diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..69e08bd --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,53 @@ +name: CI + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + +jobs: + ubuntu: + runs-on: ubuntu-latest + steps: + - name: Checkout respository + uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.10' + - name: Install dependencies + run: | + pip install -e . + - name: Run tests + run: python -m unittest discover + + # macos: + # runs-on: macos-latest + # steps: + # - uses: actions/checkout@v2 + # - name: Set up Python + # uses: actions/setup-python@v2 + # with: + # python-version: '3.9' + # - name: Install dependencies + # run: | + # pip install -e . + # pip install -e ".[dev]" + # - name: Run tests + # run: python -m unittest discover + + # windows: + # runs-on: windows-latest + # steps: + # - uses: actions/checkout@v2 + # - name: Set up Python + # uses: actions/setup-python@v2 + # with: + # python-version: '3.9' + # - name: Install dependencies + # run: | + # pip install -e . + # pip install -e ".[dev]" + # - name: Run tests + # run: python -m unittest discover \ No newline at end of file diff --git a/.gitignore b/.gitignore index b6e4761..0a9d98a 100644 --- a/.gitignore +++ b/.gitignore @@ -26,6 +26,7 @@ share/python-wheels/ .installed.cfg *.egg MANIFEST +gbmtsplits/_version.py # PyInstaller # Usually these files are written by a python script from a template diff --git a/gbmtsplits/__init__.py b/gbmtsplits/__init__.py new file mode 100644 index 0000000..994a5cf --- /dev/null +++ b/gbmtsplits/__init__.py @@ -0,0 +1,11 @@ +import os + +from .split import GloballyBalancedSplit +from .clustering import * + +__version__ = '0.0.5' +if os.path.exists(os.path.join(os.path.dirname(__file__), '_version.py')): + from ._version import version + __version__ = version + +VERSION = __version__ \ No newline at end of file diff --git a/src/gbmtsplits/cli.py b/gbmtsplits/cli.py similarity index 92% rename from src/gbmtsplits/cli.py rename to gbmtsplits/cli.py index 7eb60d1..5eadc4c 100644 --- a/src/gbmtsplits/cli.py +++ b/gbmtsplits/cli.py @@ -1,6 +1,8 @@ +import os import argparse import pandas as pd from timeit import default_timer as timer +from .logs.config import enable_logging from .split import GloballyBalancedSplit from .clustering import RandomClustering, LeaderPickerClustering, MaxMinClustering, MurckoScaffoldClustering @@ -44,8 +46,8 @@ def main(): # Start the timer start_time = timer() + # Parse arguments args = parser.parse_args() - # Read input data from csv/tsv file ########################## if '.csv' in args.input: df = pd.read_csv(args.input) @@ -62,6 +64,18 @@ def main(): if not args.output: args.output = args.input.split('.')[0] + # Enable logging ############################################# + logSettings = enable_file_logger( + os.path.dirname(args.output), + "gbmtsplits.log", + False, + __name__, + vars(args), + disable_existing_loggers=False, + ) + log = logSettings.log + + # Setup splitter ############################################# if args.clustering == 'random': clustering = RandomClustering(n_clusters=args.n_clusters, seed=args.random_seed) @@ -101,7 +115,7 @@ def main(): # Print elapsed time ######################################### elapsed_time = timer() - start_time - print('Elapsed time: {:.2f} seconds'.format(elapsed_time)) + log.info('Elapsed time: {:.2f} seconds'.format(elapsed_time)) if __name__ == '__main__': diff --git a/src/gbmtsplits/clustering.py b/gbmtsplits/clustering.py similarity index 99% rename from src/gbmtsplits/clustering.py rename to gbmtsplits/clustering.py index 7b4dd52..07b1b25 100644 --- a/src/gbmtsplits/clustering.py +++ b/gbmtsplits/clustering.py @@ -30,7 +30,7 @@ def get_name(self) -> str: return self.__class__.__name__ def _set_n_clusters(self, N : int) -> None: - self.n_clusters = self.n_clusters if self.n_clusters is not None else N // 100 + self.n_clusters = self.n_clusters if self.n_clusters is not None else N // 10 diff --git a/gbmtsplits/logs/__init__.py b/gbmtsplits/logs/__init__.py new file mode 100644 index 0000000..149b010 --- /dev/null +++ b/gbmtsplits/logs/__init__.py @@ -0,0 +1,12 @@ +import logging +import sys + +logger = None + +if not logger: + logger = logging.getLogger("gbmtsplits") + logger.setLevel(logging.INFO) + + +def setLogger(log): + sys.modules[__name__].gbmtsplits = log \ No newline at end of file diff --git a/gbmtsplits/logs/config.py b/gbmtsplits/logs/config.py new file mode 100644 index 0000000..220b654 --- /dev/null +++ b/gbmtsplits/logs/config.py @@ -0,0 +1,228 @@ +import os +import git +import json +import logging +from bisect import bisect +from datetime import datetime +from logging import config + + +from . import setLogger + +class LogFileConfig: + def __init__(self, path, logger, debug): + self.path = path + self.log = logger + self.debug = debug + +class LevelFilter(logging.Filter): + """ + LoggingFilter used to filter one or more specific log levels messages + """ + def __init__(self, level): + self.