7.2 Ligand-based drug design

Ligand-based drug design uses known active compounds to find new drug candidates. It analyzes chemical features and structures to guide the search. This approach includes pharmacophore modeling, quantitative structure-activity relationships (QSAR), and molecular similarity analysis.

These methods help researchers explore chemical space, predict drug properties, and screen large libraries. By leveraging existing knowledge, ligand-based design can speed up drug discovery and lead to novel compounds with desired biological activity.

Ligand-based drug design overview

• Ligand-based drug design (LBDD) relies on the analysis of known ligands to identify new compounds with desired biological activity
• LBDD methods exploit the chemical and structural features of known active compounds to guide the design of new drug candidates
• Encompasses a range of computational approaches including pharmacophore modeling, quantitative structure-activity relationships (QSAR), and molecular similarity analysis

Pharmacophore modeling

Types of pharmacophore models

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• Ligand-based pharmacophore models derived from a set of known active compounds sharing a common biological target
• Structure-based pharmacophore models generated from the analysis of ligand-target interactions in available crystal structures
• Integrated pharmacophore models combine information from both ligand and target structures to enhance model quality and predictive power

Pharmacophore elucidation methods

• Manual pharmacophore design involves visual inspection and alignment of active compounds to identify common features
• Automated pharmacophore generation algorithms (HipHop, HypoGen) align compounds and extract pharmacophoric features based on predefined rules and scoring functions
• Consensus pharmacophore approaches combine multiple pharmacophore models to improve robustness and applicability

Pharmacophore-based virtual screening

• Pharmacophore models serve as 3D queries to screen large compound libraries and identify potential hits with similar pharmacophoric features
• Pharmacophore screening can be combined with other filters (physicochemical properties, ADME criteria) to prioritize compounds for experimental testing
• Successful applications of pharmacophore-based virtual screening have led to the discovery of novel bioactive compounds for various therapeutic targets (HIV protease inhibitors, kinase inhibitors)

Quantitative structure-activity relationships (QSAR)

QSAR model development process

• QSAR modeling establishes mathematical relationships between structural features (descriptors) and biological activity of a set of compounds
• Key steps include data collection and curation, descriptor calculation, feature selection, model building, and validation
• QSAR models can guide the optimization of lead compounds by predicting the activity of untested analogs

2D vs 3D QSAR approaches

• 2D QSAR methods (Free-Wilson, Hansch analysis) rely on 2D structural features (substituents, fragments) to correlate with activity
• 3D QSAR approaches (CoMFA, CoMSIA) consider the 3D alignment of compounds and calculate steric, electrostatic, and other field-based descriptors
• 3D QSAR models provide insights into the 3D requirements for optimal ligand-target interactions and can guide structure-based design efforts

QSAR model validation & applicability domain

• QSAR models must be rigorously validated using internal (cross-validation) and external (test set) validation techniques to assess their predictive performance
• Applicability domain analysis determines the chemical space where the QSAR model can make reliable predictions based on the training set composition
• Consensus QSAR models that combine predictions from multiple models can improve the robustness and applicability of QSAR-based virtual screening

Molecular similarity & diversity

Similarity & diversity measures

• Molecular similarity quantifies the structural resemblance between compounds using 2D (fingerprint-based) or 3D (shape-based) approaches
• Diversity analysis assesses the chemical space coverage and structural dissimilarity within a compound set
• Commonly used similarity metrics include Tanimoto coefficient (fingerprints) and Tversky index (pharmacophoric features)

Chemical space exploration

• Chemical space represents the vast collection of all possible chemical compounds, estimated to exceed $10^{60}$ molecules
• Sampling and navigating chemical space is crucial for identifying novel scaffolds and expanding the structural diversity of compound libraries
• Techniques such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) help visualize and explore chemical space

Scaffold hopping & bioisosteric replacement

• Scaffold hopping involves the identification of novel chemotypes that maintain the desired biological activity but possess distinct molecular scaffolds
• Bioisosteric replacement strategies substitute functional groups or substructures with bioisosteres that have similar physicochemical properties but improved ADME or selectivity profiles
• Examples of successful scaffold hopping include the discovery of non-benzodiazepine anxiolytics (buspirone) and non-nucleoside reverse transcriptase inhibitors (NNRTIs) for HIV treatment

Machine learning in ligand-based design

Supervised vs unsupervised learning

• Supervised learning algorithms (random forest, support vector machines) learn from labeled data to predict the properties of new instances
• Unsupervised learning methods (clustering, dimensionality reduction) uncover hidden patterns and relationships in unlabeled data
• Semi-supervised learning combines labeled and unlabeled data to improve model performance and generalizability

