants will be guided through the process from understanding molecules at the atomic level to preparing candidate drugs for clinical trials.

Integrated Approach

Integrating both structural biology and computational approaches for comprehensive drug discovery training.

Curriculum Overview

Comprehensive 12-module curriculum covering the complete drug discovery pipeline

Introduction to structural biology and computational approaches in drug discovery.

  • Overview of Structural Biology: Definition, scope, historical milestones, interdisciplinary nature
  • Drug Discovery Pipeline: Target ID & validation, hit discovery, lead optimization, preclinical & clinical development
  • Molecular Targets in Drug Discovery: Druggability concept, protein targets, nucleic acid targets
  • Proteome, Genotype/Phenotype: Link between sequence, expression and phenotype
  • Importance of Computational Biology: Complementarity with wet-lab, success stories
  • Molecular Modeling Essentials: Definition, role in SBDD, advantages
  • Case Studies: Antimalarial virtual screening, GPCR ligand design, HIV integrase inhibitors, drug repurposing
  • Applications, Trends & Future Perspectives: SGDD, FBDD, AI, omics integration, personalized medicine

Comprehensive overview of computational tools used in structural biology and drug discovery.

  • Introduction to Computational Tools: Importance, integration with experimental methods, typical workflow
  • Structural & Chemical Databases: Protein databases, chemical databases, interaction databases, AI databases
  • Molecular Visualization & Analysis Tools: PyMOL, Chimera, VMD, Mol Viewer, Discovery Studio
  • Protein & Ligand Preparation Tools: Chimera/ChimeraX, Open Babel, Schrödinger tools
  • AI in Structural Biology: AlphaFold2, RoseTTAFold, complementing experimental structures
  • Molecular Docking & Scoring Tools: Docking engines, integrated suites, refinement methods, free energy calculations
  • Integrated Platforms & Web Servers: SwissSidechain, Pharmit, CB-Dock2, ZINC portal
  • Practical Hands-On Segment: Step-by-step workflow from protein download to docking visualization

In-depth study of protein structures and their significance in drug discovery.

  • Protein Structure Basics: Levels of organization, folding principles, structure-function relationship
  • Protein Classification: Enzymes, membrane proteins, structural proteins, transporters
  • Relevance to Drug Discovery: Why proteins dominate as drug targets, case examples, binding pockets
  • Structural Characterization & Validation: Experimental methods, computational predictions, hybrid approaches
  • Protein Structure Analysis: UniProt use, binding site analysis, druggable binding sites, assessment tools
  • Hands-On Practical Sessions: Protein data retrieval, visualization, interpreting density maps, AlphaFold predictions

Comprehensive coverage of genomics and nucleic acid targets in drug discovery.

  • Genomics in Drug Discovery: Studying the genome to identify therapeutic targets, pharmacogenomics, integration of bioinformatics and sequencing
  • Nucleic Acids as Therapeutic Targets: Role in disease, targeting DNA and RNA abnormalities
  • Drug Interaction Modes: DNA minor groove binders, intercalators, covalent DNA binders, RNA-targeting agents
  • Computational Approaches: Genomic data mining, nucleic acid structure acquisition, specialized docking protocols
  • Case Examples: Cisplatin-DNA crosslinking, siRNA therapeutics, minor groove binders
  • Selection Criteria: High-resolution structural data, disease relevance, predictable binding modes

In-depth study of molecular geometry and its applications in structural biology.

  • Introduction to Molecular Geometry: Importance for stability and drug design
  • Bond Types in Biomolecules: Covalent and non-covalent interactions
  • Bond Lengths and Chemical Basis: Typical bond lengths, hybridization effects
  • Bond Angles and Hybridization: sp³, sp², sp geometries, peptide bond angles
  • Torsion Angles in Proteins: φ, ψ, ω angles, Ramachandran plots
  • Planarity in Structural Biology: Peptide bond planarity, aromatic ring planarity
  • Measuring & Validating Geometry: Software tools for geometry measurement
  • Practical Session: Measuring bond parameters, interpreting Ramachandran plots

Comprehensive study of ligands and their role in drug discovery.

  • Introduction to Ligands: Definition, importance, and classification
  • Key Ligand Properties: Conformations, drug-likeness rules, ADMET basics
  • Ligand Libraries for Screening: Sources, types, and applications
  • Molecular Representation: File formats, visual representations, protonation states
  • Ligand-Based Drug Design: Pharmacophore modeling, similarity searching
  • Optimization & Filtering: Pose optimization, in silico ADMET filtering
  • Visualization & Analysis Tools: Docking pose inspection, interaction mapping
  • Ligand Libraries (Advanced): Design strategy, property filters, diversity maintenance
  • Practical Session: Ligand data retrieval, preparation, and format conversion

Advanced techniques in protein-ligand interaction analysis and molecular docking.

