Computational chemistry is transforming drug discovery by integrating advanced technology with scientific innovation.

Target Identification and Validation

Computational tools play a vital role in identifying molecular targets essential for combating infectious diseases.

Bioinformatics: Analyzes genomic and proteomic data to pinpoint viable drug targets.

Homology Modeling: Predicts protein structures of novel pathogens to aid target identification.

Virtual Screening

High-speed computational methods enable researchers to search vast compound libraries efficiently.

Docking Simulations: Predict how small molecules interact with active protein sites.

Machine Learning Models: Assess compounds’ therapeutic potential based on bioactivity patterns.

De Novo Drug Design

Algorithms facilitate the creation of novel drug candidates from scratch.

Generative AI: Utilizes neural networks to design molecules with tailored properties.

Quantitative Structure-Activity Relationship (QSAR): A model that correlates chemical structure with biological activity.

Molecular Dynamics Simulations

Simulating molecular interactions provides key insights into drug stability and efficacy.

Enzyme Inhibition Studies: Investigate how drugs inhibit pathogen enzymes.

Protein-Ligand Binding Dynamics: Predict the strength and stability of drug-target interactions.

Predictive Toxicology

Computational models help assess potential risks before synthesis.

In Silico Models: Identify possible toxic effects of drug candidates.

ADMET Analysis: Predict absorption, distribution, metabolism, excretion, and toxicity.

Data Analytics

Advanced data analytics enhance drug discovery decision-making.

AI-Driven Insights: Synthesize data from clinical trials, patient records, and global health trends.

Multi-Omics Analysis: Integrate genomics, proteomics, and metabolomics for a comprehensive perspective.