Merge pull request #100 from OpenDrugDiscovery/units_improvements

Units improvements + qm1b

.github/workflows/test.yml

@@ -16,7 +16,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
         os: ["ubuntu-latest"]
 
     runs-on: ${{ matrix.os }}

openqdc/__init__.py

@@ -34,6 +34,8 @@ def get_project_root():
     "SN2RXN": "openqdc.datasets.potential.sn2_rxn",
     "QM7X": "openqdc.datasets.potential.qm7x",
     "QM7X_V2": "openqdc.datasets.potential.qm7x",
+    "QM1B": "openqdc.datasets.potential.qm1b",
+    "QM1B_SMALL": "openqdc.datasets.potential.qm1b",
     "NablaDFT": "openqdc.datasets.potential.nabladft",
     "SolvatedPeptides": "openqdc.datasets.potential.solvated_peptides",
     "WaterClusters": "openqdc.datasets.potential.waterclusters3_30",
@@ -101,6 +103,8 @@ def __dir__():
     from .datasets.interaction.metcalf import Metcalf
     from .datasets.interaction.splinter import Splinter
     from .datasets.interaction.x40 import X40
+
+    # POTENTIAL
     from .datasets.potential.ani import ANI1, ANI1CCX, ANI1CCX_V2, ANI1X, ANI2X
     from .datasets.potential.comp6 import COMP6
     from .datasets.potential.dummy import Dummy
@@ -113,6 +117,7 @@ def __dir__():
     from .datasets.potential.nabladft import NablaDFT
     from .datasets.potential.orbnet_denali import OrbnetDenali
     from .datasets.potential.pcqm import PCQM_B3LYP, PCQM_PM6
+    from .datasets.potential.qm1b import QM1B, QM1B_SMALL
     from .datasets.potential.qm7x import QM7X, QM7X_V2
     from .datasets.potential.qmugs import QMugs, QMugs_V2
     from .datasets.potential.revmd17 import RevMD17

openqdc/datasets/base.py

@@ -38,7 +38,12 @@
 )
 from openqdc.utils.package_utils import has_package, requires_package
 from openqdc.utils.regressor import Regressor  # noqa
-from openqdc.utils.units import get_conversion
+from openqdc.utils.units import (
+    DistanceTypeConversion,
+    EnergyTypeConversion,
+    ForceTypeConversion,
+    get_conversion,
+)
 
 if has_package("torch"):
     import torch
@@ -129,7 +134,7 @@ def __init__(
         set_cache_dir(cache_dir)
         # self._init_lambda_fn()
         self.data = None
-        self._original_unit = self.__energy_unit__
+        self._original_unit = self.energy_unit
         self.recompute_statistics = recompute_statistics
         self.regressor_kwargs = regressor_kwargs
         self.transform = transform
@@ -225,24 +230,27 @@ def e0s_dispatcher(self):
     def _convert_data(self):
         logger.info(
             f"Converting {self.__name__} data to the following units:\n\
-                     Energy: {self.energy_unit},\n\
-                     Distance: {self.distance_unit},\n\
-                     Forces: {self.force_unit if self.__force_methods__ else 'None'}"
+                     Energy: {str(self.energy_unit)},\n\
+                     Distance: {str(self.distance_unit)},\n\
+                     Forces: {str(self.force_unit) if self.__force_methods__ else 'None'}"
         )
         for key in self.data_keys:
             self.data[key] = self._convert_on_loading(self.data[key], key)
 
     @property
     def energy_unit(self):
-        return self.__energy_unit__
+        return EnergyTypeConversion(self.__energy_unit__)
 
     @property
     def distance_unit(self):
-        return self.__distance_unit__
+        return DistanceTypeConversion(self.__distance_unit__)
 
     @property
     def force_unit(self):
-        return self.__forces_unit__
+        units = self.__forces_unit__.split("/")
+        if len(units) > 2:
+            units = ["/".join(units[:2]), units[-1]]
+        return ForceTypeConversion(tuple(units))  # < 3.12 compatibility
 
     @property
     def root(self):
@@ -291,15 +299,15 @@ def data_shapes(self):
             "forces": (-1, 3, len(self.force_methods)),
         }
 
