Computer-Aided Drug Design in Modern Pharmaceutical Research: An In-Silico Perspective

1.1 The Evolution of Drug Discovery

The discovery of traditional medicine was largely dependent on sudden observation, natural product screening, and rugged trial-and-error methods. Detection of therapeutic agents through systematic random screening of chemical libraries, although sometimes successful, proved unskilled, expensive, and timely. The average cost for bringing a new drug to market approval from the initial discovery exceeds 2.6 billion USD over 12-15 years; success rates remain unexpectedly low—about 90% of drug candidates entering clinical trials fail to achieve regulatory approval.  The molecular revolution of biology, with the exponential growth of computational energy, has fundamentally transformed pharmaceutical research. Explanation of disease processes at molecular and genetic levels, determining the three-dimensional structure of biological macromolecules through X-ray crystallography, and the development of sophisticated computational algorithms enabled reasonable, knowledge-based methods in the design of medicine. Computer-aided drug design represents this paradigm shift, using computational methods to guide and accelerate the discovery and optimization of therapeutic agents [1].

1.2 Principles of Computer-Aided Drug Design

CADD includes computational techniques that facilitate drug discovery by molecular interaction prediction, binding affinity estimation, pharmacological property evaluations, and prioritizing compounds for experimental validity. The underlying basic fundamental of CADD is that drugs interact with specific biological goals and apply therapeutic effects—usually proteins such as enzymes, receptors, ion channels, or nucleic acid. The three-dimensional structure of targets and molecular determinants of ligand-target interactions enable rational design of molecules with the desired binding properties.

The CADD method is usually classified into two complementary methods:

Structure-based drug design (SBDD): This method uses three-dimensional structural information of biological targets, which are obtained through experimental methods or computational modelling. SBDD techniques predict how small molecules are bound to target active sites, enabling rational changes to increase affinity and specificity. The main SBDD method includes molecular docking, de novo design, and structure-based virtual screening [2].

Ligand-based drug design (LBDD): When target structural information is unavailable or limited, LBDD uses knowledge of the molecules known to interact with the target. By analysing structural features, physical and chemical properties, and activity profiles of known ligands, LBDD methods detect patterns related to biological activities and use these patterns to design or identify new active compounds. The original LBDD method includes pharmacophore modelling, QSAR analysis, and similar searches [3].  Integration of SBDD and LBDD methods, complementarily combined with computational techniques, produces a wide workflow capable of dealing with various challenges across the pipeline.

Fig. 1: Strategies of CADD

Sources: https://www.nsrtc.com/computer-aided-drug-design.html

1.3 Impact and Significance

CADD has deeply influenced pharmaceutical research:

• Accelerating the duration of discovery: Millions to billions of compounds in computational screening identifies promising candidates in a day or week, rather than the required months or years for physical screening.
• Reducing cost: Virtual screening dramatically reduces the number of compounds needed for synthesis and experimental testing, reagent cost, and labour requirements and reduces resource cost.
• Enabling rational design: Understanding molecular interaction in atomic resolution guides intelligent molecular changes, replacing random trials-and-error with estimated-driven optimization.
• Reduce animal testing: prediction of calculated toxicity and property assessment reduces dependence on animal models and solves ethical concerns while accelerating initial stage evaluation.

• Simplify the target selectivity: Detailed structural analysis helps design molecules that increase therapeutic indices.

HIV protease inhibitors such as saquinavir and ritonavir, and influenza neuraminidase inhibitors such as zanamivir, oseltamivir, and kinase inhibitors such as imatinib and gefitinib, and many others have significantly benefited from the methods of CADD (computer-aided drug design) during their development. It demonstrates the practical utility of computational methods [4].

2. Structure-Based Drug Design Methodologies

2.1 Molecular Docking

Molecular docking is a structure-based medicine design method that predicts the desired binding orientation and affinity of small molecules (ligands) within the target protein's binding site. The docking process solves two basic questions: how a ligand can bind (binding pose/mode) and how strongly it can bind (binding affinity)?

