Research Areas

Ligand-based drug design (LBDD) is a computational approach that relies on the structural and physicochemical properties of known bioactive molecules to predict the activity of new compounds. One of the most widely used LBDD techniques is Quantitative Structure-Activity Relationship (QSAR), which establishes mathematical models correlating molecular descriptors—such as hydrophobicity, electronic properties, and steric factors—with biological activity. By leveraging machine learning and statistical methods, QSAR models enable the rapid screening and optimization of potential drug candidates, reducing the time and cost associated with traditional drug discovery.

QSAR modeling involves developing predictive equations that help identify molecular features crucial for binding affinity and therapeutic efficacy. These models can be classified into 2D-QSAR, which analyzes molecular properties in two dimensions, and 3D-QSAR, which incorporates spatial and conformational data. Advanced QSAR techniques integrate deep learning and artificial intelligence, enhancing predictive accuracy and generalizability. As a vital component of computational drug design, QSAR facilitates lead optimization, toxicity prediction, and scaffold hopping, playing a crucial role in modern pharmaceutical research.

Structure-Based Drug Design (SBDD) is a computational approach that leverages the 3D structure of biological targets, such as proteins or enzymes, to identify and optimize potential drug candidates. By utilizing high-resolution structural data from X-ray crystallography, NMR spectroscopy, or cryo-EM, SBDD enables researchers to model interactions between small molecules and their target sites. A key component of this approach is molecular docking, which predicts the preferred binding orientation of a ligand within a protein’s active site. Docking algorithms use search algorithms and scoring functions to estimate binding affinity, guiding the rational design of novel therapeutics.

Molecular docking plays a crucial role in lead identification and optimization by evaluating large libraries of compounds against a specific biological target. Techniques such as rigid docking, semi-flexible docking, and fully flexible docking allow for different levels of molecular flexibility in simulations, improving accuracy in predicting binding interactions. Advances in artificial intelligence and deep learning further enhance docking methodologies, making virtual screening more efficient and reliable. Structure-based approaches have contributed to the discovery of numerous FDA-approved drugs, demonstrating their significance in modern drug discovery pipelines.

Virtual screening of chemical libraries is a powerful computational approach used in drug discovery to identify potential bioactive compounds from vast collections of small molecules. By leveraging molecular docking, pharmacophore modeling, and machine learning techniques, virtual screening helps predict how a compound interacts with a biological target, significantly reducing the time and cost associated with traditional experimental screening. This method enables researchers to prioritize promising candidates for further testing, increasing the chances of identifying novel drug leads with desirable properties such as high binding affinity, selectivity, and favorable pharmacokinetics.

Modern virtual screening platforms integrate extensive databases of commercially available and proprietary compounds, allowing researchers to explore diverse chemical space efficiently. Structure-based approaches, such as molecular docking, evaluate ligand-target interactions using three-dimensional structural data, while ligand-based methods identify compounds with similar physicochemical and pharmacophoric features to known active molecules. The combination of artificial intelligence and high-performance computing has further enhanced the accuracy and speed of virtual screening, making it an indispensable tool in accelerating early-stage drug discovery and optimizing hit-to-lead development.

Molecular Dynamics (MD) simulations provide a powerful computational approach to studying the stability of protein-ligand complexes at the atomic level. By simulating the dynamic behavior of biomolecular systems over time, MD helps researchers understand critical interactions, binding affinities, and conformational changes that influence drug efficacy. Using force field-based calculations, the simulation captures real-time fluctuations, hydrogen bonding, hydrophobic contacts, and solvent effects, offering deeper insights into molecular recognition and stability. This method is widely used in drug discovery and structural biology to predict binding modes and optimize lead compounds before experimental validation.

A key aspect of MD simulations is energy calculation, which helps assess the stability and affinity of a ligand within the protein’s binding site. Energy components such as van der Waals interactions, electrostatic forces, solvation effects, and entropy contributions are evaluated using Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) or Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) methods. These computational techniques provide an estimate of the binding free energy, enabling researchers to rank potential drug candidates and refine molecular designs. By integrating MD simulations with energy calculations, scientists can achieve a more accurate and predictive understanding of protein-ligand interactions, accelerating the rational drug design process.

In silico ADME (Absorption, Distribution, Metabolism, and Excretion) and toxicity prediction play a crucial role in modern drug discovery, enabling the rapid evaluation of pharmacokinetic and safety profiles of compounds before experimental testing. Computational models utilize machine learning, molecular descriptors, and predictive algorithms to estimate key parameters such as solubility, permeability, metabolic stability, and potential drug-drug interactions. These predictive tools help researchers filter out unsuitable candidates early, reducing the time and cost of drug development while improving the efficiency of lead optimization.

Toxicity prediction through in silico approaches assesses potential adverse effects, including hepatotoxicity, cardiotoxicity, genotoxicity, and off-target interactions. Advanced computational techniques, including quantitative structure-activity relationships (QSAR), molecular docking, and deep learning models, enable the identification of toxicity risks associated with novel compounds. By integrating in silico ADME and toxicity assessments, researchers can enhance drug design strategies, minimize late-stage failures, and improve the safety profile of emerging therapeutic candidates.