![Computer-Aided Drug Design
(CADD)
Revolutionizing Drug Discovery with
Computational Tools
Presented by: [Prikshit pundir]](https://image.slidesharecdn.com/caddpresentation-250325093147-2ef8c61b/85/CADD_Presentation-pptx-by-Prikshit-pundir-1-320.jpg)
CADD_Presentation.pptx by - Prikshit pundir
- 1. Computer-Aided Drug Design (CADD) Revolutionizing Drug Discovery with Computational Tools Presented by: [Prikshit pundir]
- 2. Introduction to CADD • Definition: Computational approach for drug discovery. • - Reduces trial-and-error. • - Predicts drug interactions. • - Enhances efficacy & safety. • - Reduces cost & time.
- 3. Drug design Drug design, also known as rational drug design, is the process of identifying and developing new medications based on the knowledge of a biological target. It involves designing molecules that interact with specific biological structures, such as enzymes or receptors, to produce a therapeutic effect.
- 4. Key Aspects of Drug Design Target Identification: Finding a biological molecule (e.g., protein, enzyme, receptor) associated with a disease. Lead Compound Discovery: Identifying small molecules or biologics that can interact with the target. Optimization: Modifying lead compounds to improve their efficacy, selectivity, and pharmacokinetics. Preclinical Testing: Evaluating the drug candidate’s safety and effectiveness in lab studies. Clinical Trials: Testing the drug in human subjects for approval.
- 5. Computer-Aided Drug Design (CADD) • Computer-Aided Drug Design (CADD) refers to computational techniques used to discover, develop, and analyze drugs. It helps streamline the drug discovery process by predicting interactions between drug candidates and biological targets, thereby reducing the time and cost of research.
- 6. Why is CADD Important? • Reduces the time and cost of drug discovery. • Enhances the accuracy of drug- target interactions. • Minimizes the need for extensive laboratory testing. • Helps in predicting drug efficacy and potential side effects.
- 7. History and Evolution 1960s QSAR introduced. 1970s Computational chemistry advances. 1980s- 90s Docking & cheminformatics. 2000s- Present AI, cloud computing, big data.
- 8. Types of CADD Structure-Based Drug Design (SBDD): Uses 3D protein structures. Ligand-Based Drug Design (LBDD): Uses known active compounds. Hybrid Approaches: Combines both for accuracy.
- 9. Drug Discovery Process • 1. Target Identification • 2. Lead Discovery • 3. Lead Optimization • 4. Preclinical Testing • 5. Clinical Trials • CADD optimizes each stage.
- 10. Structure- Based Drug Design • Structure-Based Drug Design (SBDD) is a computational technique that utilizes the 3D structure of a target protein to design and optimize drug candidates. It is primarily used when the molecular structure of the biological target is known, typically through techniques such as X-ray crystallography, NMR spectroscopy, or cryo-electron microscopy.
- 11. Steps in Structure-Based Drug Design (SBDD) 1. Target Identification and Validation 1. The first step in SBDD is selecting a biological target, usually a protein or enzyme involved in a disease pathway. 2. The target must be validated to ensure its role in the disease and its ability to bind with small molecules (druggability). 2. Structure Determination of the Target 1. The 3D structure of the protein is obtained using experimental methods like X-ray crystallography, NMR spectroscopy, or cryo- electron microscopy. 2. Computational tools such as homology modeling can be used when experimental data is unavailable.
- 12. •Identification of the Active Site •The binding site (active site or allosteric site) of the protein is identified. •Techniques like blind docking and pocket detection algorithms (e.g., AutoDock, Fpocket, or SiteMap) help locate potential binding sites. •Molecular Docking •In this process, a library of small molecules (ligands) is computationally screened to predict their binding affinity with the target protein. •Molecular docking tools (e.g., AutoDock, Glide, GOLD, MOE) predict how a ligand fits into the binding site, its orientation, and binding energy.