Fig. 2: Schematic Representation of Ligand-Target Binding

Sources: https://www.ebi-edu.com/en/coup-de-coeur-research-9/

Docking Algorithms:

Docking algorithms actively search for systematically conformational space to identify optimal ligand-protein complexes. Flexible ligand requires lots of conformational space-efficient search techniques as a result of rotational bonding, protein side-chain flexibility, and multiple binding modes. The general methods include:

Systematic search method: Full sample of conformational space through systematic change of torsional angle and ligand position. Thoroughly, the calculative cost became prohibitive for molecules with numerous rotatable bonds.

Stochastic Method: Includes genetic algorithm, Monte Carlo method, and simulated annealing. These potential methods introduce random diversity and explore large conformational spaces efficiently by selecting optimal structures based on energy criteria.

Fragment-based method: Break the ligands into pieces of rigid pieces, independently docked the pieces, and reconstruct the full molecule. This method maintains accuracy and conformational complexity. Popular docking software (Autodock, Autodock Vina, Gold, Glide, Dock, FlexX, and MOE-Dock) uses optimized scoring functions for each individual algorithm and various applications [5,6].

Scoring Functions:

Scoring functions estimates binding affinities of docked poses, ranking of candidate compounds, and prediction for biological activities. The three main types are:

Force Field-based scoring: Force field (molecular mechanics force field) interaction energy is calculated using Van der Waals interaction, electrostatic interactions, hydrogen bonding, and desolvation terms. For example, AMBER, CHARMM, and OPLS force fields.

Empirical Scoring: Use individual interaction terms (hydrogen bond, hydrophobic contact, ionic interaction, entropy penalty) weighted with coefficients derived by fitting to experimental binding information. Examples include ChemScore, GlideScore, and X-score.

Knowledge-based scoring: A scoring function derived from statistical analysis of known protein-ligand complex structures, estimating optimal interaction patterns from a structural database. Examples include PMF (potential of mean force) and DrugScore.  Each scoring function presents the type advantages and limitations. The methods of force field provide physical interpretation but may lack accuracy due to approximations. Emperical functions optimize predictive accuracy but require extensive training data. Knowledge-based methods employ structural information but depend on the representation of the database [7].

Limitations and Considerations:

Despite the comprehensive application, molecular docking is facing inherent limitations:

• Scoring function accuracy: Current scoring functions imperfectly predict binding affinities with the general errors of 2-3 kcal/mol (corresponding to ~100-fold error in binding constant). Relative ranking is often more reliable than absolute values.
• Protein Flexibility: Most docking protocols consider proteins as rigid or semi-flexible, insufficiently modelling induced-fit binding where protein constructive changes adjust ligands. Advanced methods include protein flexibility through ensemble docking or flexible side-chain treatment, but the computational cost increases significantly.
• Solvent effect: Explicit water molecules and hydration effects significantly affect binding but are often simplified in docking calculations. Some protocols identify the preserved water molecules that mediate in protein-ligand interaction, but the complete solvation modelling remains challenging.
• Entropy considerations: Changing binding entropy—especially conformational entropy loss on complex formation is difficult to estimate precisely, yet it significantly affects binding free energy [8].

2.2 Pharmacophore Modelling

The fundamental spatial arrangement of molecular characteristics necessary for biological action is determined via pharmacophore modelling.  The molecular features (hydrogen bond donors/acceptors, hydrophobic areas, aromatic rings, charged groups, and exclusion volumes) required for the best supramolecular interactions with biological targets are abstractly described by a pharmacophore.

• Structure-Based Pharmacophores:

Pharmacophores are obtained directly from protein-ligand complex analysis, which identifies interaction points between ligands and binding site residues, when target structures are available. This approach allows virtual screening for compounds that match the pharmacophoric pattern and reveals the critical binding features [9].

• Ligand-Based Pharmacophores:

Pharmacophores are made from collections of active compounds when target structural information is not available. For every active substance, conformational analysis produces a number of conformers. Alignment algorithms then find common spatial arrangements of chemical characteristics. This approach relies on the idea that similar binding modes are shared by active compounds and that target interaction is mediated by similar features [10].