-    def _set_units(self, en, ds):
+    def _set_units(self, en: Optional[str] = None, ds: Optional[str] = None):
         old_en, old_ds = self.energy_unit, self.distance_unit
         en = en if en is not None else old_en
         ds = ds if ds is not None else old_ds
         self.set_energy_unit(en)
         self.set_distance_unit(ds)
         if self.__force_methods__:
-            self.__forces_unit__ = self.energy_unit + "/" + self.distance_unit
-            self._fn_forces = get_conversion(old_en + "/" + old_ds, self.__forces_unit__)
+            self._fn_forces = self.force_unit.to(str(self.energy_unit), str(self.distance_unit))
+            self.__forces_unit__ = str(self.energy_unit) + "/" + str(self.distance_unit)
 
     def _set_isolated_atom_energies(self):
         if self.__energy_methods__ is None:
@@ -308,7 +316,7 @@ def _set_isolated_atom_energies(self):
             f = get_conversion("hartree", self.__energy_unit__)
         else:
             # regression are calculated on the original unit of the dataset
-            f = get_conversion(self._original_unit, self.__energy_unit__)
+            f = self._original_unit.to(self.energy_unit)
         self.__isolated_atom_energies__ = f(self.e0s_dispatcher.e0s_matrix)
 
     def convert_energy(self, x):
@@ -324,17 +332,19 @@ def set_energy_unit(self, value: str):
         """
         Set a new energy unit for the dataset.
         """
-        old_unit = self.energy_unit
+        # old_unit = self.energy_unit
+        # self.__energy_unit__ = value
+        self._fn_energy = self.energy_unit.to(value)  # get_conversion(old_unit, value)
         self.__energy_unit__ = value
-        self._fn_energy = get_conversion(old_unit, value)
 
     def set_distance_unit(self, value: str):
         """
         Set a new distance unit for the dataset.
         """
-        old_unit = self.distance_unit
+        # old_unit = self.distance_unit
+        # self.__distance_unit__ = value
+        self._fn_distance = self.distance_unit.to(value)  # get_conversion(old_unit, value)
         self.__distance_unit__ = value
-        self._fn_distance = get_conversion(old_unit, value)
 
     def set_array_format(self, format: str):
         assert format in ["numpy", "torch", "jax"], f"Format {format} not supported."

openqdc/datasets/io.py

@@ -47,8 +47,8 @@ def __init__(
         self.recompute_statistics = True
         self.refit_e0s = True
         self.energy_type = energy_type
-        self._original_unit = energy_unit
         self.__energy_unit__ = energy_unit
+        self._original_unit = self.energy_unit
         self.__distance_unit__ = distance_unit
         self.__energy_methods__ = [PotentialMethod.NONE if not level_of_theory else level_of_theory]
         self.energy_target_names = ["xyz"]

openqdc/datasets/potential/__init__.py

@@ -10,6 +10,7 @@
 from .nabladft import NablaDFT
 from .orbnet_denali import OrbnetDenali
 from .pcqm import PCQM_B3LYP, PCQM_PM6
+from .qm1b import QM1B, QM1B_SMALL
 from .qm7x import QM7X, QM7X_V2
 from .qmugs import QMugs, QMugs_V2
 from .revmd17 import RevMD17
@@ -39,6 +40,8 @@
     "QM7X_V2": QM7X_V2,
     "QMugs": QMugs,
     "QMugs_V2": QMugs_V2,
+    "QM1B": QM1B,
+    "QM1B_SMALL": QM1B_SMALL,
     "SN2RXN": SN2RXN,
     "SolvatedPeptides": SolvatedPeptides,
     "Spice": Spice,

openqdc/datasets/potential/ani.py

@@ -210,7 +210,21 @@ class ANI1CCX_V2(ANI1CCX):
 
 
 class ANI2X(ANI1):
-    """ """
+    """
+    The ANI-2X dataset was constructed using active learning from modified versions of GDB-11, CheMBL,
+    and s66x8. It adds three new elements (F, Cl, S) resulting in 4.6 million conformers from 13k
+    chemical isomers, optimized using the LBFGS algorithm and labeled with ωB97X/6-31G*.
+
+    Usage
+    ```python
+    from openqdc.datasets import ANI@X
+    dataset = ANI2X()
+    ```
+
+    References:
+    - ANI-2x: https://doi.org/10.1021/acs.jctc.0c00121
+    - Github: https://github.com/aiqm/ANI1x_datasets
+    """
 
