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Neat new system to evolve protein-protein interactions - while altering both sides of the interface! Watch for a description of the clever method, read the paper to get the deets on how these interfaces evolved & predictive models trained on screen data.

58,527 次观看 • 2 年前 •via X (Twitter)

5 条评论

Julia Bauman 的头像
Julia Bauman2 年前

Find the paper from @aerin_yang & coauthors here:

Soccer Bot™ 的头像
Soccer Bot™1 年前

Experience the bot completely reimagined. 🌟 A completely new design, reminders for match prediction, notifications when games are interrupted, continued or postponed. New commands like /features and /standings and new languages and much more.

ScienceStanley 的头像
ScienceStanley2 年前

Such great coverage of an amazing result. Having worked with some of these geometric learning driven molecular engineering tools, think they are going to revolutionize the whole field of compound discovery process :D

Ashton C Trotman-Grant 的头像
Ashton C Trotman-Grant2 年前

YES!👏🏾🔥

Aerin Yang 的头像
Aerin Yang2 年前

Thanks for the wonderful video, Julia!

相关视频

#NewPaper The first microscope, invented in the 16th century, was designed to unlock the secrets of the microscopic world. Today, as many fields become increasingly data-driven, there is a pressing need for new types of microscopes---tools that help us zoom in, explore, and understand complex data. We call these tools "algorithmic microscopes." Introducing the Vendiscope: The first algorithmic microscope for data collections. 🔬 The Vendiscope maximizes the probability-weighted Vendi Score of a dataset to assign a weight to each element in the collection. This weight represents a data point's contribution to the overall diversity of the collection. These weights enable high-resolution data analysis at scale. We use them to zoom in on datasets across three domains: biology, materials science, & AI. 🧬 Biology: We used the Vendiscope on the protein universe, which contains nearly 250 million proteins. We found that nearly 200 million of the proteins are near-duplicates of each other and that AlphaFold fails on proteins that contribute most to the diversity of the protein universe. (See GIF below). 🪜 Materials Science: We used the Vendiscope on the Materials Project database, which contains 170K materials as of today. We found that 85% of crystals with formation energy data are near-duplicates of each other and that ML models for materials property prediction struggle with materials that contribute most to diversity. 🤖 Artificial Intelligence: We applied the Vendiscope to CIFAR-10, a benchmark dataset containing 50K images. We found duplicates. We applied the Vendiscope to analyze state-of-the-art generative models trained on this dataset. We found the best generative models memorize training data, as is known in the AI literature. However, we can do more with the Vendiscope and characterize the type of samples that get memorized. We found that data points contributing least to diversity are more prone to memorization by these generative models. 🧠 "Our findings demonstrate that the Vendiscope can serve as a powerful tool for data-driven science, providing a systematic and scalable way to identify duplicates and outliers, as well as pinpointing samples prone to memorization and those that models may struggle to predict---even before training." 💫 "The Vendiscope provides a unified framework for analyzing complex data at scale. Researchers, engineers, and data auditors can use the Vendiscope to audit datasets, identify potential biases, and refine data collection practices. For AI ethicists, the Vendiscope offers a critical lens to understand how models interact with data, particularly in the context of bias, memorization, and data fairness, enabling better mitigation strategies to prevent undesirable outcomes in AI deployment. For scientists, the Vendiscope represents a new companion in the discovery process." #VendiScoring #AlgorithmicMicroscopy Link to paper: Authors: Amey Pasarkar (Amey Pasarkar) and Adji Bousso Dieng (@adjiboussodieng)

Vertaix® (AI & Science)

34,762 次观看 • 1 年前