Common machine learning algorithms

• Decision trees and random forests construct models based on a series of binary decisions and can handle both classification and regression tasks
• Support vector machines (SVMs) find optimal hyperplanes to separate classes in high-dimensional feature space and are effective for QSAR modeling
• Deep learning architectures (convolutional neural networks, graph neural networks) can learn hierarchical representations from raw molecular data and have shown promising results in virtual screening and property prediction

Feature selection & model interpretation

• Feature selection techniques (recursive feature elimination, L1 regularization) identify the most informative descriptors and reduce model complexity
• Model interpretation methods (feature importance, SHAP values) provide insights into the contributions of individual features to the model predictions
• Interpretable machine learning models (decision trees, rule-based systems) offer greater transparency and explanatory power compared to black-box models

Ligand-based virtual screening

Ligand-based VS methodologies

• Ligand-based virtual screening (LBVS) aims to identify novel active compounds based on their similarity to known ligands
• LBVS approaches include pharmacophore screening, similarity searching (2D fingerprints, 3D shape), and machine learning-based methods
• Consensus scoring strategies that combine multiple LBVS methods can improve the enrichment of active compounds in virtual screening campaigns

Ligand-based VS case studies

• LBVS has been successfully applied to discover novel inhibitors for various therapeutic targets, including kinases (EGFR, CDK2), G protein-coupled receptors (GPCRs), and ion channels
• Integration of LBVS with high-throughput screening (HTS) and structure-based design has led to the identification of potent and selective lead compounds (MEK inhibitors, BACE1 inhibitors)
• LBVS has also been employed to repurpose existing drugs for new therapeutic indications (antiviral agents, anticancer compounds)

Combining ligand & structure-based approaches

• Ligand-based and structure-based drug design approaches provide complementary information and can be combined to enhance the efficiency and success of drug discovery efforts
• Ligand-based pharmacophore models can guide the docking and scoring of compounds in structure-based virtual screening
• Ligand-based SAR data can be integrated with structural insights from co-crystal structures to optimize ligand-target interactions and improve binding affinity and selectivity

ADME prediction in ligand-based design

Physicochemical property prediction

• Prediction of key physicochemical properties (molecular weight, logP, polar surface area) is essential for assessing the drug-likeness and ADME behavior of compounds
• Ligand-based QSAR models and machine learning algorithms can predict physicochemical properties based on molecular descriptors and structural features
• Compliance with drug-likeness rules (Lipinski's rule of five, Veber's rules) can be used as filters to prioritize compounds with favorable ADME profiles

Pharmacokinetic parameter estimation

• Pharmacokinetic parameters (absorption, distribution, metabolism, excretion) determine the fate of a drug in the body and impact its efficacy and safety
• Ligand-based QSAR models can predict pharmacokinetic properties such as intestinal absorption, blood-brain barrier permeability, and metabolic stability
• Integration of pharmacokinetic predictions with pharmacodynamic data enables the optimization of drug exposure and therapeutic effect

Toxicity & off-target effect prediction

• Prediction of potential toxicity and off-target effects is crucial for avoiding adverse drug reactions and improving drug safety
• Ligand-based approaches can identify structural alerts and toxicophores associated with specific toxicity endpoints (genotoxicity, cardiotoxicity)
• Machine learning models trained on large toxicity databases (Tox21, ToxCast) can predict the likelihood of a compound causing toxicity based on its structural features
• Off-target profiling using ligand-based similarity searches can identify potential unintended targets and guide the design of more selective compounds

Challenges & limitations

Activity cliff & activity landscape

• Activity cliffs represent pairs of structurally similar compounds with large differences in biological activity, posing challenges for QSAR modeling and similarity-based approaches
• Activity landscape analysis visualizes the structure-activity relationships (SAR) of a compound series and identifies regions of discontinuous SAR
• Addressing activity cliffs requires careful consideration of the structural features responsible for the activity differences and may necessitate local QSAR models or 3D approaches

Handling conformational flexibility

• Conformational flexibility of ligands and targets introduces additional complexity in ligand-based drug design, as different conformations may have distinct biological activities
• Conformational sampling techniques (molecular dynamics, low-mode conformational search) generate ensemble representations of ligands for pharmacophore modeling and 3D QSAR
• Consensus approaches that consider multiple conformations can improve the robustness and predictive power of ligand-based models

Addressing data quality & quantity issues

• The quality and quantity of available bioactivity data greatly influence the performance and applicability of ligand-based drug design methods
• Data curation and standardization procedures are essential for ensuring the consistency and reliability of the input data
• Strategies to address data limitations include data augmentation (SMOTE), transfer learning, and multi-task learning approaches
• Collaboration and data sharing initiatives (ChEMBL, PubChem) play a crucial role in expanding the chemical and biological space for ligand-based drug design