     __name__ = "ani2x"
     __energy_unit__ = "hartree"

openqdc/datasets/potential/qm1b.py

@@ -0,0 +1,157 @@
+import os
+from functools import partial
+from os.path import join as p_join
+
+import datamol as dm
+import numpy as np
+import pandas as pd
+
+from openqdc.datasets.base import BaseDataset
+from openqdc.methods import PotentialMethod
+from openqdc.utils.io import get_local_cache
+
+# fmt: off
+FILE_NUM = [
+    "43005175","43005205","43005208","43005211","43005214","43005223",
+    "43005235","43005241","43005244","43005247","43005253","43005259",
+    "43005265","43005268","43005271","43005274","43005277","43005280",
+    "43005286","43005292","43005298","43005304","43005307","43005313",
+    "43005319","43005322","43005325","43005331","43005337","43005343"
+    "43005346","43005349","43005352","43005355","43005358","43005364",
+    "43005370","43005406","43005409","43005415","43005418","43005421",
+    "43005424","43005427","43005430","43005433","43005436","43005439",
+    "43005442","43005448","43005454","43005457","43005460","43005463",
+    "43005466","43005469","43005472","43005475","43005478","43005481",
+    "43005484","43005487","43005490","43005496","43005499","43005502",
+    "43005505","43005508","43005511","43005514","43005517","43005520",
+    "43005523","43005526","43005532","43005538","43005544","43005547",
+    "43005550","43005553","43005556","43005559","43005562","43005577",
+    "43005580","43005583","43005589","43005592","43005598","43005601",
+    "43005616","43005622","43005625","43005628","43005634","43005637",
+    "43005646","43005649","43005658","43005661","43005676","43006159",
+    "43006162","43006165","43006168","43006171","43006174","43006177",
+    "43006180","43006186","43006207","43006210","43006213","43006219",
+    "43006222","43006228","43006231","43006273","43006276","43006279",
+    "43006282","43006288","43006294","43006303","43006318","43006324",
+    "43006330","43006333","43006336","43006345","43006354","43006372",
+    "43006381","43006384","43006390","43006396","43006405","43006408",
+    "43006411","43006423","43006432","43006456","43006468","43006471",
+    "43006477","43006486","43006489","43006492","43006498","43006501",
+    "43006513","43006516","43006522","43006525","43006528","43006531",
+    "43006534","43006537","43006543","43006546","43006576","43006579",
+    "43006603","43006609","43006615","43006621","43006624","43006627",
+    "43006630","43006633","43006639","43006645","43006651","43006654",
+    "43006660","43006663","43006666","43006669","43006672","43006681",
+    "43006690","43006696","43006699","43006711","43006717","43006738",
+    "43006747","43006756","43006762","43006765","43006768","43006771",
+    "43006777","43006780","43006786","43006789","43006795","43006798",
+    "43006801","43006804","43006816","43006822","43006837","43006840",
+    "43006846","43006855","43006858","43006861","43006864","43006867",
+    "43006870","43006873","43006876","43006882","43006897","43006900",
+    "43006903","43006909","43006912","43006927","43006930","43006933",
+    "43006939","43006942","43006948","43006951","43006954","43006957",
+    "43006966","43006969","43006978","43006984","43006993","43006996",
+    "43006999","43007002","43007005","43007008","43007011","43007014",
+    "43007017","43007032","43007035","43007041","43007044","43007047",
+    "43007050","43007053","43007056","43007062","43007068","43007080",
+    "43007098","43007110","43007119","43007122","43007125",
+]
+# fmt: on
+
+
+def extract_from_row(row, file_idx=None):
+    smiles = row["smile"]
+    z = np.array(row["z"])[:, None]
+    c = np.zeros_like(z)
+    x = np.concatenate((z, c), axis=1)
+    positions = np.array(row["pos"]).reshape(-1, 3)
+
+    res = dict(
+        name=np.array([smiles]),
+        subset=np.array(["qm1b"]) if file_idx is None else np.array([f"qm1b_{file_idx}"]),
+        energies=np.array([row["energy"]]).astype(np.float64)[:, None],
+        atomic_inputs=np.concatenate((x, positions), axis=-1, dtype=np.float32),
+        n_atoms=np.array([x.shape[0]], dtype=np.int32),
+    )
+    return res
+
+
+class QM1B(BaseDataset):
+    """
+    QM1B is a low-resolution DFT dataset generated using PySCF IPU.
+    It is composed of one billion training examples containing 9-11 heavy atoms.
+    It was created by taking 1.09M SMILES strings from the GDB-11 database and
+    computing molecular properties (e.g. HOMO-LUMO gap) for a set of up to 1000
+    conformers per molecule at the B3LYP/STO-3G level of theory.
+
+    Usage:
+    ```python
+    from openqdc.datasets import QM1B
+    dataset = QM1B()
+    ```
+
+    References:
+    - https://arxiv.org/pdf/2311.01135
+    - https://github.com/graphcore-research/qm1b-dataset/
+    """
+
+    __name__ = "qm1b"
+
+    __energy_methods__ = [PotentialMethod.B3LYP_STO3G]
+    __force_methods__ = []
+
+    energy_target_names = ["b3lyp/sto-3g"]
+    force_target_names = []
+
+    __energy_unit__ = "ev"
+    __distance_unit__ = "bohr"
+    __forces_unit__ = "ev/bohr"
+    __links__ = {
+        "qm1b_validation.parquet": "https://ndownloader.figshare.com/files/43005175",
+        **{f"part_{i:03d}.parquet": f"https://ndownloader.figshare.com/files/{FILE_NUM[i]}" for i in range(0, 256)},
+    }
+
+    @property
+    def root(self):
+        return p_join(get_local_cache(), "qm1b")
+
+    @property
+    def preprocess_path(self):
+        path = p_join(self.root, "preprocessed", self.__name__)
+        os.makedirs(path, exist_ok=True)
+        return path
+
+    def read_raw_entries(self):
+        filenames = list(map(lambda x: p_join(self.root, f"part_{x:03d}.parquet"), list(range(0, 256)))) + [
+            p_join(self.root, "qm1b_validation.parquet")
+        ]
+
+        def read_entries_parallel(filename):
+            df = pd.read_parquet(filename)
+
+            def extract_parallel(df, i):
+                return extract_from_row(df.iloc[i])
+
+            fn = partial(extract_parallel, df)
+            list_of_idxs = list(range(len(df)))
+            results = dm.utils.parallelized(fn, list_of_idxs, scheduler="threads", progress=False)
+            return results
+
+        list_of_list = dm.utils.parallelized(read_entries_parallel, filenames, scheduler="processes", progress=True)
+
+        return [x for xs in list_of_list for x in xs]
+
+
+class QM1B_SMALL(QM1B):
+    """
+    QM1B_SMALL is a subset of the QM1B dataset containing a
+    maximum of 15 random conformers per molecule.
+
+    Usage:
+    ```python
+    from openqdc.datasets import QM1B_SMALL
+    dataset = QM1B_SMALL()
+    ```
+    """
+
+    __name__ = "qm1b_small"

