LossLab: Modular Coordinate Refinement Library

LossLab is a modular library supporting coordinate refinement against experimental data: cryo-EM maps, crystallographic structure factors, and beyond.

LossLab is based on pytorch.

Structure and Vision

LossLab implements two primary abstractions:

1. Losses. These are likelihood functions that compute the probability of some structure given a set of experimental data: p(x|D). A common interface to these losses is enforced by an abstract base class, BaseLoss.

2. The Refinement Engine, a gradient decent manager and logger. Many of the outputs of refinement are common to all refinement strategies: structures as a function of iteration, compute metrics, etc. The RefinementEngine class implements these common features and provides a foundation which specific refinement implementations can extend.

Out of scope

LossLab does not generate or sample structures/coordinates. LossLab simply provides a likelihood (and, via torch, liklihood gradients) and a generic system for tracking progress as one seeks to optimize that likelihood.

LossLab assumes your are working with a discrete list of cartesian coordinates that represent atomic positions. Models that use densities, continous distributions, etc. are out of scope.