BioAI
Artificial Intelligence Drug Discovery
BioAI aims to enhance medical care by accelerating the discovery of new drugs and treatment possibilities by applying artificial intelligence to predict interactions between drugs and drug target proteins. We hope this to benefit pharmaceutical companies, patients, and society at large with scalable and cost-effective solutions.
Strengths of Our Products over Traditional Methods
compared to traditional methods to our platform method, the conventional way to test the structure of potential drug candidates is through x-ray crystallography and NMR.
However, our platform will allow our AI to learn from an entire database of proteins at once.
This will streamline the R&D process of drug creation by processing more images at once for a lower cost and greater accuracy.
3D structure predictable by protein sequence only
• Structural prediction by combining the physical and chemical properties and regularity of protein sequences
• Protein sequences are relatively less difficult to obtain than PDB files
Prediction of drug binding structures
• Protein input -> drug recommendation and binding site analysis -> optimal drug prediction and recommendation
• When a candidate is determined, the predicted binding site is reasonable and optimized for biological and molecular dynamics.
High accuracy through various drug-protein database learnings
• Increasing accuracy by introducing various experimental data as well as the structural information of compounds
Predictable effectiveness of target proteins and large numbers of drugs
• High efficiency due to an increased number of drugs simultaneously instead of one at a time.
Protein sequencing of new drug candidates can be predicted
• Competitors processes are divided into stages, but our product completes sequencing at one step
• Consistent results, no advanced training required for using platform
Protein structure predictions must be made accurately, but new drug candidates need to be discovered quickly.
DeepPharming will increase the efficacy of finding the correct drug candidate by determining the exact binding site using deep learning predictions.
Prediction technology of Deep Pharming:
- Reduces costs, trials, and errors, and increases efficiency
- Automated with deep learning protein structure prediction technology
- Pre-trained Deep Learning Model predicts accurate structure identification
- Users can determine exact binding site
- Further research based on predicted protein structure is possible with additional resources
Must be made accurately,
but also Quickly.
Data
Collaboraters
