Laurent Sifre

Title: Innovations of Laurent Sifre in Protein Structure Prediction

Introduction

Laurent Sifre is a prominent inventor based in Paris, France, known for his contributions to the field of protein structure prediction. With a total of 5 patents, Sifre has developed innovative methods that leverage advanced computational techniques to enhance our understanding of protein structures.

Latest Patents

One of Sifre's latest patents focuses on predicting protein structures using geometry neural networks. This method estimates the similarity between predicted protein structures and actual protein structures. The process involves determining alternative predicted structures of a given protein through iterations, utilizing a geometry neural network to generate an output that characterizes an alternative geometry score. This score serves as an estimate of the similarity measure between the predicted and actual structures of the protein.

Another significant patent involves determining protein distance maps by combining distance map crops. This method generates a distance map for a given protein, defined by a sequence of amino acid residues. The distance map characterizes estimated distances between these residues, enhancing the accuracy of protein structure predictions.

Career Highlights

Throughout his career, Laurent Sifre has worked with notable companies such as DeepMind Technologies Limited and GDM Holding LLC. His work in these organizations has contributed to advancements in artificial intelligence and computational biology.

Collaborations

Sifre has collaborated with esteemed colleagues, including Fabio Viola and Piotr Wojciech Mirowski. These partnerships have fostered innovative research and development in the field of protein structure prediction.

Conclusion

Laurent Sifre's innovative work in protein structure prediction showcases the intersection of computational techniques and biological research. His contributions continue to advance our understanding of protein structures and their implications in various scientific fields.