InSilicoTrials

A mutagenicity risk assessment tool combining expert rule-based and statistical-based QSAR prediction methodologies to evaluate the genotoxic potential of compounds and impurities according to ICH M7 guidelines.

Estimates the safety marker qNet based on up to 7 ion channels in vitro data at different concentrations, as by FDA recommendations.

Assesses a compound's mutagenicity potential by combining two complementary quantitative structure-activity methodologies, as by ICH M7 guidelines.

Simulate 10,000 trials before the first patient, stress testing every design and what-if scenario.

Platform module handling trial feasibility, forecasting, and execution within the same environment.

Generate digital twins, replace the placebo arm, and create synthetic cohorts with causal by design regulatory-grade evidence.

A multi-agent layer over the simulation core that reads your protocol, proposes scenarios, runs them in parallel, and returns the trial design most likely to succeed.

A machine learning-based tool to predict a compound's proarrhythmic risk using electrophysiology and machine learning.

500+ validated modeling frameworks including QSP, PBPK, and PK/PD, calibrated against your data.

Evaluates a compound's toxicological profile by in silico assessment of major toxicological endpoints such as genotoxicity, neurotoxicity, carcinogenicity, and skin irritation.

Virtual patient and clinical trial simulation platform for Parkinson's Disease.

Clinical simulation tool for neutropenia and chemotherapy effects.

Virtual patient and clinical trial simulation tool for Amyotrophic Lateral Sclerosis.

Virtual patient simulation tool for Gaucher Disease types 1 and 3.

Virtual patient simulation tool for Polycystic Ovary Syndrome (PCOS).

Simulation tool for assessing bone mineral density safety profiles during clinical developments.

Virtual patient modeling platform for fertility and reproductive medicine trials.

Early immunogenicity risk screen emulating the CD4+ T-cells proliferation assay to determine the potential immunogenicity risk of new protein sequences.

Predicts putative biological targets and corresponding bioactivity of a query compound based on a large database covering a chemical space of more than 600,000 molecules and 1,000,000 activity records.

Predictions of the immunotherapy treatment effect in a virtual population of mice with mammary carcinoma.

Computational tools for every step in the design and development of a vaccine or combination therapy.

Explores the interactions between a ligand towards a biological target at the molecular level, evaluates potential target-ligand combinations and quantifies target-ligand binding affinities.

Prediction of ADME properties of a compound including absorption, distribution, metabolism, and elimination.

Analyzes dissolution, diffusion and transfer of drug from intra-articular knee space to the plasma, based on physiologically-based pharmacokinetic modeling.

Quantifies Blood-Brain-Barrier (BBB) permeability to predict whether a compound will cross the BBB, alongside a whole range of molecular PhysChem properties.

Estimates insulin injection, skin absorption and residual volume in an injection port device over time via Computational Fluid Dynamics.

XPS (eXtended Particle Simulation) is a highly efficient simulation solution to understand, control and predict pharmaceutical fluid-granular processes to enhance efficiency and product quality.

Predictions of the iron distribution and elimination pathway in a virtual hemochromatosis mouse based on a system-biology model.

Predicts phenotype changes towards M1 and M2 phenotypes in an in vitro polarization experiment with customizable experimental conditions.

Virtually experiments tablet coating process for pharmaceutical applications to establish desired setups in terms of coater rotation speed, mass of tablets, and spray composition.

Leverages machine learning to uncover new drug-disease-microbe associations and predict indications for microbiome-based therapies.

Deep Learning-based tool that predicts protein-protein interactions probability through 2D maps.