Innovative Drug Discovery Powered by Nature and AI
We harness the power of proprietary AI technology to revolutionize drug discovery by rapidly screening billions of plant-derived chemicals for promising bioactive properties.
2B+
compound library
10M+
natural compounds
64x
higher hit rates than the most popular docking software
44.83%
increase in bioactivity prediction efficiency
Small Molecule Discovery Pipeline
Our pipeline is built upon three core modules: Botany and Mycology Genomics & Chemistry, AI and Computational Biology, and Biomedical Validation. This holistic approach allows us to efficiently uncover and develop potent, nature-based compounds, driving innovation and accelerating the journey from discovery to market-ready treatments. Each module works in harmony, transforming natural potential into life-changing therapies.
01
Botany and Mycology Genomics & Chemistry
Botany and Mycology Genomics & Chemistry
Building the world’s largest natural compound library from tropical medicinal herbs and fungi
We collect and cultivate a diverse array of medicinal plants in-house and from across the world to create an extensive library of small molecules. These naturally derived compounds serve as potential drug candidates, providing a rich source for novel therapeutic discoveries.
• Natural-based Small Molecules Library
• Metagenomics and Metaproteomics Analysis
• ChEMBL Library Integration
We collect and cultivate a diverse array of medicinal plants in-house and from across the world to create an extensive library of small molecules. These naturally derived compounds serve as potential drug candidates, providing a rich source for novel therapeutic discoveries.
• Natural-based Small Molecules Library
• Metagenomics and Metaproteomics Analysis
• ChEMBL Library Integration
02
AI and Computational Biology
AI and Computational Biology
Accelerating drug discovery through advanced AI and computational techniques
Our proprietary Drug-Target Interaction Graph Neural Network (DTIGN) model employs cutting-edge graph neural networks to accurately model and predict interactions between drugs and their targets. By integrating protein language models with molecular graphs, our AI enhances the accuracy of identifying promising drug candidates, ensuring effective and reliable discoveries.
• Cross Attention Between Protein Language Models (PLM) and Molecular Graphs
• Drug-Target Interaction Graph Neural Network (DTIGN)
• Molecular Dynamic Simulations
Our proprietary Drug-Target Interaction Graph Neural Network (DTIGN) model employs cutting-edge graph neural networks to accurately model and predict interactions between drugs and their targets. By integrating protein language models with molecular graphs, our AI enhances the accuracy of identifying promising drug candidates, ensuring effective and reliable discoveries.
• Cross Attention Between Protein Language Models (PLM) and Molecular Graphs
• Drug-Target Interaction Graph Neural Network (DTIGN)
• Molecular Dynamic Simulations
03
Biomedical Validation
Biomedical Validation
Validating candidates through rigorous in vitro and in vivo studies
We conduct thorough preclinical testing of our drug candidates in non-human subjects to evaluate their safety, efficacy, and pharmacokinetics. Our comprehensive mechanistic and ADMET studies ensure that only the most promising candidates proceed to human trials, safeguarding both efficacy and patient safety.
• Preclinical Validation
• Mechanistic and ADMET Studies
We conduct thorough preclinical testing of our drug candidates in non-human subjects to evaluate their safety, efficacy, and pharmacokinetics. Our comprehensive mechanistic and ADMET studies ensure that only the most promising candidates proceed to human trials, safeguarding both efficacy and patient safety.
• Preclinical Validation
• Mechanistic and ADMET Studies
01
Botany and Mycology Genomics & Chemistry
Botany and Mycology Genomics & Chemistry
Building the world’s largest natural compound library from tropical medicinal herbs and fungi
We collect and cultivate a diverse array of medicinal plants in-house and from across the world to create an extensive library of small molecules. These naturally derived compounds serve as potential drug candidates, providing a rich source for novel therapeutic discoveries.
• Natural-based Small Molecules Library
• Metagenomics and Metaproteomics Analysis
• ChEMBL Library Integration
We collect and cultivate a diverse array of medicinal plants in-house and from across the world to create an extensive library of small molecules. These naturally derived compounds serve as potential drug candidates, providing a rich source for novel therapeutic discoveries.
• Natural-based Small Molecules Library
• Metagenomics and Metaproteomics Analysis
• ChEMBL Library Integration
02
AI and Computational Biology
AI and Computational Biology
Accelerating drug discovery through advanced AI and computational techniques
Our proprietary Drug-Target Interaction Graph Neural Network (DTIGN) model employs cutting-edge graph neural networks to accurately model and predict interactions between drugs and their targets. By integrating protein language models with molecular graphs, our AI enhances the accuracy of identifying promising drug candidates, ensuring effective and reliable discoveries.