__level = level + + def filter(self, record): + return record.levelno in self.__level + + +# Adapted from https://stackoverflow.com/a/68154386 +class LevelFormatter(logging.Formatter): + """LoggingFormatter used to specifiy the formatting per level""" + def __init__(self, formats: dict[int, str], **kwargs): + super().__init__() + + if "fmt" in kwargs: + raise ValueError( + "Format string must be passed to level-surrogate formatters, " + "not this one" + ) + + self.formats = sorted( + (level, logging.Formatter(fmt, **kwargs)) for level, fmt in formats.items() + ) + + def format(self, record: logging.LogRecord) -> str: + idx = bisect(self.formats, (record.levelno, ), hi=len(self.formats) - 1) + level, formatter = self.formats[idx] + return formatter.format(record) + + +def config_logger(log_file_path, debug=None, disable_existing_loggers=True): + """ + Function to configure the logging. + All info is saved in a simple format on the log file path. + Debug entries are saved to a separate file if debug is True + Debug and warning and above are save in a verbose format. + Warning and above are also printed to std.out + + Args: + log_file_path (str): Folder where all logs for this run are saved + debug (bool): if true, debug messages are saved + no_exist_log (bool): if true, existing loggers are disabled + """ + debug_path = os.path.join(os.path.dirname(log_file_path), "debug.log") + simple_format = "%(message)s" + verbose_format = "[%(asctime)s] %(levelname)s [%(filename)s %(name)s %(funcName)s (%(lineno)d)]: %(message)s" # noqa: E501 + + LOGGING_CONFIG = { + "version": 1, + "disable_existing_loggers": disable_existing_loggers, + "formatters": + { + "simple_formatter": { + "format": simple_format + }, + "verbose_formatter": { + "format": verbose_format + }, + "bylevel_formatter": + { + "()": LevelFormatter, + "formats": + { + logging.DEBUG: verbose_format, + logging.INFO: simple_format, + logging.WARNING: verbose_format, + }, + }, + }, + "filters": { + "only_debug": { + "()": LevelFilter, + "level": [logging.DEBUG] + } + }, + "handlers": + { + "stream_handler": + { + "class": "logging.StreamHandler", + "formatter": "simple_formatter", + "level": "WARNING", + }, + "file_handler": + { + "class": "logging.FileHandler", + "formatter": "bylevel_formatter", + "filename": log_file_path, + "level": "INFO", + }, + "file_handler_debug": + { + "class": "logging.FileHandler", + "formatter": "bylevel_formatter", + "filename": debug_path, + "mode": "w", + "delay": True, + "filters": ["only_debug"], + }, + }, + "loggers": + { + None: + { + "handlers": + ["stream_handler", "file_handler", "file_handler_debug"] + if debug else ["stream_handler", "file_handler"], + "level": + "DEBUG", + } + }, + } + + config.dictConfig(LOGGING_CONFIG) + + +def get_git_info(): + """ + Get information of the current git commit + + If the package is installed with pip, read detailed version extracted by setuptools_scm. + Otherwise, use gitpython to get the information from the git repo. + """ + + import qsprpred + + path = qsprpred.__path__[0] + logging.debug(f"Package path: {path}") + is_pip_package = "site-packages" in path + + if is_pip_package: + # Version info is extracted by setuptools_scm (default format) + from .._version import __version__ + + info = __version__ + logging.info(f"Version info [from pip]: {info}") + else: + # If git repo + repo = git.Repo(search_parent_directories=True) + # Get git hash + git_hash = repo.head.object.hexsha[:8] + # Get git branch + try: + branch = repo.active_branch.name + except TypeError: + branch = "detached HEAD" + # Get git tag + tag = repo.tags[-1].name + # Get number of commits between current commit and last tag + ncommits = len(list(repo.iter_commits(f"{tag}..HEAD"))) + # Check if repo is dirty + dirty = repo.is_dirty() + info = f"({branch}) {tag}+{ncommits}[{git_hash}]+{'dirty' if dirty else ''} " + logging.info(f"Version info [from git repo]: {info}") + + +def init_logfile(log, args=None): + """ + Put some intial information in the logfile + + Args: + log : Logging instance + args (dict): Dictionary with all command line arguments + """ + logging.info(f"Initialize GBMT log file: {log.root.handlers[1].baseFilename} at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") + get_git_info() + if args: + logging.info("Command line arguments:") + for