Artificial Intelligence for Drug Discovery
Our AI-enabled platform provides rigorously validated drug discovery strategies, precisely tailored to meet the unique challenges of each target.
![Hit Identification](https://cdn.prod.website-files.com/65427fb4704e5b194297e82e/6550f3a652816a8f57ffad3b_HIT.png)
We employ a synergistic approach to hit identification by fusing advanced AI algorithms, physics-based computational methods, and real-time experimental feedback. This integrated methodology generates high-quality candidates that are primed for subsequent lead selection and optimization.
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Generation of ultra-large chemical databases with specified properties.
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Curated custom and focused chemical libraries (DNA-encoded, fragments, diversity, natural products, etc.).
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Screening of large-scale multi billion chemical databases.
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AI-assisted ligand- and structure-based virtual screening.
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Molecular docking with custom-tuned AI scoring functions.
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Assays for hit evaluation, in vitro screening and profiling.
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Primary in silico and in vitro toxicity assessment.
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Target molecule preparation (homology and ab initio modeling, structural quality assessment, binding sites identification).
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Ultra-fast chemical space exploration (similarity searching, shape and electrostatic alignment and scoring).
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Small molecules preparation and augmentation.
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Drug-target interaction prediction using graph neural networks.
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Rapid synthesis of designed hit compounds.
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Primary in silico and in vitro target-specific selectivity assessment.
![Hit-to-Lead](https://cdn.prod.website-files.com/65427fb4704e5b194297e82e/6550f6fa99137cd61658efb7_LEAD.png)
Optimized hit compounds are systematically transformed into a series of strategically designed derivative compounds. These are then evaluated for pharmacokinetic and toxicological profiles using an array of AI models, which are fine-tuned based on real-world experimental feedback.
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Experimental feedback to improve bioactivity.
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Structure-based optimization (using AI and SAR information).
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ADME-Tox prediction (toxicity class identification, cytotoxicity, hepatotoxicity, acute toxicity, carcinogenicity, plasma protein binding, oral bioavailability, cytochromes inhibition, etc.).
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Molecular dynamics and quantum calculations.
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Molecular docking with custom-tuned AI scoring functions.
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Experimental biochemistry and enzymology.
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Exploration of the structure-activity relationships (SAR).
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Off-target interactions evaluation using drug-target affinity predictive AI models.
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Computer-aided drug design (in silico hit derivatives expansion and diversification, rapid synthesis and in vitro tests).
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Advanced in silico and in vitro target-specific selectivity assessment.
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Functional cellular assays.
![Lead Optimization](https://cdn.prod.website-files.com/65427fb4704e5b194297e82e/6550f74d2606af8e050d999a_OPTIMIZED.png)
A powerful blend of AI algorithms and physics-based methods to fast-track your lead optimisations process. With machine learning for data-driven insights and molecular dynamics for structure refinement, we fine-tune lead compounds for optimal efficacy, safety, and pharmacokinetics.
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Experimental feedback to improve safety.
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Comprehensive polypharmacological profiles of the leads via knowledge graph.
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Validation by molecular dynamics simulations and quantum chemistry techniques.
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Ames test, Irwin's test, high-dose pharmacology, repeated dose toxicity in animal models, pharmacokinetics/ pharmacodynamics (PK/PD) studies, drug-induced metabolism exploration.
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Confirmation of on-target and off-target effects.
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Automated lead annotation.
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Optimization of the structure-activity and structure-toxicity relationships.
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Advanced medicinal chemistry and molecular optimization.
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Solubility and permeability fine-tuning to increase bioavailability
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Advanced ADME-Tox prediction (metabolism, biotransformation, biodistribution, compound excretion, Ames mutagenicity, etc.).
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Comprehensive selectivity profiling with selected organ-specific targets.
Drug Candidate
![Drug Candidate](https://cdn.prod.website-files.com/65427fb4704e5b194297e82e/65518c47f224407ac96d8852_drugcandidate.svg)
Selectivity
Stability
Bioavailability