How Our Deep Learning Outperforms Prominent Models in Bioactivity Prediction
Since the publication last summer, we have received inquiries from pharmaceutical companies, and our Pairwise Meta-learning framework was also featured in Tech news and digest articles.
Bioactivity prediction is crucial in drug design and development, as it helps scientists identify promising compounds from a vast pool of candidates, reducing the need for time-consuming and costly experiments.
Limited data labeling and incompatibility between experiments pose a significant hurdle for existing machine and deep learning approaches. Our foundation model is developed to bypass the hurdle and offer a fundamentally cost-effective solution to the pharmaceutical industry.
Three of the media mentions captured critical contributions that we can start with to re-write the norms of drug development — which made it dubbed the least efficient industry:
1. Precision achieved by innovative learning techniques
In AiNews.com, Ms. Alicia Shapiro elegantly summarized the challenge AI faces in this field, and how our deep learning achieves the precision for superior performance and cost-effectiveness.
2. Validated ability to perform well with new, unseen experimental data
“ActFound was evaluated using six real-world bioactivity datasets and proved to be more effective than nine other models within the same domain and across different domains.
This highlights its ability to not only predict bioactivity for data it is trained on but also perform well with new types of data."
In AI Wire, Ali Azhar analyzed the rationale behind existing challenges and the success of our innovation.
3. Empowering practical, scalable solutions in real world
“Imagine a world where the discovery of new drugs is accelerated, more affordable, and accessible. Thanks to an emerging AI model, ActFound, we’re getting closer to that reality.
This state-of-the-art AI tool has shown exceptional promise in predicting the bioactivity of compounds, thereby streamlining the drug discovery process.”
In The AI Enthusiast, Alex Thompson explains why our deep learning is not merely a theoretical breakthrough but a precursor to practical, scalable solutions in drug development.
Explore what we offer to speed your drug development.
Sources of image: theaienthusiast.blog & aiwire.net