key, value in args.items(): + logging.info(f"{key}: {value}") + logging.info("") + +def enable_file_logger( + log_folder: str, + filename: str, + debug: bool = False, + log_name: str | None = None, + init_data: dict | None = None, + disable_existing_loggers: bool = False, +): + """Enable file logging. + + Args: + log_folder (str): path to the folder where the log file should be stored + filename (str): name of the log file + debug (bool): whether to enable debug logging. Defaults to False. + log_name (str, optional): name of the logger. Defaults to None. + init_data (dict, optional): initial data to be logged. Defaults to None. + disable_existing_loggers (bool): whether to disable existing loggers. + """ + # create log folder if it does not exist + path = os.path.join(log_folder, filename) + if not os.path.exists(log_folder): + os.makedirs(log_folder) + + # configure logging + config.config_logger(path, debug, disable_existing_loggers=disable_existing_loggers) + + # get logger and init configuration + log = logging.getLogger(filename) if not log_name else logging.getLogger(log_name) + log.setLevel(logging.INFO) + setLogger(log) + settings = LogFileConfig(path, log, debug) + + # Begin log file + config.init_logfile(log, json.dumps(init_data, sort_keys=False, indent=2)) + + return settings diff --git a/src/gbmtsplits/plot.py b/gbmtsplits/plot.py similarity index 100% rename from src/gbmtsplits/plot.py rename to gbmtsplits/plot.py diff --git a/src/gbmtsplits/split.py b/gbmtsplits/split.py similarity index 66% rename from src/gbmtsplits/split.py rename to gbmtsplits/split.py index 6745919..1e3ac1e 100644 --- a/src/gbmtsplits/split.py +++ b/gbmtsplits/split.py @@ -1,16 +1,15 @@ -import tqdm import numpy as np import pandas as pd from pulp import * -from typing import List, Dict, Callable -from abc import ABC, abstractmethod +from typing import Callable from rdkit import Chem, DataStructs from rdkit.Chem.rdFingerprintGenerator import GetMorganGenerator from .clustering import MaxMinClustering, LeaderPickerClustering, MurckoScaffoldClustering, RandomClustering +from .logs import logger class GloballyBalancedSplit: @@ -19,18 +18,18 @@ class GloballyBalancedSplit: Attributes ---------- - sizes : List[int], optional - List of sizes of the splits. + sizes : list[int], optional + list of sizes of the splits. clusters : dict, optional - Dictionary of clusters, where keys are cluster indices and values are indices of molecules. + dictionary of clusters, where keys are cluster indices and values are indices of molecules. clustering_method : Callable, optional Clustering method. n_splits : int, optional Number of splits. equal_weight_perc_compounds_as_tasks : bool, optional Whether to weight the tasks equally or not. - relative_gap : float, optional - Relative gap for the linear programming problem. + absolute_gap : float, optional + Absolute gap between the absolute optimal objective and the current one at which the solver stops and returns a solution. time_limit_seconds : int, optional Time limit for the linear programming problem. n_jobs : int, optional @@ -41,15 +40,17 @@ class GloballyBalancedSplit: def __init__( self, - sizes : List[int] = [0.8, 0.1, 0.1], + sizes : list[int] = [0.8, 0.1, 0.1], clusters : dict = None, clustering_method : Callable | None = MaxMinClustering(), n_splits : int = 1, equal_weight_perc_compounds_as_tasks : bool = True, - relative_gap : float = 0.1, + absolute_gap : float = 1e-3, time_limit_seconds : int = None, n_jobs : int = 1, - min_distance : bool = True, + min_distance : bool = True, + stratify : bool = True, + stratify_reg_nbins : int = 5, ) -> None: if clusters is None and clustering_method is None: @@ -62,17 +63,20 @@ def __init__( self.clustering_method = clustering_method self.n_splits = n_splits self.equal_weight_perc_compounds_as_tasks = equal_weight_perc_compounds_as_tasks - self.relative_gap = relative_gap + self.absolute_gap = absolute_gap self.time_limit_seconds = time_limit_seconds self.n_jobs = n_jobs self.min_distance = min_distance + self.stratify = stratify + self.stratify_reg_nbins = stratify_reg_nbins def __call__( self, data : pd.DataFrame, smiles_column : str = 'SMILES', - tasks : List[str] = None, - ignore_columns : List[str] = None, + tasks : list[str] = None, + ignore_columns : list[str] = None, + preassigned_smiles : dict[str, int] = None, ) -> pd.DataFrame: """ @@ -84,10 +88,10 @@ def __call__( Dataframe with SMILES strings and tasks. smiles_column : str, optional Name of the column with SMILES strings. - tasks : List[str], optional - List of task columns. - ignore_columns : List[str], optional - List of columns to ignore. + tasks : list[str], optional + list of task columns. + ignore_columns : list[str], optional + list of columns to ignore. Returns ------- @@ -107,11 +111,13 @@ def __call__( self.df = data.copy() self.smiles_column = smiles_column self.ignore_columns = ignore_columns + self.preassigned_smiles = preassigned_smiles # Save the original tasks and create the tasks for balancing self._set_original_tasks(tasks) self._set_tasks_for_balancing() + smiles_list = self.df[smiles_column].tolist() for split in range(self.n_splits): @@ -125,29 +131,34 @@ def __call__( txt = f' {split_name} ' n = int((80 - len(txt)) / 2) - print('=' * n + txt + '=' * n) - print(f'Clustering method: {self.clustering_method.get_name() if self.clustering_method else "precomputed clusters"}') - print(f'Original tasks: {self.original_tasks}') - print(f'Tasks for balancing: {self.tasks_for_balancing}') - print(f'Subset sizes: {self.sizes}') + logger.info('=' * n + txt + '=' * n) + logger.info(f'Clustering method: {self.clustering_method.get_name() if self.clustering_method else "precomputed clusters"}') + logger.info(f'Original tasks: {self.original_tasks}') + logger.info(f'Tasks for balancing: {self.tasks_for_balancing}') + logger.info(f'Subset sizes: {self.sizes}') # Cluster molecules if self.clusters : clusters = self.clusters else: - clusters = self.clustering_method(smiles_list) - print(f'Number of initial clusters: {len(clusters)}') + clusters = self.clustering_method(smiles_list, ) + logger.info(f'Number of initial clusters: {len(clusters)}') + + # Get clusters indices of pre-assigned molecules + preassigned_clusters = self._get_preassigned_clusters(clusters) if preassigned_smiles else None # Compute the number of self.dfpoints per task for each cluster tasks_per_cluster = self._compute_tasks_per_cluster(self.tasks_for_balancing, clusters) - + # Merge the clusters with a linear programming method to create the subsets merged_clusters_mapping = self._merge_clusters_with_balancing_mapping( tasks_per_cluster, self.sizes, self.equal_weight_perc_compounds_as_tasks, - self.relative_gap, + self.absolute_gap, self.time_limit_seconds if self.time_limit_seconds else self.get_default_time_limit_seconds(len(smiles_list), len(self.tasks_for_balancing)), - self.n_jobs) + self.n_jobs, + preassigned_clusters) + for i, idx in clusters.items(): self.df.loc[idx, split_name] = merged_clusters_mapping[i]-1 @@ -174,6 +185,9 @@ def __call__( cols2drop = [col for col in self.tasks_for_balancing if col not in self.original_tasks] self.df.drop(cols2drop, axis=1, inplace=True) + + logger.info('=' * 80) + return self.df def get_default_time_limit_seconds(self, nmols : int, ntasks : int) -> int: @@ -200,25 +214,25 @@ def get_default_time_limit_seconds(self, nmols : int, ntasks : int) -> int: tmin = 10 tmax = 60 * 60 tlim = min(tmax, max(tmin, tmol * ttarget)) - print(f'Time limit (s): {tlim:.0f }') + logger.info(f'Time limit: {int(tlim)}s') return tlim def _compute_tasks_per_cluster( self, - tasks : List[str], - clusters : Dict[int, List[int]] - ) -> Dict[int, List[str]]: + tasks : list[str], + clusters : dict[int, list[int]] + ) -> dict[int, list[str]]: """ Compute the number of datapoints per task for each cluster. Parameters ---------- - tasks : List[str] - List of tasks - clusters : Dict[int, List[int]] - Dictionary of clusters and list of indices of molecules in the cluster + tasks : list[str] + list of tasks + clusters : dict[int, list[int]] + dictionary of clusters and list of indices of molecules in the cluster Returns ------- @@ -236,19 +250,15 @@ def _compute_tasks_per_cluster( task_vs_clusters[i+1,j] = self.df_per_cluster[task].dropna().shape[0] return task_vs_clusters - - # Bash command to delete conda environment - # conda env remove --name myenv - - def _set_original_tasks(self, tasks : List[str] | None) -> None: + def _set_original_tasks(self, tasks : list[str] | None) -> None: """ Set the original tasks. Parameters ---------- - tasks : List[str] | None - List of task columns. + tasks : list[str] | None + list of task columns. """ if tasks is None: @@ -269,29 +279,98 @@ def _set_original_tasks(self, tasks : List[str] | None) -> None: def _set_tasks_for_balancing(self) -> None: """ - Set the tasks for balancing. If all values for a task are integers, - the task is considered a classification task, and a separate column is - created for each class. If the task is not a classification task, - the task is considered a regression task, and the task is used as is. + Set the tasks for balancing. + + If stratify is True, the tasks are used to create a column for each class (in case of classification tasks) and + bin the data (in case of regression