New AI Method for Protein Structure Prediction Handles All Kinds of Biologically Relevant Molecules

New AI Method for Protein Structure Prediction Handles All Kinds of Biologically Relevant Molecules.

Overview

Author(s): LucianoSphere

Originally published on Towards AI.

Allowing scientists to predict their joint structures and to create new proteins designed to specifically bind defined molecules
Image composed by the author from Dall-E 2 generations and own-made illustrations.

Predicting the complex three-dimensional structures of proteins with high accuracy is no longer a dream, thanks to deep learning networks like AlphaFold2 and others that followed it. But proteins don’t work alone. They interact with other proteins, with DNA, RNA, and small molecules and ions of all kinds — all crucial to their biological function. These interactions have been a huge challenge to model, but that’s only until now, again, thanks to deep learning.

Presented in a preprint last week, the new software RoseTTAFold All-Atom (I’ll call it just RFAA)… Read the full blog for free on Medium.

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Published via Towards AI

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AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.