Biotechnology and Pharmaceutical Development

Structural Bioinformatics and Molecular Modeling Training Course

Introduction

Structural Bioinformatics and Molecular Modeling is a rapidly advancing field that leverages computational methods to analyze and predict the structures of biological macromolecules. By integrating molecular biology with computational science, this training course aims to empower learners with the knowledge and skills necessary to model biological molecules, understand protein folding, and predict the interaction of drugs with their targets. In the age of personalized medicine and drug discovery, the ability to manipulate and simulate biological structures is invaluable. Structural Bioinformatics and Molecular Modeling Training Course will provide a comprehensive foundation, from basic principles to advanced techniques, in structural bioinformatics, including molecular dynamics simulations, protein-ligand docking, and visualization techniques.

With a strong focus on real-world applications, this course delves into the tools and technologies that are shaping modern biomedical research. Participants will gain hands-on experience with the latest software packages and computational tools used in the study of molecular structures, drug design, and genetic engineering. By the end of the course, learners will be equipped with the expertise needed to contribute to cutting-edge research and development in fields such as biotechnology, pharmaceutical sciences, and academic research.

Programme Curriculum

Structural Bioinformatics and Molecular Modeling Training Course

Introduction

Course Duration

10 days

Course Objectives

Understand the basics of structural bioinformatics and its applications in drug discovery and protein engineering.
Learn advanced molecular modeling techniques, including molecular dynamics simulations and quantum chemistry methods.
Gain hands-on experience with protein-ligand docking and protein-protein interaction prediction.
Understand macromolecular structure prediction and apply various software tools for structure-based drug design.
Master key concepts in biomolecular visualization and the use of software like PyMOL and Chimera.
Develop a solid understanding of bioinformatics databases, such as PDB, and their role in computational biology.
Study the thermodynamics of protein-ligand binding and its impact on drug development.
Acquire skills in structure-based virtual screening to identify potential drug candidates.
Analyze protein folding and stability through computational techniques.
Understand target identification in drug discovery and the role of bioinformatics in precision medicine.
Explore machine learning techniques for molecular modeling and structural predictions.
Learn the integration of computational biology with experimental methods in drug development pipelines.
Study real-world case studies on the optimization of molecular interactions and the discovery of novel therapeutics.

Target Audience

Bioinformatics professionals.
Biotechnology and pharmaceutical companies looking to improve drug design.
Academic researchers in molecular biology and computational biology.
Medical scientists.
Computational biologists.
PhD students and postdocs in structural bioinformatics or molecular biology.
Data scientists.
Healthcare professionals.

Course Modules

Module 1: Introduction to Structural Bioinformatics

Overview of the field and its importance in drug discovery
Key terminology and concepts in molecular modeling
Introduction to protein structures: Primary, Secondary, Tertiary, and Quaternary
Overview of structural databases: PDB, UniProt, and others
Case Study: Predicting protein structures from sequences

Module 2: Protein Structure Prediction

Methods for predicting 3D protein structures: Homology modeling, ab initio modeling
Computational tools for structure prediction (MODELLER, I-TASSER)
Structural alignment techniques for homologous proteins
Handling structural data and errors in predictions
Case Study: Structural prediction of a novel enzyme

Module 3: Molecular Dynamics Simulations

Introduction to molecular dynamics (MD) simulations
Setting up and running MD simulations (GROMACS, AMBER)
Analyzing MD trajectories: RMSD, RMSF, and solvent accessibility
Applications of MD in protein folding and drug design
Case Study: MD simulation of a protein-ligand complex

Module 4: Molecular Docking and Virtual Screening

Basics of protein-ligand docking
Docking software: AutoDock, DOCK, Glide
Virtual screening to identify lead compounds
Scoring functions and binding affinity predictions
Case Study: Virtual screening for drug candidates against COVID-19 targets

Module 5: Protein-Ligand Interactions

Key principles of protein-ligand interactions
Understanding binding sites and hot spots
Computational techniques for optimizing ligand binding
Role of solvent in protein-ligand interactions
Case Study: Designing inhibitors for cancer-related proteins

Module 6: Molecular Visualization Techniques

Introduction to visualization tools: PyMOL, Chimera, VMD
Visualizing protein structures and molecular interactions
Structural representation techniques: ribbons, surface, space-filling
Advanced features in visualization tools: animations, ligand interactions
Case Study: Visualizing the binding of a drug molecule to a receptor

Module 7: Bioinformatics Databases

Introduction to bioinformatics databases and resources
PDB: Structure repository and search techniques
UniProt: Protein sequence and functional analysis
Sequence alignment tools: BLAST, ClustalW
Case Study: Searching for homologous proteins in PDB

Module 8: Advanced Molecular Dynamics

Techniques for enhanced sampling in MD simulations
Free energy calculations and binding affinity predictions
Analysis of protein stability and folding pathways
Coupled simulations with experimental data
Case Study: Predicting the effect of mutations on protein stability

Module 9: Integrating Bioinformatics with Experimental Data

Combining experimental data with computational models
Homology-based modeling using experimental structures
Using NMR, X-ray crystallography, and cryo-EM data
Validation of computational models with experimental results
Case Study: Combining cryo-EM and computational methods to refine protein structures

Module 10: Machine Learning in Molecular Modeling

Overview of machine learning applications in bioinformatics
Training machine learning models for molecular property predictions
Feature selection and data preprocessing techniques
Neural networks and deep learning in molecular modeling
Case Study: Predicting protein-ligand binding affinity using machine learning

Module 11: Drug Discovery and Target Identification

Identifying potential drug targets using computational methods
Structure-based drug discovery and screening
Designing small molecules for target modulation
ADMET predictions in drug development
Case Study: Identifying a target for Alzheimer's treatment

Module 12: Computational Approaches for Enzyme Engineering

Methods for engineering enzymes for industrial applications
Modifying enzyme active sites using computational techniques
Stability and activity prediction of engineered enzymes
Molecular dynamics simulations in enzyme design
Case Study: Engineering a thermostable enzyme for biofuel production

Module 13: Biomolecular Simulation for Disease Modeling

Simulating disease-related proteins and mutations
Predicting drug resistance in viral proteins
Structural insights into neurodegenerative diseases
Modeling protein misfolding and aggregation
Case Study: Simulation of protein aggregation in Alzheimer's disease

Module 14: Advanced Protein-Protein Interactions

Predicting protein-protein interactions (PPI)
Tools and methods for PPI prediction: ZDOCK, ClusPro
Role of PPIs in signaling pathways and disease mechanisms
Structural and functional analysis of PPIs
Case Study: Understanding the PPI network in cancer

Module 15: Applications in Precision Medicine

Using structural bioinformatics for personalized medicine
Modeling patient-specific mutations and drug responses
In silico prediction of drug efficacy and toxicity
Designing patient-tailored therapeutic approaches
Case Study: Modeling drug interactions for genetic diseases

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Molecular modeling software bioinformatics tools structure prediction

Programme Fee

Duration: 10 Days

Method: Online or Physical

Online Price: $2,000

Physical Price: $4,000

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 30 Oct 2026

💻 Online / Virtual session

Online $2,000