AI-assisted Discovery (AID) PLATFORM
Lead discovery is a long and expensive endeavor with multiple unforeseen difficulties creating setbacks for discovery teams. The preclinical candidate is typically reached via 15 – 25 optimization cycles following lead identification. The target to GLP tox process typically takes 3 – 4 years and costs $10 – 25 million per project in biotech companies. ChemPass’ AID platform can help our partners reduce the timeline to <2 years and the cost to <$4 million for validated targets.
ChemPass’ AI-assisted lead discovery platform promises to revolutionize the process. AI-assisted design methods coupled with innovative solutions in evaluation, scoring and selection make major differences: significant expansion of patentable IP, reduction of # compounds synthesized, reduction of lead optimization time, and higher success-rate. Each component in the platform is carefully optimized to fulfill its key objectives in the process while together as a whole they deliver a unified and powerful AI-platform.
The AID platform relies on the ONLY TECHNOLOGIES available today to generate a comprehensive and relevant synthetically feasible chemical space around lead structures that is a critical capability to truly reduce cycle time and cycle count in lead optimization. Powerful design components include scaffold hopping, side-chain optimization design, and library enumeration via SynSpace API. In addition, our proprietary generative design methodologies efficiently explore relevant chemical space around the lead while guaranteeing full synthesizability of the entire designed set (see our virtual POC study here). Property, med-chem and novelty filters automatically remove undesirable molecules.
The project relevant analog cloud is analyzed and filtered for synthesis time, reagent cost, reagent availability, and patentability. Docking or ligand-based evaluations of on-target and off-target activities are further enhanced by machine learning and deep learning models if sufficient datasets are available. Finally, ADMET models with the aid of active learning help generate an MPO score for the selection of best candidates in one or more virtual cycles.
ChemPass’ AI-assisted lead discovery platform promises to revolutionize the process. AI-assisted design methods coupled with innovative solutions in evaluation, scoring and selection make major differences: significant expansion of patentable IP, reduction of # compounds synthesized, reduction of lead optimization time, and higher success-rate. Each component in the platform is carefully optimized to fulfill its key objectives in the process while together as a whole they deliver a unified and powerful AI-platform.
The AID platform relies on the ONLY TECHNOLOGIES available today to generate a comprehensive and relevant synthetically feasible chemical space around lead structures that is a critical capability to truly reduce cycle time and cycle count in lead optimization. Powerful design components include scaffold hopping, side-chain optimization design, and library enumeration via SynSpace API. In addition, our proprietary generative design methodologies efficiently explore relevant chemical space around the lead while guaranteeing full synthesizability of the entire designed set (see our virtual POC study here). Property, med-chem and novelty filters automatically remove undesirable molecules.
The project relevant analog cloud is analyzed and filtered for synthesis time, reagent cost, reagent availability, and patentability. Docking or ligand-based evaluations of on-target and off-target activities are further enhanced by machine learning and deep learning models if sufficient datasets are available. Finally, ADMET models with the aid of active learning help generate an MPO score for the selection of best candidates in one or more virtual cycles.