Protein Database. In this work, we focus on the de novo approaches, where there are no previously solved structures of homologous proteins.

All recent molecular structure determination pipelines still rely on interactive tools with heavy hand labor. In principle, most available approaches, of which Rosetta-denovo is a typical example, use template matching and Monte Carlo sampling based on a library with millions of fragments from solved protein structures as templates for structural modeling. Briefly the target protein is divided into short fragments and structures of similar sequences in the library are identified for every fragment. And then candidate structure fragments are assembled by Monte Carlo simulated annealing to optimize a fitting score. Alternatively, RosettaES then uses a greedy conformational sampling algorithm to assemble the main chain of protein maximally consistent with sequence and density volume. The accuracy and coverage of these methods are often not satisfactory, due to limitations of the hand-crafted scoring functions and the sheer number of template structures.

### Object Detection

Approaches for object detection including Faster R-CNN, Cascade R-CNN, SNIPER, FishNet [@ren2015faster; @cai18cascadercnn; @sniper2018; @fishnet] have improved drastically in terms of performance and efficiency. These methods follow a similar framework in which the objects are extracted 