Usage Guide

Usage Guide#

This guide covers the basic usage of shepherd-score for interaction profile extraction, 3D similarity scoring, and alignment.

Overview#

The package has base functions and convenience wrappers. Scoring can be done with either NumPy or Torch, but alignment requires Torch. There are also JAX implementations for both scoring and alignment of gaussian overlap, ESP similarity, and pharmacophore similarity.

Note

Applicable xTB functions and evaluation pipeline evaluations are now parallelizable through the num_workers argument in the .evaluate method.

Base Functions#

Conformer Generation#

Useful conformer generation functions are found in the shepherd_score.conformer_generation module.

Interaction Profile Extraction#

Interaction Profile

Function

Shape

shepherd_score.extract_profiles.get_molecular_surface()

Electrostatics

shepherd_score.extract_profiles.get_electrostatic_potential()

Pharmacophores

shepherd_score.extract_profiles.get_pharmacophores()

Scoring#

shepherd_score.score contains the base scoring functions with separate modules for those dependent on PyTorch (*.py), NumPy (*_np.py), and JAX (*_jax.py).

Similarity

Function

Shape

shepherd_score.score.gaussian_overlap.get_overlap()

Electrostatics

shepherd_score.score.electrostatic_scoring.get_overlap_esp()

Pharmacophores

shepherd_score.score.pharmacophore_scoring.get_overlap_pharm()

Convenience Wrappers#

Molecule Class#

shepherd_score.container.Molecule accepts an RDKit Mol object (with an associated conformer) and generates user-specified interaction profiles.

MoleculePair Class#

shepherd_score.container.MoleculePair operates on Molecule objects and prepares them for scoring and alignment.

MoleculePairBatch Class#

shepherd_score.container.MoleculePairBatch operates on a list of MoleculePair objects and enables accelerated alignment by padding all arrays to a common shape so a single compiled JAX kernel is reused across every pair. Supports optional multi-CPU parallelism.

Extraction Example#

Extraction of interaction profiles:

from shepherd_score.conformer_generation import embed_conformer_from_smiles
from shepherd_score.conformer_generation import charges_from_single_point_conformer_with_xtb
from shepherd_score.extract_profiles import get_atomic_vdw_radii, get_molecular_surface
from shepherd_score.extract_profiles import get_pharmacophores, get_electrostatic_potential

# Embed conformer with RDKit and partial charges from xTB
ref_mol = embed_conformer_from_smiles('Oc1ccc(CC=C)cc1', MMFF_optimize=True)
partial_charges = charges_from_single_point_conformer_with_xtb(ref_mol)

# Radii are needed for surface extraction
radii = get_atomic_vdw_radii(ref_mol)
# `surface` is an np.array with shape (200, 3)
surface = get_molecular_surface(ref_mol.GetConformer().GetPositions(), radii, num_points=200)

# Get electrostatic potential at each point on the surface
# `esp`: np.array (200,)
esp = get_electrostatic_potential(ref_mol, partial_charges, surface)

# Pharmacophores as arrays with averaged vectors
# pharm_types: np.array (P,)
# pharm_{pos/vecs}: np.array (P,3)
pharm_types, pharm_pos, pharm_vecs = get_pharmacophores(ref_mol, multi_vector=False)

3D Similarity Scoring Example#

Scoring the similarity of two different molecules using 3D surface, ESP, and pharmacophore similarity metrics:

from shepherd_score.score.constants import ALPHA
from shepherd_score.conformer_generation import embed_conformer_from_smiles
from shepherd_score.conformer_generation import optimize_conformer_with_xtb
from shepherd_score.container import Molecule, MoleculePair

# Embed a random conformer with RDKit
ref_mol_rdkit = embed_conformer_from_smiles('Oc1ccc(CC=C)cc1', MMFF_optimize=True)
fit_mol_rdkit = embed_conformer_from_smiles('O=CCc1ccccc1', MMFF_optimize=True)

# Local relaxation with xTB
ref_mol, _, ref_charges = optimize_conformer_with_xtb(ref_mol_rdkit)
fit_mol, _, fit_charges = optimize_conformer_with_xtb(fit_mol_rdkit)

