Our process begins with AI-based compound detection, utilizing advanced machine learning algorithms to swiftly and accurately identify promising chemical compounds. We sift through massive virtual libraries, predicting bioactivity and filtering for optimal drug-like properties.

This initial step dramatically reduces the time and cost of hit identification, focusing resources only on the most viable candidates for further development. By leveraging predictive models trained on vast datasets, we can explore a larger chemical space more efficiently than traditional high-throughput screening, uncovering novel molecular scaffolds with high potential.

Molecular docking is a pivotal computational technique where we predict the preferred orientation of a compound when bound to a biological target. By simulating these interactions, we analyze binding affinities and structural complementarity to rank potential drug candidates.

This method is crucial for accelerating drug development, allowing us to virtually screen and prioritize promising lead compounds for experimental validation, saving valuable lab time and resources. Our pipeline employs a consensus scoring approach, combining multiple algorithms to increase the reliability of our hit prioritization.

Molecular dynamics (MD) simulations allow us to observe the movement and behavior of molecules over time. By mimicking the interactions between drug compounds and their target proteins at an atomic level, we can predict binding stability and observe dynamic structural changes that docking alone cannot capture.

This powerful tool provides deep insights into molecular recognition, enhances energy minimization, and guides the rational design of drugs, optimizing candidates for improved efficacy and safety before they reach the lab. We analyze trajectories to calculate binding free energies (MMPBSA/GBSA) and identify key residue interactions, providing a comprehensive understanding of the compound's mechanism of action.