schrodinger.application.glide.newxp_reorg

Class ReorgFitting

Class to compile input data appropriate for fitting and execute various fits of XP Reorg term to experiemntal data

Instance Methods

__init__(self, basename, default_zroff=0.0, default_offset=2.0)

pick_best_pose_prop(self, ligand_props, use_ref_lig=True, softrmscut=3.5, rmscut=99.0, debug=0)
Method takes a set of pose properties/per pose for a ligand and picks the best by the XP6.0 QSAR methodology.

pick_best_pose_prop_(self, ligand_props, best_props, use_ref_lig=True, rmscut=3.5, debug=0)

remove_Reorg_zroff_GS_contribution(self, pose_select_data)
Method returns the GS with the Reorg term and zroff contribution subtracted

add_ligand_fitting_data(self, complex)
Reads Glide docking pv file and imports all descriptors necessary for fitting.

_xrange_float(self, low, high, delta)
Method to act as a float xrange

adjust_props_offset2(self, orig_props, offset1, offset2, pcut, zroff)
Method recomputes the XP GlideScore property based on the new poff2/zroff.

adjust_props(self, orig_props, offset, zroff, debug=0)
Method recomputes the XP GlideScore property based on the new offset/zroff.

_rmsd(self, x, y)
Compute RMSD

fit_zroff_offp2(self, offset_range=[0.0, 4.0], offset2_range=[3.0, 7.0], pcut_range=[-2.0, 2.0], pcut_delta=0.1, offset_delta=0.5, zroff_range=[0.0, 3.0], zroff_delta=0.2)
Method to brute force find the optimal zroff and offset values...

fit_zroff_offset(self, offset_range=[0.0, 6.0], offset_delta=0.1, zroff_range=[0.0, 3.0], zroff_delta=0.1, offp2=False)
Method to brute force find the optimal zroff and offset values...

_get_param_dist(self, best_rmsd, param, mode)
Method computes distance from current parameters to another set of parameters in euclidean space

_diff_param(self, dist)
Method takes a list of distances and returns the index of any points that are within the cutoff

check_new_best_fit(self, best_rmsds, rmsd, mode, params)
Method determines if a new fit is better then the existing stored fits and if so replaces one of the stored fits

_makeValidationBasename(self, basename, params, count)

pick_best_pose_prop(self, ligand_props, use_ref_lig=True, softrmscut=3.5, rmscut=99.0, debug=0)

Method takes a set of pose properties/per pose for a ligand and picks the best by the XP6.0 QSAR methodology. This is based on the poseselect routine in Glide.

add_ligand_fitting_data(self, complex)

Reads Glide docking pv file and imports all descriptors necessary for fitting. Fill pose_select_data, reorg_components_header and sum_components_header

fit_zroff_offp2(self, offset_range=`[`0.0`,` 4.0`]`, offset2_range=`[`3.0`,` 7.0`]`, pcut_range=`[`-2.0`,` 2.0`]`, pcut_delta=0.1, offset_delta=0.5, zroff_range=`[`0.0`,` 3.0`]`, zroff_delta=0.2)

Method to brute force find the optimal zroff and offset values
zroff:   GlideScore = GlideScore+min(3.0, r_glide_XP_waterscore+zroff_value)
offset:  GlideScore = GlideScore+offset
         XP Reorg = min(max_reorg, cap)+offset  [offset is extracted from both sides of min arguments]
                    cap = 0.75*(XP_hbn+XP_PhobEnHB+XP_phobprn)
                    max_reorg = (XP_RPscore-1)*1.5 
         where offset = X if r_glide_XP_PhobEn >= Y, else offset = Z

fit_zroff_offset(self, offset_range=`[`0.0`,` 6.0`]`, offset_delta=0.1, zroff_range=`[`0.0`,` 3.0`]`, zroff_delta=0.1, offp2=False)

Method to brute force find the optimal zroff and offset values
zroff:   GlideScore = GlideScore+min(3.0, r_glide_XP_waterscore+zroff_value)
offset:  GlideScore = GlideScore+offset
         XP Reorg = min(max_reorg, cap)+offset  [offset is extracted from both sides of min arguments]
                    cap = 0.75*(XP_hbn+XP_PhobEnHB+XP_phobprn)
                    max_reorg = (XP_RPscore-1)*1.5 
if offp2 == True:  offset = X if r_glide_XP_PhobEn >= Y, else zcore = Z

Class ReorgFitting

pick_best_pose_prop(self, ligand_props, use_ref_lig=True, softrmscut=3.5, rmscut=99.0, debug=0)

add_ligand_fitting_data(self, complex)

fit_zroff_offp2(self, offset_range=[0.0, 4.0], offset2_range=[3.0, 7.0], pcut_range=[-2.0, 2.0], pcut_delta=0.1, offset_delta=0.5, zroff_range=[0.0, 3.0], zroff_delta=0.2)

fit_zroff_offset(self, offset_range=[0.0, 6.0], offset_delta=0.1, zroff_range=[0.0, 3.0], zroff_delta=0.1, offp2=False)

fit_zroff_offp2(self, offset_range=`[`0.0`,` 4.0`]`, offset2_range=`[`3.0`,` 7.0`]`, pcut_range=`[`-2.0`,` 2.0`]`, pcut_delta=0.1, offset_delta=0.5, zroff_range=`[`0.0`,` 3.0`]`, zroff_delta=0.2)

fit_zroff_offset(self, offset_range=`[`0.0`,` 6.0`]`, offset_delta=0.1, zroff_range=`[`0.0`,` 3.0`]`, zroff_delta=0.1, offp2=False)