openqdc/methods/enums.py

@@ -9,7 +9,7 @@
 
 class StrEnum(str, Enum):
     def __str__(self):
-        return self.value
+        return self.value.lower()
 
 
 @unique
@@ -45,6 +45,7 @@ class BasisSet(StrEnum):
     HA_DZ = "haDZ"
     HA_TZ = "haTZ"
     CBS_ADZ = "cbs(adz)"
+    STO3G = "sto-3g"
     GSTAR = "6-31g*"
     CC_PVDZ = "cc-pvdz"
     CC_PVTZ = "cc-pvtz"
@@ -231,6 +232,7 @@ class PotentialMethod(QmMethod):  # SPLIT FOR INTERACTIO ENERGIES AND FIX MD1
     B1PW91_VWN5_DZP = Functional.B1PW91_VWN5, BasisSet.DZP
     B1PW91_VWN5_SZ = Functional.B1PW91_VWN5, BasisSet.SZ
     B1PW91_VWN5_TZP = Functional.B1PW91_VWN5, BasisSet.TZP
+    B3LYP_STO3G = Functional.B3LYP, BasisSet.STO3G  # TODO: calculate e0s
     B3LYP_VWN5_DZP = Functional.B3LYP_VWN5, BasisSet.DZP
     B3LYP_VWN5_SZ = Functional.B3LYP_VWN5, BasisSet.SZ
     B3LYP_VWN5_TZP = Functional.B3LYP_VWN5, BasisSet.TZP