ESMFold2 Description
ESMFold2 builds upon its predecessor, ESMFold, by establishing a new benchmark in single-sequence structure prediction and facilitating the creation of novel functional proteins via exploration of the latent space within the ESMC model. This advanced model is capable of forecasting high-resolution, all-atom 3D structures of biomolecular complexes straight from the amino acid sequence, and it allows for the incorporation of multiple sequence alignments to improve accuracy on difficult targets. Tailored for predicting structures through both sequence and structure modalities, it employs ESM representations that drive a series of looped folding layers while a diffusion model translates pairwise representations into atomic-resolution outcomes. ESMFold2 excels in predicting protein structures from amino acid sequences, providing detailed structural data, including precise all-atom coordinates for both backbone and side chains, along with confidence metrics and optional distogram predictions for in-depth structural evaluation. Furthermore, its innovative approach enhances the understanding of protein folding dynamics and functional implications, making it a valuable tool for researchers in the field.
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ESMC
ESMC represents the newest advancement in the ESM series of protein language models, pushing the boundaries of representation learning within the field of protein biology. With training on billions of evolutionary sequences, it adeptly captures representations that encapsulate a mechanistic understanding of protein structure and function. The model utilizes a transformer architecture, focusing on sequences as its primary modality, and is trained on a vast dataset comprising up to 6 billion proteins. ESMC is tailored for various protein science applications, such as predicting structures, annotating functions, designing proteins, and exploring evolutionary connections among proteins. Additionally, it possesses the capability to create novel proteins based on partial sequences, structures, or functional constraints, thereby enabling researchers to investigate innovative avenues in protein design and biological discovery. Accessible through the Biohub Platform, ESMC can be utilized via an API and the ESM Python package, which includes quickstart resources for installation, API key generation, and platform connectivity, ensuring a seamless experience for users. This comprehensive accessibility encourages a broader engagement with protein research and enhances collaborative efforts in the scientific community.
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FLUX 3
FLUX 3 is an advanced multimodal foundation model that integrates learning from images, video, and audio all within a cohesive framework, effectively modeling how objects connect, how movements occur, and how events produce sound. Utilizing the Self-Flow methodology, it harmonizes the generation and comprehension of multiple modalities in a singular architecture, ensuring that each modality influences the others—sound corresponds to impact, motion adheres to physical laws, and future occurrences are informed by past events. This model is capable of blending modalities, allowing for the simultaneous generation of images, video, and authentic audio based on text prompts or references such as visual and auditory inputs. Its video functionalities are extensive, featuring text-to-video capabilities, image-driven video animation, video transformation, generative continuation of video and audio, controlled transitions using keyframes, multilingual dialogue support, animated text design, and the ability to deliver various styles and aspect ratios, alongside the capacity for agentic chaining into intricate, longer multi-shot sequences. Additionally, FLUX 3 represents a significant leap forward in the field of multimodal AI, offering unprecedented flexibility and creativity in generating rich, interactive content.
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Pricing
Pricing Starts At:
Free
Free Version:
Yes
Company Details
Company:
Biohub
Year Founded:
2016
Headquarters:
United States
Website:
biohub.ai/models/esmfold2
Media
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
Customer Support
Online Support
ESMFold2 Features and Options
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