tasks). If stratify is False, the tasks are used as is. """ + def is_convertible(value): + try: + float(value) + return True + except ValueError: + return False + self.tasks_for_balancing = [] - for task in self.original_tasks: - if all( x % 1 == 0 for x in self.df[task].dropna()): - for cls in self.df[task].dropna().unique(): - self.df[task + '_' + str(cls)] = (self.df[task] == cls).map({True: 1, False: np.nan}) - self.tasks_for_balancing.append(f'{task}_{cls}') - else: - self.tasks_for_balancing.append(task) + if self.stratify: + for task in self.original_tasks: + values = self.df[task].dropna().unique() + values_is_numerical = [is_convertible(value) for value in values] + + # Check if contains non-convertible strings + if not (all(values_is_numerical)): # Some non numerical values + + if any(values_is_numerical): # But some values are numerical + raise ValueError(f'Column {task} contains both numerical and strings values.') + else: + # Create a task per string + for string in values: + key = f'{task}_{string}' + self.df[key] = (self.df[task] == string).map({True: 1, False: np.nan}) + self.tasks_for_balancing.append(key) + logger.info(f'String classification {task} stratified into {len(self.df[task].dropna().unique())} tasks.') + # Classification: task per class + elif all( x % 1 == 0 for x in values): + for cls in values: + key = f'{task}_{int(cls)}' + self.df[key] = (self.df[task] == cls).map({True: 1, False: np.nan}) + self.tasks_for_balancing.append(key) + logger.info(f'Classification task {task} stratified into {len(self.df[task].dropna().unique())} tasks.') + # Regression: bin data and use as tasks + else: + sorted_values = np.sort(values) + bins = np.array_split(sorted_values, self.stratify_reg_nbins) + for i, bin in enumerate(bins): + key = f'{task}_{bin[0]:.2f}_{bin[-1]:.2f}' + self.df[key] = self.df[task].apply(lambda x: x if x in bin else np.nan) + self.tasks_for_balancing.append(key) + logger.info(f'Regression task {task} stratified into {self.stratify_reg_nbins} tasks.') + else: + self.tasks_for_balancing = self.original_tasks + + def _get_preassigned_clusters( + self, + clusters : dict[int, list[int]]) -> dict[int, int]: + + """ + Get the pre-assigned clusters. + + Parameters + ---------- + clusters : dict[int, list[int]] + dictionary of clusters and list of indices of molecules in the clusters. + + Returns + ------- + dict[int, int] + dictionary with cluster indices as keys and subset indices as values. + """ + + preassigned_clusters = {} + for smi, subset in self.preassigned_smiles.items(): + imol = self.df[self.df[self.smiles_column] == smi].index[0] + for idx, cluster in clusters.items(): + if imol in cluster: + if (idx in preassigned_clusters.keys()) and (subset != preassigned_clusters[idx]): + raise ValueError(f'Pre-assigned cluster {idx} is assigned to multiple subsets.') + preassigned_clusters[idx] = subset + logger.info(f'Cluster {idx} contaning {smi} is preassigned to subset {subset}.') + break + + preassigned_clusters + + return preassigned_clusters def _merge_clusters_with_balancing_mapping( self, tasks_vs_clusters_array : np.array, - sizes : List[float] = [0.9, 0.1, 0.1], + sizes : list[float] = [0.9, 0.1, 0.1], equal_weight_perc_compounds_as_tasks : bool = False, - relative_gap : float = 0, + absolute_gap : float = 1e-3, time_limit_seconds : int = 60*60, - max_N_threads : int = 1) -> List[List[int]]: + max_N_threads : int = 1, + preassigned_clusters : dict[int, int] | None = None) -> list[list[int]]: """ Linear programming function needed to balance the self.df while merging clusters. @@ -314,21 +393,23 @@ def _merge_clusters_with_balancing_mapping( equal_weight_perc_compounds_as_tasks : bool - if True, matching the % records will have the same weight as matching the % self.df of individual tasks. - if False, matching the % records will have a weight X times larger than the X tasks. - relative_gap : float - - the relative gap between the absolute optimal objective and the current one at which the solver + absolute_gap : float + - the absolute gap between the absolute optimal objective and the current one at which the solver stops and returns a solution. Can be very useful for cases where the exact solution requires far too long to be found to be of any practical use. - - set to 0 to obtain the absolute optimal solution (if reached within the time_limit_seconds) time_limit_seconds : int - the time limit in seconds for the solver (by default set to 1 hour) - after this time, whatever solution is available is returned max_N_threads : int - the maximal number of threads to be used by the solver. - it is advisable to set this number as high as allowed by