# Extract interaction profiles
ref_molec = Molecule(ref_mol,
                     num_surf_points=200,
                     partial_charges=ref_charges,
                     pharm_multi_vector=False)
fit_molec = Molecule(fit_mol,
                     num_surf_points=200,
                     partial_charges=fit_charges,
                     pharm_multi_vector=False)

# Centers the two molecules' COM's to the origin
mp = MoleculePair(ref_molec, fit_molec, num_surf_points=200, do_center=True)

# Compute the similarity score for each interaction profile
shape_score = mp.score_with_surf(ALPHA(mp.num_surf_points))
esp_score = mp.score_with_esp(ALPHA(mp.num_surf_points), lam=0.3)
pharm_score = mp.score_with_pharm()

Alignment Example#

Alignment using the shepherd_score.container.MoleculePair class:

# Centers the two molecules' COM's to the origin
mp = MoleculePair(ref_molec, fit_molec, num_surf_points=200, do_center=True)

# Align fit_molec to ref_molec with your preferred objective function
# By default we use automatic differentiation via pytorch
surf_points_aligned = mp.align_with_surf(ALPHA(mp.num_surf_points),
                                         num_repeats=50)
surf_points_esp_aligned = mp.align_with_esp(ALPHA(mp.num_surf_points),
                                            lam=0.3,
                                            num_repeats=50)
pharm_pos_aligned, pharm_vec_aligned = mp.align_with_pharm(num_repeats=50)

# Optimal scores and SE(3) transformation matrices are stored as attributes
# mp.sim_aligned_surf, mp.sim_aligned_esp, mp.sim_aligned_pharm
# mp.transform_surf, mp.transform_esp, mp.transform_pharm

# Get a copy of the optimally aligned fit Molecule object
transformed_fit_molec = mp.get_transformed_molecule(
    se3_transform=mp.transform_surf  # or mp.transform_esp, mp.transform_pharm
)

Acclerated alignment of multiple pairs using the shepherd_score.container.MoleculePairBatch class:

from shepherd_score.container import MoleculePairBatch

batch = MoleculePairBatch(pairs)  # list of MoleculePair objects

# JAX-based volumetric alignment with padding
scores, aligned = batch.align_with_vol()

# Multi-CPU parallel via shard_map (must set XLA_FLAGS *before* importing JAX)
scores, aligned = batch.align_with_vol(num_workers=4, num_buckets=4, use_shmap=True)

Evaluation Pipelines#

We implement three evaluations of generated 3D conformers. Evaluations can be done on an individual basis or in a pipeline.

  • ConfEval: Checks validity, pre-/post-xTB relaxation, calculates 2D graph properties

  • ConsistencyEval: Inherits from ConfEval and evaluates the consistency of the molecule’s jointly generated interaction profiles with the true interaction profiles

  • ConditionalEval: Inherits from ConfEval and evaluates the 3D similarity between generated molecules and the target molecule

Note

Evaluations can be run from any molecule’s atomic numbers and positions with explicit hydrogens (i.e., straight from an xyz file).

Example:

from shepherd_score.evaluations.evaluate import ConfEval
from shepherd_score.evaluations.evaluate import UnconditionalEvalPipeline

# ConfEval evaluates the validity of a given molecule, optimizes it with xTB,
#   and also computes various 2D graph properties
# `atom_array` np.ndarray (N,) atomic numbers of the molecule (with explicit H)
# `position_array` np.ndarray (N,3) atom coordinates for the molecule
conf_eval = ConfEval(atoms=atom_array, positions=position_array)

# Alternatively, if you have a list of molecules you want to test:
uncond_pipe = UnconditionalEvalPipeline(
    generated_mols=[(a, p) for a, p in zip(atom_arrays, position_arrays)]
)
uncond_pipe.evaluate(num_workers=4)

# Properties are stored as attributes and can be converted into pandas df's
sample_df, global_series = uncond_pipe.to_pandas()

For more detailed examples, see the Jupyter notebooks in the examples/ directory of the repository.