Table compares how effectively each method recovers low-lying structures from these distorted inputs. In the case of Auto3D, file reading is managed entirely by RDKit, which can fail when initial bond/angle/dihedral parameters are significantly distorted. Consequently, only a portion of the 100 distorted structures were successfully processed by Auto3D, and fewer still converged to within 0.5 Å RMSD of the QM reference. By contrast, DeepConf directly optimizes from coordinates and successfully recovers the nearest local or global minimum in most cases (98% for 'distortion 1' and 60% for 'distortion 2').We should also note that Auto3D is not designed for direct geometry optimization rather it produces the lowest energy conformer no matter what starting structure is given even though the parameters are set to yield 1 conformer only. On the other hand, DeepConf is designed to overcome such issues by attempting to optimize directly from coordinates. In addition to the previously described distortion methods ('distortion 1' and 'distortion 2'), we introduced a third distortion approach ('distortion 3') to further evaluate the optimization processes for both DeepConf and Auto3D. ).