AI-Driven Drug Discovery
Harnessing our expertise in AI/ML, Computational Chemistry, and Peptide Science, we discover and optimize active, specific, non-toxic, and patentable small molecules, delivering superior drug candidates in a faster and more cost-effective manner.
We are Increasing Drug Discovery Productivity via AI/ML and Biophysics.
We have discovered and optimized two patentable, potent, selective, and brain-specific Butyrylcholinesterase Inhibitors (BChE-Is) with potential applications in late-stage Alzheimer’s - the main cause of Dementia. These inhibitors hold promise as symptomatic drugs, disease-modifying agents with neuroprotective functions, and potential biomarkers
Drug discovery from extensive chemical space -
Shooting on Targets!
PREDIT: Our Proprietary Discovery Platform
Extensive small molecule database with approximately 4 billion compounds, including peptides of tetra to penta length. This rich resource allows addressing the specificity of protein targets and customize peptide lengths based on the unique characteristics of the binding pockets.
Our advanced computational methods and extensive database efficiently identify hit compounds for drug design. Using the state-of -the art virtual screening pipeline, we locate promising compounds. Our approach guarantees customized solutions, exploiting our expertise in computational analysis and molecular modeling.
In scenarios where crystal structure data is unavailable, we employ homology modeling to construct accurate predictive 3D structures of target proteins. This method is essential for comprehending the protein's functional mechanisms and forms a robust basis for drug design and virtual screening procedures.
Our advanced virtual screening pipeline integrates a diverse range of molecular docking software and tools. This ensures comprehensive screening and facilitates the identification of potential lead molecules from our vast database, narrowing down the most promising compounds.
Hit to Lead Optimization
Advanced molecular modeling and cheminformatics techniques are used to refine and optimize potential lead molecules. This includes in-depth analysis of the structure-activity relationship (SAR) to refine potency, selectivity, and the ADMET properties of the molecules.
By utilizing predictive modeling and analyzing vast quantities of data, we can identify patterns and generate insights more quickly and efficiently. Compounds with the highest probability of success, are prioritized. This technological edge keep us at the forefront of the drug discovery field.