Fig. 3: Pharmacophore Modelling strategies

Sources: https://www.profacgen.com/pharmacophore-modeling.htm

Applications:

Pharmacophore models work for multiple purposes:

• Virtual screening: Quickly screen large compound databases for molecules with pharmacophoric properties in the appropriate spatial system.

• Hit-to-lead optimization: Guide structural changes by preserving the necessary features while exploring diversity in unnecessary regions.

• Scaffold hopping: Identify chemically distinct scaffolds while maintaining pharmacophoric properties and discovering new chemical series to avoid intellectual property conflicts [11].

Popular pharmacophore software includes Ligand Scout, Phase (Schrödinger), Discovery Studio, and MOE [12].

2.3 De Novo Drug Design

De novo design creates new molecules to fit to the computational target binding site, making compounds from scratch instead of screening existing libraries. This method explores chemical space outside the available compound collection, discovering potential unprecedented structural class.

Methodologies:

Fragment-Based Construction: Binding creates a molecule by connecting molecular pieces within the binding site. Algorithms put the initial pieces in a favourable position, add additional pieces or fragments to them, and optimize the resulting structure. Software such as LUDI, SPROUT, and GroupBuild employs this technique.

Atom-by-atom growth: sequentially adds atoms to the growing molecule, assessing energetic contributions to each additive. Thoroughly, this method creates a large number of possibilities needed for sophisticated pruning techniques.

Evolutionary algorithm: Develop the population of molecules by using the genetic algorithm principles for desirable characteristics. The molecules go through mutation (structural change) and crossover (combination of fragments from different molecules), in which selection favouring molecules score high in fitness functions in combination with binding affinity, drug -likeness, and synthetic accessibility.

Challenges:

De novo design is facing significant challenges with the synthetic accessibility of designed molecules, chemically valid structure production, and the computational cost of thoroughly exploring chemical space. Despite these limitations, successful applications show the potential of the approach, especially when medicinal chemistry is integrated to ensure practicality [13].

2.4 Molecular Dynamics Simulations

Molecular dynamics (MD) simulations create atomic-level motions of biological macromolecules and ligands over time, solving Newton’s speed equations to propagate systems through time steps. MD provides a dynamic view of molecular behavior, complementing to static docking predictions.

Fig. 4: Molecular Dynamics Simulation Process

Sources: https://www.profacgen.com/molecular-dynamics-simulation.htm

Application of drug design:

Elucidation of binding mechanism: Stimulate ligand binding pathways, detect intermediate status, transition barriers, and induced-fit conformational changes.

• Binding Free Energy Calculation: Advanced techniques like Free Energy Perturbation (FEP), Thermodynamic Integration (TI), and MM-PBSA/MM-GBSA estimate bind free energy more accurately than docking scoring functions.

• Protein flexibility: Create a conformational protein ensemble to detect the cryptic binding sites present in specific conformations for ensemble docking.

• ADME property forecast: Stimulate membrane permeation, solubility, and protein-protein interaction relevant to pharmacokinetics.

Computational Requirements:

MD simulation requires enough computational resources. For medium-sized systems, microsecond-timescale mimic requires a few weeks for special hardware such as a high-performance computing cluster or an Anton supercomputer. However, MD provides unparalleled mechanical insights by justifying computational investments for critical drug discovery questions [14].

3. Ligand-Based Drug Design Methodologies

3.1 Quantitative Structure-Activity Relationship (QSAR) Analysis

QSAR deploys mathematical relations between molecular structure and biological activities and enables prediction of new compounds' activities based on structural properties. The basic QSAR principle states that molecular structure determines characteristics and biological activities, and these relationships can be measured mathematically.