• Cross Attention Between Protein Language Models (PLM) and Molecular Graphs
• Drug-Target Interaction Graph Neural Network (DTIGN)
• Molecular Dynamic Simulations
Our proprietary Drug-Target Interaction Graph Neural Network (DTIGN) model employs cutting-edge graph neural networks to accurately model and predict interactions between drugs and their targets. By integrating protein language models with molecular graphs, our AI enhances the accuracy of identifying promising drug candidates, ensuring effective and reliable discoveries.
• Cross Attention Between Protein Language Models (PLM) and Molecular Graphs
• Drug-Target Interaction Graph Neural Network (DTIGN)
• Molecular Dynamic Simulations
03
Biomedical Validation
Biomedical Validation
Validating candidates through rigorous in vitro and in vivo studies
We conduct thorough preclinical testing of our drug candidates in non-human subjects to evaluate their safety, efficacy, and pharmacokinetics. Our comprehensive mechanistic and ADMET studies ensure that only the most promising candidates proceed to human trials, safeguarding both efficacy and patient safety.
• Preclinical Validation
• Mechanistic and ADMET Studies
We conduct thorough preclinical testing of our drug candidates in non-human subjects to evaluate their safety, efficacy, and pharmacokinetics. Our comprehensive mechanistic and ADMET studies ensure that only the most promising candidates proceed to human trials, safeguarding both efficacy and patient safety.
• Preclinical Validation
• Mechanistic and ADMET Studies
CORE TECHONOLOGY
Drug-Target Interaction Graph Neural Network (DTIGN)
Our DTIGN model serves as the cornerstone of our AI-driven drug discovery platform. Engineered to accelerate and enhance the prediction of drug-target interactions, DTIGN plays a pivotal role in understanding bioactivity and driving groundbreaking innovations in drug development.
DTIGN offers a breakthrough AI solution with the following key benefits:
• Enhanced hit selection and lead optimization.
• Accelerated drug discovery timelines.
• Exploration of new therapeutic opportunities.
Core capabilities of DTIGN include:
• Integrating multiple advanced techniques: Graph Neural Networks (GNNs), Self-Attention Mechanisms, Semi-Supervised Learning
• Modeling intricate drug-target interactions.
DTIGN offers a breakthrough AI solution with the following key benefits:
• Enhanced hit selection and lead optimization.
• Accelerated drug discovery timelines.
• Exploration of new therapeutic opportunities.
Core capabilities of DTIGN include:
• Integrating multiple advanced techniques: Graph Neural Networks (GNNs), Self-Attention Mechanisms, Semi-Supervised Learning
• Modeling intricate drug-target interactions.
How DTIGN works?
Our model, DTIGN, predicts how well drugs interact with their targets by analyzing their molecular structures and binding patterns.
It looks at specific measurements (pIC₅₀ and pEC₅₀) that show how potent a drug is. The model uses basic physics principles (like how atoms attract or repel each other) and learns from a small set of real molecular structures.
When compared to other leading methods, DTIGN performs 27.03% better at accurately predicting how strong a drug's effect will be on its target.
It looks at specific measurements (pIC₅₀ and pEC₅₀) that show how potent a drug is. The model uses basic physics principles (like how atoms attract or repel each other) and learns from a small set of real molecular structures.
When compared to other leading methods, DTIGN performs 27.03% better at accurately predicting how strong a drug's effect will be on its target.
01
Botany and Mycology Genomics & Chemistry
Botany and Mycology Genomics & Chemistry
Building the world’s largest natural compound library from tropical medicinal herbs and fungi
We collect and cultivate a diverse array of medicinal plants in-house and from across the world to create an extensive library of small molecules. These naturally derived compounds serve as potential drug candidates, providing a rich source for novel therapeutic discoveries.
• Natural-based Small Molecules Library
• Metagenomics and Metaproteomics Analysis
• ChEMBL Library Integration
We collect and cultivate a diverse array of medicinal plants in-house and from across the world to create an extensive library of small molecules. These naturally derived compounds serve as potential drug candidates, providing a rich source for novel therapeutic discoveries.
• Natural-based Small Molecules Library
• Metagenomics and Metaproteomics Analysis
• ChEMBL Library Integration
02
AI and Computational Biology
AI and Computational Biology
Accelerating drug discovery through advanced AI and computational techniques
Our proprietary Drug-Target Interaction Graph Neural Network (DTIGN) model employs cutting-edge graph neural networks to accurately model and predict interactions between drugs and their targets. By integrating protein language models with molecular graphs, our AI enhances the accuracy of identifying promising drug candidates, ensuring effective and reliable discoveries.
• Cross Attention Between Protein Language Models (PLM) and Molecular Graphs
• Drug-Target Interaction Graph Neural Network (DTIGN)
• Molecular Dynamic Simulations
Our proprietary Drug-Target Interaction Graph Neural Network (DTIGN) model employs cutting-edge graph neural networks to accurately model and predict interactions between drugs and their targets. By integrating protein language models with molecular graphs, our AI enhances the accuracy of identifying promising drug candidates, ensuring effective and reliable discoveries.
