Advanced AI Models Boost Drug Discovery Accuracy and Efficiency, Leading to 10-20x Improvement in Margins
Before
A leading protein and pulmonary product company was struggling to identify promising drug candidates from a vast and complex dataset. The intricate nature of three-dimensional structures and biological data made it difficult to pinpoint viable candidates, resulting in inefficiencies and slower progress in drug discovery.
Solution
Our data scientists engineered a sophisticated solution using state-of-the-art AI models tailored for high-performance computing:
- 3D Convolutional Neural Networks (3D CNN) and Vision Transformer (ViT) models were employed to analyze and interpret complex 3D structures, while BERT models were adapted for biological data, all optimized for Tensor Processing Units (TPUs). This approach markedly increased the hit rate in drug discovery.
- 3D Diffusion Models (DDPM) were integrated to improve the accuracy of identifying functional mutations and to verify the stability of the results. This ensured that the drug candidates selected were not only effective but also reliable for manufacturing.
The advanced models and computing power led to the identification of more stable enzymes, which resulted in a significantly better yield in the manufacturing process, enhancing profitability and operational efficiency for the Company.
Outcome
By implementing cutting-edge AI models, the company significantly enhanced its drug discovery process. The newly developed solution increased the accuracy and efficiency of identifying stable enzymes, leading to a 10-20x improvement in manufacturing margins for end customers.