Fig. 5: The principle of Quantitative Structure-Activity Relationship

Sources: https://studylib.net/doc/25732833/qsar-for-drug-design

Molecular Descriptors:

Numerical representation of molecular structure is required for QSAR models. There are thousands of molecular descriptors, which are classified as follows:

Physiochemical Description: Include molecular weight, lipophilicity (logP), molar refractivity, polar surface area, and hydrogen bonding capacity. These descriptors are related to ADME features and drug likeness.

Topological Description: Encode molecular graph features with connectivity indices, winner indices, and Balaban indices. They contain structural complexity and shape.

Electronic Description: Charge distribution, orbital energies, and reactivity indices derived from quantum chemical calculation.

3D Description: Require three-dimensional conformations, describing molecular shape, surface characteristics, and spatial feature distributions [15].

QSAR Model Development:

Classical QSAR workflow consists of:

1. Dataset Compilation: Combine structurally diverse molecules with experimental activities.

2. Description Calculation: Count the molecular description for all molecules.

3. Feature Selection: Use statistical techniques (correlation analysis, principal component analysis, and genetic algorithms) to figure out which descriptors are most significant to the activity.

4. Model Training: Regression or classification techniques (support vector machines, random forests, neural networks, multiple linear regression, and partial least squares) can be used to determine structure-activity relationships.

5. Validation: Assess the model's predictive performance through separate test sets, cross-validation, and external validation to ensure generalizability.

6. Explanation: Analyze the contributions of the description to understand structural features that affect activity and indicate reasonable design [16].

3D-QSAR:

Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) of three-dimensional QSAR analyze aligning molecules in 3D space by calculating steric, electrostatic, hydrophobic, and hydrogen bonding fields in grid points. These fields act as descriptors related to biological activities, which reveal the optimal and adverse interaction regions that are directional to structural optimization.

Applications and Limitations:

QSAR enables predicting of activities for virtual or newly synthesized compounds, prioritizes synthesis targets, and provides SAR insights. However, the limitations include applicability domain constraints (models perform weakly outside the chemical space of training data), descriptor interpretability challenges, and dependence on the quality and quantity of data. Successful QSAR requires careful validation and integration with mechanistic understanding [17].

3.2 Similarity Searching and Molecular Fingerprints

The similarity principle in medicinal chemistry states that structurally similar molecules show similar properties and biological activities. Similarity searching identifies identical molecules similar to the known active compound, assuming they share similar activities.

Molecular Fingerprints:

Fingerprints encode the molecular structure as a binary bit string, where each bit represents the presence or absence of structural features. Types include:

Structural Key Fingerprint: A prefabricated set of substructural features (MACCS keys contain 166 bits representing common medicinal chemistry fragments).

Round Fingerprint: The Extended Connectivity Fingerprints (ECFP) or Morgan fingerprints encodes circular atomic areas of the increasing radii, containing the local structural environments.

Topological fingerprint: Encode molecular graph path or pharmacophoric pattern.

Similarity Measures:

Measure similarity in fingerprints using coefficients such as the Tanimoto coefficient (Jaccard index), the Dice coefficient, or the cosine coefficient. These metrics ranges from 0 (no similarity) to 1 (identical structure).

Virtual Screening:

Similar searching screens for millions of compounds rapidly in databases, identifying molecules with high similarity to search activities. This method efficiently enriches the hit rate in screening campaigns while exploring structural analogues of validated scaffolds [18].

4. ADMET Prediction and Drug-Likeness

4.1 Lipinski's Rule of Five and Drug-Likeness

Drug-likeness assesses whether molecules of orally bioavailable drugs with general physicochemical properties. Christopher Lipinski’s Rule of Five, derived from marketed oral drug analysis, states that there is a greater possibility of poor absorption or permeation when:

• Molecular weight exceeds 500 Da

• LogP exceeds 5

• Hydrogen bond donors exceed 5

• Hydrogen bond acceptors exceed 10

Although there are exceptions to the rules (natural products, antibiotics, and drugs for specific targets), they provide useful filters for library design and virtual screening, eliminating compounds unlikely to exhibit favourable oral bioavailability. Veber's guidelines, which emphasize polar surface area and rotatable bonds, and quantitative estimates of drug-likeness (QED scores), which offer continuous metrics instead of binary classifications, are examples of extensions.