  • Binding Site Identification: Prediction, validation, and tools
  • Docking Principles: Rigid vs flexible docking, scoring functions
  • Molecular Docking Tools: Docking engines, visualization platforms
  • Protein Preparation: Retrieval, cleanup, protonation, energy minimization
  • Ligand Preparation: Geometry optimization, conformational analysis
  • Ligand–Protein Interaction Principles: Types of interactions, affinity determinants
  • Molecular Docking: Algorithms, types, pitfalls, and validation
  • Scoring Functions & Analysis: Binding energy evaluation, consensus scoring
  • Case Studies: Known complexes from PDB, real drug successes
  • Practical Hands-On: Running docking, analyzing poses, interaction diagrams
  • From Interaction to Optimization: Linking SAR to docking results

Virtual screening techniques and hit identification strategies.

  • Introduction to Virtual Screening: Concept, objectives, advantages
  • Virtual Screening Workflow: Preparation, receptor grid generation, screening
  • Docking Model Validation: Active vs decoy ligands, enrichment metrics
  • Structure-Based Virtual Screening: Using 3D structures, docking large libraries
  • Ligand-Based Virtual Screening: Similarity searching, pharmacophore models
  • Pharmacophore Modeling & QSAR: Hypothesis generation, AI/ML-assisted modeling
  • Library Design & Filtering: Focused vs diverse libraries, ADMET filters
  • Hit Ranking & Selection: Scoring function types, consensus scoring
  • Practical Hands-On: Performing VS, visualizing and ranking docking poses

QSAR modeling and its applications in drug discovery.

  • Introduction to QSAR: Definition, historical perspective, role in drug discovery
  • Types of QSAR: 2D-QSAR, 3D-QSAR, 4D/5D-QSAR
  • Descriptors & Feature Selection: Categories, tools, selection methods
  • Model Building Approaches: Classical statistics, ML-based approaches, deep learning
  • Model Validation & Applicability Domain: Internal and external validation techniques
  • Applications in Drug Discovery: Lead optimization, ADMET prediction, virtual screening
  • Schrödinger Technology Enabled: SAR Enabled, Structurally Enabled, Modelling Enabled
  • Practical Hands-On: Generating descriptors, building QSAR models, validation

Molecular dynamics simulations and trajectory analysis.

  • Computational Chemistry Fundamentals: Molecular mechanics, force fields, energy minimization
  • Introduction to MD: Principles, applications in drug discovery
  • Setting Up MD Simulations: Preparation, force field selection, solvation
  • Running MD Simulations: Time steps, ensembles, energy minimization
  • Trajectory Analysis: RMSD, RMSF, H-bond analysis, radius of gyration
  • Free Energy Calculations: MM-PBSA & MM-GBSA methods, binding energy estimation
  • Case Study & Practical Session: MD of top-ranked ligands, trajectory analysis
  • Reporting & Visualization: Preparing simulation reports, visualization tools

Lead optimization strategies and techniques.

  • Overview of Lead Optimization: Role in drug discovery, objectives, trade-offs
  • Structure–Activity Relationship Exploration: Systematic scaffold modifications
  • Multi-Parameter Optimization: Balancing efficacy, safety, and PK properties
  • In Silico Tools for Lead Optimization: Docking refinement, FEP, bioisosterism
  • Case Studies: Oncology kinase inhibitors, antiviral nucleoside analogs, CNS drugs
  • Practical Hands-On: Proposing optimization steps, re-docking optimized ligands

Translational pharmacology and clinical trial design principles.

  • Introduction to Translational Pharmacology: Definition, importance, challenges
  • Model-Informed Drug Development: Regulatory guidance, role of computational models
  • Predictive Modeling for Dose & Response: EC50, PK-PD models, PBPK simulations
  • Translating Preclinical Data to Human Trials: Allometric scaling, NOAEL & MABEL
  • Clinical Trial Design Principles: Phase I-IV trials, objectives and methodologies
  • Innovative & Adaptive Trial Designs: Adaptive methodologies, basket & umbrella trials
  • Case Studies & Regulatory Perspective: Model-based dose optimization, PBPK-informed dosing
  • Future Directions: AI-driven trial simulations, real-world evidence integration
  • Practical Hands-On: Translating preclinical data, designing trial protocols

Programme Learning Outcomes

By the end of this training, participants will be able to:

1-5
Identify potential molecular targets relevant to disease pathology using structural and computational tools.
Analyze protein, nucleic acid, and membrane structures to determine their suitability for drug design.
Apply molecular modeling and docking techniques to predict ligand–target interactions.
Conduct molecular dynamics simulations to assess the stability and conformational changes of biomolecular complexes.
Evaluate ADMET properties of lead compounds using predictive computational methods.
6-10
Optimize lead molecules through structure-guided design to improve potency, selectivity, and safety profiles.
Integrate preclinical computational data into translational pharmacology frameworks to support dose and response predictions.
Design model-informed clinical trial strategies to facilitate safe and efficient drug candidate progression to human testing.
Collaborate effectively in multidisciplinary teams, communicating findings to scientific, regulatory, and industry audiences.
Utilize global networking and learning platforms to access up-to-date drug discovery resources and tools.

