Services

Premanand Adaikalasamy is a seasoned expert in biotechnology and bioinformatics, with extensive experience in genomics, computational biology, and data analysis. His leadership and scientific acumen form the backbone of Vettri BioMed, driving research excellence and technological innovation. As the CEO, Dr. Premanand envisions a future where bioinformatics bridges the gap between data and discovery, transforming healthcare and life sciences.

Computer-Aided Drug Designing (CADD)

At Vettri BioMed Technologies, we accelerate drug discovery and development through Computer-Aided Drug Designing (CADD), combining computational power with biological insights. Our CADD services streamline the identification of potential drug candidates, reducing costs and timelines while enhancing precision.

By leveraging advanced algorithms and molecular modeling tools, we enable target-based drug design, virtual screening, and lead optimization to facilitate the discovery of novel therapeutics.

Our Key CADD Services

1. Molecular Docking and Virtual Screening

Ligand-Based Virtual Screening – Rapidly screen large compound libraries against known active molecules.
Structure-Based Docking – Simulate interactions between ligands and protein targets to predict binding affinity.
High-Throughput Docking – Screen thousands of compounds for hit identification and ranking.

2. Molecular Dynamics (MD) Simulations

Protein-Ligand Stability Analysis – Assess the stability and flexibility of protein-ligand complexes.
Conformational Studies – Simulate molecular motions to understand conformational changes during drug binding.
Long-Timescale Simulations – Capture rare binding events and interactions for deeper insights.

3. Pharmacophore Modeling

Pharmacophore Generation – Identify the essential features responsible for target interaction.
Pharmacophore-Based Screening – Screen compound databases to find molecules matching pharmacophore models.
Lead Optimization – Refine hit compounds to enhance potency and selectivity based on pharmacophore insights.

4. Quantitative Structure-Activity Relationship (QSAR) Modeling

2D/3D-QSAR – Predict the biological activity of chemical compounds based on molecular descriptors.
Toxicity Prediction – Assess potential toxicity profiles of drug candidates.
ADMET Modeling – Predict Absorption, Distribution, Metabolism, Excretion, and Toxicity properties to improve drug-like characteristics.

5. De Novo Drug Design

Fragment-Based Drug Design – Assemble novel compounds by optimizing small molecular fragments bound to target sites.
Scaffold Hopping – Develop new chemical scaffolds to overcome patent barriers and improve efficacy.
Novel Ligand Design – Design new ligands from scratch using AI-driven algorithms and iterative feedback.

6. Protein and Antibody Design

Antibody Modeling and Optimization – Simulate antibody-antigen interactions for therapeutic antibody design.
Protein-Protein Docking – Model interactions between therapeutic proteins and disease targets.
Epitope Mapping – Identify immunogenic epitopes for vaccine and antibody development.

7. Fragment-Based Drug Discovery (FBDD)

Fragment Library Screening – Screen small fragment libraries to identify promising starting points.
Fragment-to-Lead Expansion – Grow and merge fragments to develop high-affinity lead compounds.
Hotspot Mapping – Identify critical regions of protein targets for fragment binding.