• Cross Attention Between Protein Language Models (PLM) and Molecular Graphs
• Drug-Target Interaction Graph Neural Network (DTIGN)
• Molecular Dynamic Simulations
03
Biomedical Validation
Biomedical Validation
Validating candidates through rigorous in vitro and in vivo studies
We conduct thorough preclinical testing of our drug candidates in non-human subjects to evaluate their safety, efficacy, and pharmacokinetics. Our comprehensive mechanistic and ADMET studies ensure that only the most promising candidates proceed to human trials, safeguarding both efficacy and patient safety.
• Preclinical Validation
• Mechanistic and ADMET Studies
We conduct thorough preclinical testing of our drug candidates in non-human subjects to evaluate their safety, efficacy, and pharmacokinetics. Our comprehensive mechanistic and ADMET studies ensure that only the most promising candidates proceed to human trials, safeguarding both efficacy and patient safety.
• Preclinical Validation
• Mechanistic and ADMET Studies
01
Drug Discovery
Drug Discovery
DTIGN accelerates the identification of new drug candidates by screening billions of natural and synthetic compounds, predicting which molecules best interact with disease targets. This approach reduces discovery timelines by up to 68% compared to traditional methods.
02
Drug Repurposing
DTIGN enables researchers to find new therapeutic uses for existing drugs by accurately predicting their interactions with different disease targets. This helps pharmaceutical companies reduce the time and cost associated with bringing treatments to market, especially for diseases like cancer and metabolic disorders.
03
Nutraceutical and Wellness Product
Nutraceutical and Wellness Product
DTIGN is used to discover and optimize bioactive compounds from plants and natural sources for functional foods, supplements, and wellness products. Its AI-driven predictions help identify safe, effective ingredients that can address issues like mental wellness, metabolic health, and skin care, accelerating product development
01
Botany and Mycology Genomics & Chemistry
Botany and Mycology Genomics & Chemistry
Building the world’s largest natural compound library from tropical medicinal herbs and fungi
We collect and cultivate a diverse array of medicinal plants in-house and from across the world to create an extensive library of small molecules. These naturally derived compounds serve as potential drug candidates, providing a rich source for novel therapeutic discoveries.
• Natural-based Small Molecules Library
• Metagenomics and Metaproteomics Analysis
• ChEMBL Library Integration
We collect and cultivate a diverse array of medicinal plants in-house and from across the world to create an extensive library of small molecules. These naturally derived compounds serve as potential drug candidates, providing a rich source for novel therapeutic discoveries.
• Natural-based Small Molecules Library
• Metagenomics and Metaproteomics Analysis
• ChEMBL Library Integration
02
AI and Computational Biology
AI and Computational Biology
Accelerating drug discovery through advanced AI and computational techniques
Our proprietary Drug-Target Interaction Graph Neural Network (DTIGN) model employs cutting-edge graph neural networks to accurately model and predict interactions between drugs and their targets. By integrating protein language models with molecular graphs, our AI enhances the accuracy of identifying promising drug candidates, ensuring effective and reliable discoveries.
• Cross Attention Between Protein Language Models (PLM) and Molecular Graphs
• Drug-Target Interaction Graph Neural Network (DTIGN)
• Molecular Dynamic Simulations
Our proprietary Drug-Target Interaction Graph Neural Network (DTIGN) model employs cutting-edge graph neural networks to accurately model and predict interactions between drugs and their targets. By integrating protein language models with molecular graphs, our AI enhances the accuracy of identifying promising drug candidates, ensuring effective and reliable discoveries.
• Cross Attention Between Protein Language Models (PLM) and Molecular Graphs
• Drug-Target Interaction Graph Neural Network (DTIGN)
• Molecular Dynamic Simulations
03
Biomedical Validation
Biomedical Validation
Validating candidates through rigorous in vitro and in vivo studies
We conduct thorough preclinical testing of our drug candidates in non-human subjects to evaluate their safety, efficacy, and pharmacokinetics. Our comprehensive mechanistic and ADMET studies ensure that only the most promising candidates proceed to human trials, safeguarding both efficacy and patient safety.
• Preclinical Validation
• Mechanistic and ADMET Studies
We conduct thorough preclinical testing of our drug candidates in non-human subjects to evaluate their safety, efficacy, and pharmacokinetics. Our comprehensive mechanistic and ADMET studies ensure that only the most promising candidates proceed to human trials, safeguarding both efficacy and patient safety.
• Preclinical Validation
• Mechanistic and ADMET Studies
Publications
Publications
Publication of DTIGN on IEEE Xplore
Advancing Bioactivity Prediction Through Molecular Docking and Self-Attention.
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