the available resources. + preassigned_clusters : dict + - a dictionary of the form {cluster_index: ML_subset_index} to force the clusters to be assigned + to the ML subsets as specified by the user. Returns ------ - List (of length equal to the number of columns of tasks_vs_clusters_array) of final cluster identifiers + list (of length equal to the number of columns of tasks_vs_clusters_array) of final cluster identifiers (integers, numbered from 1 to len(sizes)), mapping each unique initial cluster to its final cluster. Example: if sizes == [20, 10, 70], the output will be a list like [3, 3, 1, 2, 1, 3...], where '1' represents the final cluster of relative size 20, '2' the one of relative size 10, and '3' the @@ -373,6 +454,12 @@ def _merge_clusters_with_balancing_mapping( # Create WML sk_harmonic = (1 / fractional_sizes) / np.sum(1 / fractional_sizes) + # Round all values to have only 3 decimals > reduce computational time + A = np.round(A, 3) + fractional_sizes = np.round(fractional_sizes, 3) + obj_weights = np.round(obj_weights, 3) + sk_harmonic = np.round(sk_harmonic, 3) + # Create the pulp model prob = LpProblem("Data_balancing", LpMinimize) @@ -401,6 +488,13 @@ def _merge_clusters_with_balancing_mapping( for c in range(N): prob += LpAffineExpression([(x[c+m*N],+1) for m in range(S)]) == 1 + # If preassigned_clusters is pro[int, int]vided, add the constraints to the model to force the clusters + # to be assigned to the ML subset preassigned_clusters[t] + if preassigned_clusters: + for c, subset in preassigned_clusters.items(): + # prob += LpAffineExpression(x[c+(subset)*N]) == 1 + prob += x[c+(subset)*N] == 1 + # Constraints related to the ABS values handling, part 1 and 2 for m in range(S): for t in range(M): @@ -409,12 +503,9 @@ def _merge_clusters_with_balancing_mapping( prob += LpAffineExpression([(x[c+m*N],A[t,c]) for c in cs]) + X[t] >= fractional_sizes[m] # Solve the model - prob.solve(PULP_CBC_CMD(gapRel = relative_gap, timeLimit = time_limit_seconds, threads = max_N_threads, msg=False)) - #solver.tmpDir = "/zfsself.df/self.df/erik/erik-rp1/pQSAR/scaffoldsplit_trial/tmp" - #prob.solve(solver) + prob.solve(PULP_CBC_CMD(gapAbs = absolute_gap, timeLimit = time_limit_seconds, threads = max_N_threads, msg=False)) # Extract the solution - list_binary_solution = [value(x[i]) for i in range(N * S)] list_initial_cluster_indices = [(list(range(N)) * S)[i] for i,l in enumerate(list_binary_solution) if l == 1] list_final_ML_subsets = [(list((1 + np.repeat(range(S), N)).astype('int64')))[i] for i,l in enumerate(list_binary_solution) if l == 1] @@ -444,7 +535,7 @@ def _compute_min_interset_Tanimoto_distances( # Print header txt = f' Min. inter-set Tanimoto distance ' n = int((80 - len(txt)) / 2) - print('-' * n + txt + '-' * n) + logger.info('-' * n + txt + '-' * n) # Compute fingerprints mols = [ Chem.MolFromSmiles(smi) for smi in self.df[self.smiles_column].tolist() ] @@ -465,11 +556,11 @@ def _compute_min_interset_Tanimoto_distances( # Print average and std of minimum distances per subset for subset in sorted(self.df[split_col].unique()): dist = self.df[self.df[split_col] == subset][mTd_col] #.to_numpy() - print(f'Subset {int(subset)}: {dist.mean():.2f} +/- {dist.std():.2f} | {dist.median():.2f}') + logger.info(f'Subset {int(subset)}: {dist.mean():.2f} +/- {dist.std():.2f} | {dist.median():.2f}') # Chemical dissimilarity score cd_score = self.df.groupby(split_col)[mTd_col].median().min() - print(f'Chemical dissimilarity score: {cd_score:.2f}') + logger.info(f'Chemical dissimilarity score: {cd_score:.2f}') def _compute_task_balace(self, split_col : str = 'Split'): @@ -485,30 +576,49 @@ def _compute_task_balace(self, split_col : str = 'Split'): # Header txt = f' {split_col} balance ' n = int((80 - len(txt)) / 2) - print('-' * n + txt + '-' * n) + logger.info('-' * n + txt + '-' * n) + + + # 1. Print out for each balancing task the fraction and number of self.df points per subset + # 2. Print out for each original task the fraction and number of self.df points per subset + # 3. Print the fraction and number of self.df point per subset for all tasks combined # Get name of longets task longest_task = max(self.tasks_for_balancing, key=len) - # Subset balance per task - for task in self.tasks_for_balancing: + # 1. Print out for each balancing task the fraction and number of self.df points per subset + if self.stratify: + for task in self.tasks_for_balancing: + txt = f'{task} balance:' + txt += ' ' * (len(longest_task) - len(task)) + '\t' + counts = self.df[[task, split_col]].groupby(split_col).count() + n = counts[task].sum() + for subset in sorted(self.df[split_col].unique()): + n_subset = counts.loc[subset, task] + txt += f' {int(subset)}: {n_subset/n:.2f} [{n_subset}]\t' + logger.info(txt) + logger.info('') + + # 2. Print out for each original task the fraction and number of self.df points per subset + for task in self.original_tasks: txt = f'{task} balance:' txt += ' ' * (len(longest_task) - len(task)) + '\t' counts = self.df[[task, split_col]].groupby(split_col).count() n = counts[task].sum() for subset in sorted(self.df[split_col].unique()): n_subset = counts.loc[subset, task] - txt += f' {int(subset)}: {n_subset/n:.2f}\t' - print(txt) - - # Overall balance + txt += f' {int(subset)}: {n_subset/n:.2f} [{n_subset}]\t' + logger.info(txt) + logger.info('') + + # 3. Print the fraction and number of self.df point per subset for all tasks combined txt = f'Overall balance:' + ' ' * (len(longest_task) - len(task)) + '\t' - counts = self.df[self.tasks_for_balancing].sum(axis=1).groupby(self.df[split_col]).sum() - n = counts.sum() + n = self.df.shape[0] balance_score = 0 - for i, subset in enumerate(sorted(self.df[split_col].unique())): - n_subset = counts.loc[subset] - txt += f' {int(subset)}: {n_subset/n:.2f}\t' + subsets = sorted(self.df[split_col].unique()) + for i, subset in enumerate(subsets): + n_subset = self.df[self.df[split_col] == subset].shape[0] + txt += f' {int(subset)}: {n_subset/n:.2f} [{n_subset}]\t' balance_score += np.abs(n_subset/n - self.sizes[i]) - print(txt) - print(f'Balance score: {balance_score/len(self.sizes):.4f}') \ No newline at end of file + logger.info(txt) + logger.info(f'Balance score: {balance_score/len(self.sizes):.4f}') \ No newline at end of file diff --git a/gbmtsplits/test_data.csv b/gbmtsplits/test_data.csv new 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+CCCOC(=O)c1c(-c2ccccc2)nc(CC)c(C(=O)SCC2COC(C)(C)O2)c1CCC,,1.0,, +CCCOC(=O)c1c(-c2ccccc2)nc(CC)c(C(=O)SCCCF)c1CCC,4.474559096928033,,,"[0,2[" +CCCOC(=O)c1c(-c2ccccc2)nc(CC)c(C(=O)SCCO)c1CCC,1.847233785607233,,1.0,"[4,6[" +CCCOC(=O)c1c(-c2ccccc2)nc(CC)c(C(=O)SCCOC2CCCCO2)c1CCC,,,0.0,"[0,2[" +CCCOC(=O)c1c(-c2ccccc2)nc(CC)c2c1CCSC2=O,8.00432242557299,1.0,,"[2,4[" +CCCOC(=O)c1c(-c2ccccc2)nc(CCOCc2ccccc2)c(C(=O)SCC)c1CCC,,,1.0,"[2,4[" +CCCOC(=O)c1c(C)nc(-c2ccccc2)c(C(=O)OCC)c1C,,,2.0,"[2,4[" +CCCOC(=O)c1c(CC)c(C(=O)SCC)c(CC)[n+](C)c1-c1ccccc1,,,1.0,"[4,6[" +CCCOC(=O)c1c(CC)nc(-c2cccc(Cl)c2)c(C(=O)OCCC)c1CC,,1.0,0.0, +CCCOC(=O)c1c(CCC)c(C(=O)SCC)c(CC)[n+](C)c1-c1ccccc1,4.346967374840957,,,"[0,2[" +CCCOC(=O)c1c(CCC)c(C(=O)SCC)c(CCOCc2ccccc2)[n+](C)c1-c1ccccc1,6.273218335914192,,, +CCCOC(=O)c1c(CCC)c(C(=O)SCCCF)c(CC)[n+](C)c1-c1ccccc1,1.0976907299822247,,0.0,"[0,2[" +CCCOC(=O)c1c(CCSC(C)=O)c(C(=O)SCC)c(CC)[n+](C)c1-c1ccccc1,5.7247959553044,,, +CCCOC(=O)c1cnc2c(cnn2CC(Cl)c2ccccc2)c1N1CCOCC1,4.66412810418669,1.0,,"[4,6[" +CCCOC(=O)c1cnc2c(cnn2CC(Cl)c2ccccc2)c1NC1CC1,,,1.0,"[4,6[" +CCCOC(=O)c1cnc2c(cnn2CC(Cl)c2ccccc2)c1NCc1ccccc1,,1.0,, +CCCOC(O)=C1C(C)=NC(c2ccccc2)=C(C(=O)OCC)C1C,7.198980347321031,,,"[4,6[" +CCCOC(O)=C1C(C)=NC(c2ccccc2)=C(C(=O)OCc2ccccc2)C1C#Cc1ccccc1,,0.0,0.0,"[4,6[" diff --git a/src/gbmtsplits/tests.py b/gbmtsplits/tests.py similarity index 66% rename from src/gbmtsplits/tests.py rename to gbmtsplits/tests.py index 6bbebc0..a26b018 100644 --- a/src/gbmtsplits/tests.py +++ b/gbmtsplits/tests.py @@ -2,49 +2,78 @@ import os import pandas as pd from unittest import TestCase +from parameterized import parameterized + +import logging from rdkit.Chem.rdFingerprintGenerator import GetMorganGenerator from .split import GloballyBalancedSplit from .clustering import RandomClustering, MaxMinClustering, LeaderPickerClustering, MurckoScaffoldClustering +preassigned_smiles = { + 'Brc1cccc(Nc2nc3c(N4CCCC4)ncnc3s2)c1' : 0, + 'C#CCn1c(=O)c2c(nc3n2CCCN3C2CCC2)n(C)c1=O' : 1, +} + +logging.basicConfig(level=logging.DEBUG) + class TestSplits(TestCase): test_data_path = os.path.join(os.path.dirname(__file__), 'test_data.csv') seed = 2022 - time_limit = None + time_limit = 10 - def test_random_split(self): + @parameterized.expand([ + ([0.9, 0.1], None,), + ([0.9, 0.1], preassigned_smiles,), + ]) + + def test_random_split(self, sizes, preassigned_smiles): data = pd.read_csv(self.test_data_path) ncols = data.shape[1] clustering = RandomClustering(seed=self.seed) splitter = GloballyBalancedSplit( - sizes = [0.9, 0.1], + sizes = sizes, clustering_method = clustering, time_limit_seconds=self.time_limit, ) - data = splitter(data) + data = splitter(data, preassigned_smiles=preassigned_smiles) assert data.shape[1] == ncols + 2 assert data.Split.nunique() == 2 - def test_dissimilarity_maxmin_split(self): + if preassigned_smiles is not None: + for smiles, subset in preassigned_smiles.items(): + assert