Teaching & Learning Methods

  • Lectures – to deliver foundational concepts and theoretical knowledge.
  • Hands-on Computer Lab Sessions – for molecular modeling, docking, and simulations.
  • Workshops – focused on structural biology tools, data analysis, and trial design.
  • Case-Based Learning (CBL) – real-world examples of drug discovery projects.
  • Seminars & Expert Talks – by international facilitators sharing current trends and research.
  • Group Discussions – for collaborative problem solving and peer learning.
  • Guided Literature Reviews – critical analysis of scientific publications.
  • Problem-Based Learning (PBL) – tackling open-ended computational drug design challenges.
  • Online Learning Modules – through the AGLS Learning Management System.
  • Role-Play & Simulation – for team-based translational pharmacology and clinical trial scenarios.

Assessment Methods

  • Quizzes & MCQ Tests – to evaluate understanding of key concepts.
  • Assignments – including literature reviews, molecular docking reports, and analysis tasks.
  • Practical Project Work – complete computational modeling and drug discovery pipeline tasks.
  • Simulation Output Reports – assessment of molecular dynamics and ADMET predictions.
  • Oral Presentations – explaining research findings, project outcomes, or trial designs.
  • Peer Assessment – evaluating teamwork and collaborative contributions.
  • Case Study Reports – linking computational results to real-world therapeutic contexts.
  • Lead Optimization Proposal – improving potency, selectivity, and safety of a compound.
  • Clinical Trial Protocol Draft – integrating computational results into a real trial framework.
  • LMS Activity Tracking – completion of online modules, participation in discussion forums.

Participants & Selection

Target Participants
  • Health Science Students – pharmacy, medicine, nursing, biomedical sciences, laboratory sciences, public health, etc.
  • Computer Science, Data Science, and Engineering Students – focusing on bioinformatics, computational biology, AI in health.
  • Early Career Researchers – in biomedical, pharmaceutical, clinical, and computational fields.
  • Industry & Regulatory Professionals – from pharma companies, research institutes, health authorities.
Selection Process
  • Competitive online application including a motivation letter, CV, and optional recommendation letter.
  • Selection committee ensures disciplinary, gender, and regional diversity.
  • Commitment requirements include willingness to cooperate in learning and group activities.
  • Openness to co-publish research or training outputs.
  • Willingness to disseminate findings to the community.
  • Agreement to recognize the organizers on possible platforms where outcomes are presented or shared.

Trainers

Trainers will be pooled from across the world, bringing diverse expertise, knowledge, and skills in computational and structural biology, proteomics, genomics, bioinformatics, medicinal chemistry, and drug discovery. This multidisciplinary team will provide participants with both theoretical foundations and practical, hands-on guidance, ensuring a globally informed and high-quality learning experience.

A call for volunteer trainers will be made through social media platforms, WhatsApp network groups, and LinkedIn to identify and engage the most suitable experts for each subtopic, ensuring participants benefit from the best possible mentorship in every area of the training.

Mission Statement

EMT-CADDA aims to cultivate a new cadre of African scientists with the capacity to lead innovative drug discovery projects both locally and internationally. By bridging the molecule-to-medicine continuum, this initiative aspires to accelerate the translation of African research into impactful, locally relevant health solutions, thereby empowering the continent's drug discovery future.

Community Service Initiative

This training is part of the Dr. Ermias Mergia Terefe's Computational Drug Discovery and Structural Biology Initiative, a community service project dedicated to building capacity in cutting-edge drug discovery approaches in Africa.

The program aligns with the broader goal of empowering African health science, computational science, and early-career research communities to contribute to global health solutions. Through high-quality, accessible training, the initiative seeks to:

  • Strengthen human resource capacity in computational and structural biology.
  • Promote cross-country collaborations and multi-disciplinary problem solving.
  • Encourage open science, co-publications, and community-focused dissemination of findings.

Course Information

Duration: 8-12 weeks (Professional Track)
Cohort Size: 100 participants quarterly
Format: Blended (Online + Face-to-face)
Cost: Free (Professional Track: $50-$500)
Certificate: Professional Certificate of Competency

Contact Information

info@akgls.org
+254 794 934 965
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