4.2 ADMET Property Prediction

The computational prediction of absorption, distribution, metabolism, excretion, and toxicity properties enables the initial identification of compounds with unfavourable pharmacokinetic or safety profiles, reducing attrition at the late stage.

Absorption:

Models are made for predicting intestinal permeability, P-glycoprotein substrate liability affecting efflux, and oral bioavailability. Caco-2 cell permeability is used as a typical experimental measurement that is applied to a computational approach based on membrane-interaction descriptors and physicochemical characteristics.

Distribution:

Predictions include plasma protein binding, blood brain barrier penetration and volume of distribution. Such properties define tissue exposure and are also important when CNS drugs or compounds are used, the brain penetration is unwanted.

Metabolism:

Models are used to predict the specificity of cytochrome P450 substrates, metabolic stability and possible metabolites. It is predicted by CYP450 inhibition that indicates drug-drug interaction risks. Structure-based methods simulate the relationship between ligands and CYP whereas ligand-based methods are QSAR that has been trained on experimental metabolism data.

Excretion:

Dose schedules and risk of accumulation are based on renal clearance and half-life predictions.

Toxicity:

 Computational toxicology models a range of toxicity endpoints:

• Mutagenicity/Genotoxicity: The prediction of carcinogenic substances is done by Ames test.
• Cardiotoxicity: the hERG channel blockade prediction can be used to identify compounds that are at risk of cardiac arrhythmias.
• Hepatotoxicity: Models predict liver damage possibility.
• Other Endpoints: Development of toxicity, endocrine disruption, and organ-specific toxicity.

Full property predictions are offered by software platforms such as admetSAR, pkCSM, SwissADME and ADMET Predictor, although not all endpoints and chemical spaces are accurately predicted. Predictions are used to provide priorities but critical decisions need experimental confirmation [19].

5. Integrated CADD Workflows

5.1 Virtual Screening Campaigns

Virtual screening uses computational filters on large compound libraries to obtain subsets that are rich in active compounds to be experimentally validated. Common workflows are a combination of several filtering steps:

1. Preparation of Library: Identify stereoisomers and tautomers, generate 3D conformations, and screen by drug-likeness.
2. Pharmacophore Screening: filter out compounds with no critical features in haste.
3. Molecular Docking: Docking left compounds, scoring and ranking by the predicted affinity.
4. Visual Inspection: A manual visual evaluation of the most prominent compounds is conducted to assess their novelty and the plausibility of the binding process.
5. Clustering and Diversity Selection: To achieve the greatest chemical space coverage, select structurally diverse compounds for testing.
6. Experimental Validation: synthesize or purchase chosen compounds to do biochemical tests.

Effective virtual screening programs have achieved enrichment factors of 10-100-fold over random screening and the virtual screening campaigns significantly enhanced hits and reduce costs [20].

5.2 Fragment-Based Drug Discovery

Fragment-based drug discovery (FBDD) is a technique that screens libraries of small molecule fragments (usually 150-250 Da) that bind to targets with low affinity. Despite low individual affinities, fragments exhibit high ligand efficiency (binding energy per heavy atom) of fragments. Fragmented molecules are expanded into larger, and more highly affinity molecules by repeated design and synthesis.

FBDD is aided with the help of computational techniques by:

• Fragment Library Design: Choosing a wide variety of drug-like fragments of chemical space.
• Fragment Docking: This method finds binding modes when there are weak affinities, which are difficult to detect in an experiment.
• Fragment Growing/Linking: Propose strategies to elaborate fragments and connect linkages, and pathways optimization [21].

FBDD has produced FDA-approved drug such as venetoclax (BCL-2 inhibitor) and vemurafenib (BRAF inhibitor) to show that the approach is clinically viable.

Fig. 6: An overview of fragment-based drug discovery (FBDD)

Sources: https://www.researchgate.net/publication/367120792_Fragment based_approaches_to_identify_RNA_binders