Core clinical engineering and protocols for In-Silico ADMET Screening.
Over 90% of drug candidates entering clinical trials fail, predominantly due to unanticipated human toxicity, poor metabolic stability, or cardiotoxicity. Discovering these liabilities in Phase II/III trials costs hundreds of millions of dollars per failed program.
DrugInference evaluates candidate molecules against deep neural surrogate models trained on decades of experimental toxicology datasets. Compounds exhibiting high predicted affinity but elevated hERG blockage risk are automatically redesigned with polar substitutions that preserve target binding while abolishing cardiac liability.