Machine learning-aided drug design to meet the efficacy and safety requirements of your Target Product Profile
The ML-driven integrated drug discovery services at Chemotargets involve four main proprietary platforms that cover the entire pre-clinical phase.
Hit generation
SurfScan [1] [2] is used to perform a full fragment-based protein surface scan to identify protein environments compatible with chemical fragments. In this regard, it is an agnostic approach to design bioactive small molecules for both orthosteric and allosteric binding sites and address difficult-to-treat protein targets.
Hit-to-Lead
ChemBang is a ML-based library generator that is used in this phase to explode the chemical space around hit molecules. Then, CLARITY, our state-of-the-art platform for predictive pharmacology and safety, is applied as a compass to illuminate the best path towards the optimal small molecules.
Lead optimization
An integrated platform suite is applied in this phase to generate a drug candidate with the best possible chances to successfully reach the market. Molecular dynamics simulations are used to get a deeper understanding of pharmacokinetics, CLARITY is applied to predict ADME profiles and metabolism, and SafetyVista is used to forecast any potential side effects that may turn into serious safety liabilities in the postmarketing phase.
References
[1] Chemoisosterism in the Proteome. Jalencas X, Mestres, J. J Chem Inf Model. 2013, 53, 279-292.
Press Release
[2] Chemotargets Announces First AI-Designed Drug for Huntington’s Disease to Enter Clinical Trials 2021
