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AI を活用したライフサイエンス探索のリソース

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ユースケース

Molecular Dynamics, Trajectory and Ensemble Analysis on Vecura

Proteins move, and a single structure does not capture that. Vecura provides five related operations for working with protein dynamics: running physics-based simulations, analysing existing trajectories, generating trajectories with learned models, analysing conformational ensembles, and generating ensembles from sequence. Together they support biophysical characterisation, ensemble-based docking, binding kinetics and conformational landscape exploration. The five operations differ mainly in what you start from and how much physical accuracy you need. The selection table below is the fastest route to the right one.

インサイト

Making Water Visible: How Generative AI Can Support Protein Structure Research

Water is fundamental to biological systems, yet the individual water molecules surrounding proteins are often difficult and expensive to model. SuperWater, a generative AI framework developed to predict water molecule positions around biomolecular complexes, offers a new approach to this longstanding computational challenge.

Quy Dao
MSA Search is now available on Vecura

アップデート

Accelerating Protein Structure Prediction: An Introduction to msa-search

Discover how msa-search, a GPU-accelerated service, slashes the compute time of Multiple Sequence Alignments (MSA) from hours to seconds, significantly accelerating AlphaFold2 and ColabFold structure prediction pipelines.