Revolutionizing Therapeutics Through Computation
Our computational drug design group is at the forefront of a paradigm shift in pharmaceutical research. By integrating sophisticated modeling, machine learning, and data analytics, we aim to identify, optimize, and validate drug candidates with unprecedented speed and accuracy.
Molecular Modeling & Simulation
Precise simulation of molecular interactions to understand binding affinities, predict efficacy, and explore complex biological systems.
Learn MoreAI-Driven Drug Discovery
Utilizing machine learning for target identification, lead optimization, and predicting drug properties, dramatically reducing experimental costs.
Discover AIVirtual Screening
High-throughput virtual screening of vast chemical libraries to identify potential drug candidates that bind to specific disease targets.
Explore ScreeningStructure-Based Design
Designing novel molecules based on the 3D structure of target proteins, ensuring optimal fit and interaction for maximum therapeutic effect.
View DesignVisualizing the Future of Medicine
Simulating ligand-protein interactions for enhanced efficacy.
AI model predicting pharmacokinetic properties of new drug candidates.
Our Advanced Methodologies
Molecular Dynamics
Simulating the movement and conformational changes of molecules over time to understand dynamic behavior and interactions.
Deep Learning for QSAR
Applying deep neural networks to quantitative structure-activity relationships, predicting biological activity from molecular structure.
Pharmacophore Modeling
Identifying key features of a molecule that are necessary for its biological activity, guiding the design of new compounds.
De Novo Design
Generating entirely new molecular structures computationally, optimized for specific targets and desired properties.