Primary in silico and in vitro target-specific selectivity assessment.
We utilize the most efficient hit identification methodology combining AI techniques, traditional in silico approaches and feedback from experimental evaluation, which provides favorable candidates for further lead selection and optimization.
Primary in silico and in vitro toxicity assessment (CYP450 related toxicity, hERG binding).
Target molecule preparation (homology and ab initio modeling, structural quality assessment, binding sites identification).
Generation of ultra-large chemical databases (unlimited or with specified properties).
Fast database exploration (similarity searching, shape and electrostatic alignment and scoring).
Curated custom and focused chemical libraries (fragments, diversity, natural products, etc.).
Screening of large-scale multi billion chemical databases.
Small molecules preparation and augmentation.
Drug-target interaction prediction using graph neural networks.
AI-assisted ligand-based and structure-based virtual screening.
Rapid synthesis of predicted hit compounds.
Molecular docking with custom-tuned scoring functions.
Assays for hit evaluation, in vitro screening and profiling.
Safety, affinity and selectivity of the top-tier lead compounds are maximized by adjusting their chemical structures with AI-based drug optimization methods, synthetic chemistry and iterative experimental assessment. This efficiently turns the leads into drug candidates ready for preclinical and clinical testing.
Comprehensive selectivity profiling with selected organ-specific targets.
Automated lead annotation.
The most promising hit compounds are selected by in silico assessment and experimental validation and then promoted to the lead generation phase. The best hits are expanded into the series of related compounds which are assessed for pharmacokinetic and toxicological parameters with multiple AI models tuned by experimental feedback. Our approach eliminates dead-end structures and reduces time and costs of lead development significantly.
Functional cellular assays.
Experimental biochemistry and enzymology.
Exploration of the structure-activity relationships (SAR).
Experimental feedback to improve bioactivity.
Evaluation of off-target interactions using drug-target interaction (DTI) predictive models.
Structure-based optimization (using Transformers neural network and SAR information).
Medicinal chemistry and computer-aided drug design (in silico hit derivatives expansion and diversification, rapid synthesis and in vitro tests).
ADME-Tox prediction (toxicity class identification, cytotoxicity, hepatotoxicity, acute toxicity, carcinogenicity, plasma protein binding, oral bioavailability, cytochromes inhibition, etc.).
Advanced in silico and in vitro target-specific selectivity assessment.
Molecular dynamics and quantum calculations.
Optimization of the structure-activity and structure-toxicity relationships.
Experimental feedback to improve safety.
Advanced medicinal chemistry and molecular optimization.
Comprehensive polypharmacological profiles of the leads via knowledge graph.
Solubility and permeability fine-tuning to increase bioavailability
Validation by molecular dynamics simulations and quantum chemistry techniques.
Advanced ADME-Tox prediction (metabolism, biotransformation, biodistribution, compound excretion, Ames mutagenicity, etc.).
Ames test, Irwin's test, high-dose pharmacology, repeated dose toxicity in animal models, pharmacokinetics/ pharmacodynamics (PK/PD) studies, drug-induced metabolism exploration.
Confirmation of on-target and off-target effects.
A flexible modularity of our AI services can be configured to solve challenging tasks
via building up a drug discovery pipeline of any complexity.
Artificial Intelligence for Drug Design