AI-ACCELERATED DRUG DISCOVERY
COMING SOON

Artificial intelligence to expand the notion of drug design

Modular synergy of AI, computational chemistry and biotechnology to design new superior drugs, which are safe, efficient and successful in clinical trials.

Services
Drug Design Workflow
Our AI platform is integrated with partnership experimental laboratories at each stage of the drug design workflow to provide customers with precise and fast end-to-end solutions
Hit Identification
Hit-to-Lead
Lead Optimization
Target
Identification
Drug
Candidate
In Silico Drug Design
Synthesis
In vitro
In vivo
Experimental validation
Provide Lead Compounds
Integrate Assays Results
Primary in silico and in vitro target-specific selectivity assessment.
Hit Identification
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.
Target molecule preparation (homology and ab initio modeling, structural quality assessment, binding sites identification).
Generation of ultra-large chemical databases 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.).
Small molecules preparation and augmentation.


Screening of large-scale multi billion chemical databases.
Drug-target interaction prediction using graph neural networks.
AI-assisted ligand- and structure-based virtual screening.
Rapid synthesis of designed 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.
Lead Optimization
Comprehensive selectivity profiling with selected organ-specific targets.
Automated lead annotation.
The best hit compounds are expanded into the series of derivative 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.
Hit-to-Lead
Exploration of the structure-activity relationships (SAR).
Experimental feedback to improve bioactivity.
Off-target interactions evaluation using drug-target affinity predictive AI models.
Structure-based optimization (using AI and SAR information).
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.
Drug Candidate
Safety
Efficacy
Affinity
Selectivity
Stability
Bioavailability
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
A flexible modularity of our AI services can be configured to solve challenging tasks via building up a drug discovery pipeline of any complexity.
for Drug Design
Artificial Intelligence
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.
Hit Identification
  • Generation of ultra-large chemical databases (unlimited or with specified properties).
  • Target molecule preparation (homology and ab initio modeling, structural quality assessment, binding sites identification).
  • Curated custom and focused chemical libraries (fragments, diversity, natural products, etc.).
  • Fast database exploration (similarity searching, shape and electrostatic alignment and scoring).
  • Small molecules preparation and augmentation.
  • Screening of large-scale multi billion chemical databases.
  • AI-assisted ligand-based and structure-based virtual screening.
  • Drug-target interaction prediction using graph neural networks.
  • Molecular docking with custom-tuned scoring functions.
  • Rapid synthesis of predicted hit compounds.
  • Assays for hit evaluation, in vitro screening and profiling.
  • Primary in silico and in vitro target-specific selectivity assessment.
  • Primary in silico and in vitro toxicity assessment (CYP450 related toxicity, hERG binding).
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.
Hit-to-Lead
  • Experimental feedback to improve bioactivity.
  • Exploration of the structure-activity relationships (SAR).
  • Structure-based optimization (using Transformers neural network and SAR information).
  • Off-target interactions evaluation using drug-target affinity predictive AI models.
  • ADME-Tox prediction (toxicity class identification, cytotoxicity, hepatotoxicity, acute toxicity, carcinogenicity, plasma protein binding, oral bioavailability, cytochromes inhibition, etc.).
  • Computer-aided drug design (in silico hit derivatives expansion and diversification, rapid synthesis and in vitro tests).
  • Molecular dynamics and quantum calculations.
  • Advanced in silico and in vitro target-specific selectivity assessment.
  • Experimental biochemistry and enzymology.
  • Functional cellular assays.
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.
Lead Optimization
  • Experimental feedback to improve safety.
  • Optimization of the structure-activity and structure-toxicity relationships.
  • Comprehensive polypharmacological profiles of the leads via knowledge graph.
  • Validation by molecular dynamics simulations and quantum chemistry techniques.
  • Solubility and permeability fine-tuning to increase bioavailability.
  • Ames test, Irwin's test, high-dose pharmacology, repeated dose toxicity in animal models, pharmacokinetics/ pharmacodynamics (PK/PD) studies, drug-induced metabolism exploration.
  • Advanced ADME-Tox prediction (metabolism, biotransformation, biodistribution, compound excretion, Ames mutagenicity, etc.).
  • Confirmation of on-target and off-target effects.
  • Comprehensive selectivity profiling with selected organ-specific targets.
  • Automated lead annotation.
Drug Candidate
Safety
Efficacy
Affinity
Selectivity
Stability
Bioavailability
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