Precisely Fast Drug Discovery
COMING SOON
AI-automated Platform for Rapid & Precise Drug Design
Our in-house platform is equipped with an extensive user interface and allows to automate, plan and control all aspects of multiple drug discovery projects developed simultaneously
Solutions
Accelerated
40+ advanced AI Methodologies open up a new high-speed avenue for drug candidate design
Precise
Molecules are designed with atom-like precision through a holistic approach of entwinding computational chemistry and AI methods
Customizable
Modular platform architecture can be easily fine-tuned to make drug design more flexible
Validated
Our experimentally-proven platform integrates biological assays to deliver high-quality medicines
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
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 (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.
Lead Optimization
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.
Hit-to-Lead
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.
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.
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 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.
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).
- Evaluation of off-target interactions using drug-target interaction (DTI) predictive models.
- ADME-Tox prediction (toxicity class identification, cytotoxicity, hepatotoxicity, acute toxicity, carcinogenicity, plasma protein binding, oral bioavailability, cytochromes inhibition, etc.).
- Medicinal chemistry and 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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