- 13. •Scoring and Ranking of Ligands •Ligands are ranked based on scoring functions that predict their binding strength. •The scoring functions consider factors such as hydrogen bonding, hydrophobic interactions, and electrostatic forces. •Molecular Dynamics (MD) Simulations •The stability and flexibility of the ligand-protein complex are tested under physiological conditions using Molecular Dynamics (MD) simulations (e.g., GROMACS, AMBER, CHARMM). •This step helps refine the predicted binding poses and evaluate dynamic interactions over time. •Lead Optimization •The best-performing molecules are modified to improve their binding affinity, pharmacokinetic properties (ADMET: Absorption, Distribution, Metabolism, Excretion, and Toxicity), and drug-like properties. •Computational tools such as Lipinski’s Rule of Five, ADMET prediction software, and free energy perturbation methods help in refining lead compounds.
- 14. Ligand-Based Drug Design • Ligand-Based Drug Design (LBDD) is a computational drug discovery approach that relies on the structural and chemical properties of known bioactive molecules (ligands) to design and optimize new drug candidates. Unlike Structure-Based Drug Design (SBDD), which requires knowledge of the 3D structure of the biological target, LBDD focuses on analyzing previously identified ligands that exhibit desired biological activity.
- 15. Principle of Ligand-Based Drug Design The fundamental assumption in LBDD is that molecules with similar chemical structures tend to exhibit similar biological activities. By analyzing the chemical and structural features of known active compounds, researchers can predict and design new drug molecules with improved potency, selectivity, and pharmacokinetic properties.
- 16. Key Techniques in Ligand-Based Drug Design 1. Quantitative Structure-Activity Relationship (QSAR) QSAR is a computational technique that establishes a mathematical relationship between the chemical structure of a compound and its biological activity. This technique helps in predicting the activity of new compounds without the need for extensive laboratory testing. Steps in QSAR Modeling: 1. Data Collection: 1. A dataset of known active and inactive compounds is gathered, along with their biological activity values (e.g., IC₅₀, EC₅₀, Ki). 2. Feature Extraction: 1. Chemical descriptors such as physicochemical properties (logP, molecular weight, hydrogen bond donors/acceptors), topological indices, and electronic properties are computed. 3. Model Building: 1. Machine learning and statistical techniques (e.g., Multiple Linear Regression (MLR), Partial Least Squares (PLS), Support Vector Machines (SVM), Random Forest, Neural Networks) are used to develop predictive models.
- 17. 1. Model Validation: 1. The developed QSAR model is validated using statistical methods like R² (coefficient of determination), Q² (cross-validation score), and external validation tests to ensure reliability. 2. Prediction and Optimization: 1. The model is used to predict the biological activity of novel compounds, which are then optimized for improved efficacy. • 📌 Example: A QSAR study on retinoids could analyze structural features such as conjugated double bonds, hydroxyl groups, and hydrophobicity to predict their effects on bone density or sexual health—areas relevant to your research interests!
- 18. Computational Tools for Drug Designing • Computational drug design involves various software tools that assist in molecular modeling, virtual screening, docking, and pharmacokinetic predictions. These tools fall into different categories based on their functionality in the Computer-Aided Drug Design (CADD) process.
- 19. •1. Molecular Docking Tools •These tools predict how small molecules (ligands) bind to a target protein and calculate binding affinity. •Popular Docking Tools: •📌 Example: AutoDock can be used to dock retinoid compounds to RAR (Retinoic Acid Receptor) to study their binding affinity for osteoporosis treatment. Tool Features AutoDock Free, widely used, flexible docking, developed by Scripps Research. AutoDock Vina Faster version of AutoDock with improved accuracy. Molecular Operating Environment (MOE) Commercial tool, integrates docking, QSAR, and pharmacophore modeling. Glide (Schrödinger) High-precision docking, used in industry for lead optimization. Gold (CCDC) Genetic algorithm-based docking with high accuracy. SwissDock Web-based docking tool using AutoDock. DOCK One of the first docking programs, used for flexible ligand docking.
