Fig. 1: Predicting conformational distributions with the DiG framework.
From: Predicting equilibrium distributions for molecular systems with deep learning
a, DiG takes the basic descriptor \({{{\mathcal{D}}}}\) of a target molecular system as input—for example, an amino acid sequence—to generate a probability distribution of structures that aims at approximating the equilibrium distribution and sampling different metastable or intermediate states. In contrast, static structure prediction methods, such as AlphaFold1, aim at predicting one single high-probability structure of a molecule. b, The DiG framework for predicting distributions of molecular structures. A deep learning model (Graphormer10) is used as modules to predict a diffusion process (→) that gradually transforms a simple distribution towards the target distribution. The model is trained so that the derived distribution pi in each intermediate diffusion time step i matches the corresponding distribution qi in a predefined diffusion process (←) that is set to transform the target distribution to the simple distribution. Supervision can be obtained from both samples (workflow in the top row) and a molecular energy function (workflow shown in the bottom row).
