jwohlwend/boltz

Features

• Structure Prediction Models - Predicts three-dimensional biomolecular coordinates by learning spatial relationships and folding patterns from protein sequence data.
• Binding Affinity Prediction - Estimates the probability that a ligand binds to a target or calculates specific affinity values for lead compound optimization.
• Computational Workbenches - Provides a computational toolkit for predicting molecular interactions and structures to accelerate therapeutic compound development.
• Molecular Deep Learning Frameworks - Utilizes a neural network architecture designed to simulate the physical folding and docking of biomolecules.
• Protein Interaction Modeling - Analyzes how small molecules interact with protein targets to predict binding sites and effectiveness of drug candidates.
• Affinity Scoring Functions - Calculates binding probability and strength by comparing energetic states of bound and unbound molecular complexes.
• Geometric Modeling - Implements attention-based geometric modeling to determine the relative distance and orientation of atoms.
• Coordinate Refinement - Iteratively denoises coarse structural predictions to achieve high-resolution atomic coordinates using a stochastic generative process.
• Sequence Featurization - Transforms raw protein sequences into high-dimensional feature vectors using evolutionary information from related sequences.
• Cotranslated Folding Simulations - Models the process of protein folding during synthesis to predict the final stable molecular conformation.

Star history