- 20. •2. Molecular Dynamics (MD) Simulation Tools •These tools simulate the movement and stability of drug-protein complexes over time. •Popular MD Simulation Tools: •📌 Example: GROMACS can be used to simulate retinoid binding stability with RAR and RXR receptors in different solvent conditions. Tool Features GROMACS Free, high-speed molecular dynamics for biomolecules. AMBER Commercial, widely used in pharmaceutical research. CHARMM Provides force fields for protein-ligand simulations. Desmond (Schrödinger) Fast MD simulations with accurate solvation models. LAMMPS Versatile tool for large- scale molecular simulations.
- 21. •. Pharmacophore Modeling Tools •These tools identify essential chemical features required for drug activity and aid in virtual screening. •Popular Pharmacophore Modeling Tools: •📌 Example: LigandScout can identify hydrophobic regions, hydrogen bond donors/acceptors in retinoids, helping in drug design for bone health and reproductive health. Tool Features LigandScout Generates and validates pharmacophore models. Discovery Studio (BIOVIA) Industry-standard tool for pharmacophore- based screening. Phase (Schrödinger) Advanced pharmacophore modeling and 3D QSAR analysis. HipHop & HypoGen (Catalyst) Identifies common features among active ligands.
- 22. •QSAR (Quantitative Structure-Activity Relationship) Tools •QSAR models predict drug activity based on chemical structure using machine learning. •Popular QSAR Tools: •📌 Example: VLife QSARpro can be used to predict retinoid derivatives’ potency for RAR activation. Tool Features OEGA (OpenEye) Machine learning-based QSAR modeling. AlvaDesc Extracts molecular descriptors for QSAR analysis. VLife QSARpro Used for 2D and 3D QSAR modeling. ChemProp AI-driven QSAR model for toxicity prediction. MOBY Generates descriptors and models using machine learning.
- 23. •Virtual Screening Tools •These tools screen large compound libraries to identify potential drug candidates. •Popular Virtual Screening Tools: •📌 Example: ZINC Database can be used to screen natural retinoid-like compounds for RAR binding. Tool Features ZINC Database Free compound library for virtual screening. PubChem Open-access chemical database with biological activity data. Schrödinger Glide Docking-based virtual screening tool. SwissADME Predicts drug-like properties of molecules.
- 24. •ADMET Prediction Tools •These tools predict Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties of drug candidates. •Popular ADMET Tools: •📌 Example: SwissADME can predict whether a retinoid analog follows Lipinski’s Rule of Five for oral bioavailability. Tool Features SwissADME Free, predicts drug-likeness and bioavailability. ADMET Predictor (Simulations Plus) Advanced commercial tool for ADME profiling. pkCSM Predicts pharmacokinetic properties using graph- based modeling. PreADMET Web-based tool for predicting ADMET properties. TOPKAT (BIOVIA) Toxicity prediction tool used in the pharmaceutical industry.
- 25. •ADMET Prediction Tools •These tools predict Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties of drug candidates. •Popular ADMET Tools: •📌 Example: SwissADME can predict whether a retinoid analog follows Lipinski’s Rule of Five for oral bioavailability. Tool Features SwissADME Free, predicts drug-likeness and bioavailability. ADMET Predictor (Simulations Plus) Advanced commercial tool for ADME profiling. pkCSM Predicts pharmacokinetic properties using graph-based modeling. PreADMET Web-based tool for predicting ADMET properties. TOPKAT (BIOVIA) Toxicity prediction tool used in the pharmaceutical industry.
- 26. •7. De Novo Drug Design Tools •These AI-driven tools generate novel drug-like molecules from scratch. •Popular De Novo Design Tools: •📌 Example: DeepChem can generate new retinoid-like compounds with improved safety and efficacy. Tool Features DeepChem AI-based drug discovery framework. ChemGAN Challenge Uses generative adversarial networks (GANs) for drug design. MOSES (Molecular Sets) Benchmarks AI- generated molecules. REINVENT (Insilico Medicine) Reinforcement learning- based molecule generation. SYNTHIA (Merck) AI-assisted retrosynthesis tool.
- 27. • Thank you