data.loc[data['SMILES'] == smiles, 'Split'].values[0] == subset + + + @parameterized.expand([ + ([0.7, 0.1, 0.1, 0.1], None,), + ([0.7, 0.1, 0.1, 0.1], preassigned_smiles,), + ]) + + def test_dissimilarity_maxmin_split(self, sizes, preassigned_smiles): data = pd.read_csv(self.test_data_path) ncols = data.shape[1] - clustering = MaxMinClustering(seed=self.seed, n_clusters=10) + clustering = MaxMinClustering(seed=self.seed) splitter = GloballyBalancedSplit( - sizes = [0.7, 0.1, 0.1, 0.1], + sizes = sizes, clustering_method = clustering, time_limit_seconds=self.time_limit, ) - data = splitter(data) + data = splitter(data, preassigned_smiles=preassigned_smiles) assert data.shape[1] == ncols + 2 assert data.Split.nunique() == 4 + if preassigned_smiles is not None: + for smiles, subset in preassigned_smiles.items(): + assert data.loc[data['SMILES'] == smiles, 'Split'].values[0] == subset + def test_dissimilarity_leader_split(self): data = pd.read_csv(self.test_data_path) @@ -84,7 +113,8 @@ def test_multiple_random_splits(self): splitter = GloballyBalancedSplit( clustering_method = clustering, time_limit_seconds=self.time_limit, - n_splits=3 + n_splits=3, + absolute_gap = 1e-3, ) data = splitter(data) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..f22d9ad --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,38 @@ +[build-system] +requires = ["setuptools>=61.0", "setuptools_scm[toml]>=6.2"] +build-backend = "setuptools.build_meta" + +[project] +name = "gbmtsplits" +dynamic = ["version"] +description = "A tool to create well-balanced data splits for multi-task learning" +readme = { file = "README.md", content-type = "text/markdown" } +authors = [{name = "Sohvi Luukkonen", email = "s.luukkonen@lacdr.leidenuniv.nl"},] +license = { file = "LICENSE" } +keywords = ["data split", "multi-task", "balanced data"] +classifiers = [ + "Development Status :: 5 - Production/Stable", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.7", +] + +dependencies = [ + "parameterized", + "matplotlib", + "seaborn", + "pandas", + "scikit-learn", + "rdkit", + "numpy", + "tqdm", + "pulp" +] + +[tool.setuptools.packages.find] +where = ["."] + +[tool.setuptools_scm] +write_to = "gbmtsplits/_version.py" \ No newline at end of file diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index a85478d..0000000 --- a/setup.cfg +++ /dev/null @@ -1,53 +0,0 @@ -[metadata] -name = gbmt-splits -version = 1.0.0 -description = A tool to create well-balanced data splits for multi-task learning -long_description = file: README.md -long_description_content_type = text/markdown - -url = https://https://github.com/sohviluukkonen/gbmt-splits - -author = Sohvi Luukkonen -author_email = sohvi.luukkonen@hotmail.com -maintainer = Sohvi Luukkonen -maintainer_email = sohvi.luukkonen@hotmail.com - -license = MIT -license_file = LICENSE - -classifiers = - Development Status :: 5 - Production/Stable - License :: OSI Approved :: MIT License - Programming Language :: Python :: 3.10 - Programming Language :: Python :: 3.9 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.7 - -keywords = - data split - multi-task - -[options] -include_package_data = True -packages = find: -package_dir = - = src -install_requires = - scikit-learn - matplotlib - seaborn - pandas - rdkit - numpy - tqdm - pulp - -[options.packages.find] -where = src - -[options.package_data] -* = *.csv - -[options.entry_points] -console_scripts = - gbmtsplits = gbmtsplits.cli:main diff --git a/setup.py b/setup.py deleted file mode 100644 index 0af9463..0000000 --- a/setup.py +++ /dev/null @@ -1,8 +0,0 @@ -# -*- coding: utf-8 -*- - -"""Setup module.""" - -import setuptools - -if __name__ == "__main__": - setuptools.setup() diff --git a/src/__init__.py b/src/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/gbmtsplits/__init__.py b/src/gbmtsplits/__init__.py deleted file mode 100644 index d897190..0000000 --- a/src/gbmtsplits/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .split import GloballyBalancedSplit -from .clustering import * - - -__version__ = '0.2.0' \ No newline at end of file diff --git a/src/gbmtsplits/test_data.csv b/src/gbmtsplits/test_data.csv deleted file mode 100644 index f847055..0000000 --- a/src/gbmtsplits/test_data.csv +++ /dev/null @@ -1,876 +0,0 @@ -SMILES,Contineous,Binary,Categorical -Brc1cccc(Nc2nc3c(N4CCCC4)ncnc3s2)c1,8.989961070623387,0.0,2.0 -C#CC1(O)CCC2C3CCc4cc(O)ccc4C3CCC21C,1.7509884393007646,1.0,2.0 -C#CCCC(=O)Nc1nc2nn(C)cc2c2nc(-c3ccco3)nn12,7.413304406400709,1.0, -C#CCCCC#Cc1nc(NCc2cccc(Cl)c2)c2ncn(C3C(O)C(O)C4(C(=O)NC)CC34)c2n1,,,1.0 -C#CCCCC(=O)Nc1nc2cccc(Cl)c2c2nc(-c3ccco3)nn12,,1.0, -C#CCCCC(=O)Nc1nc2nn(C)cc2c2nc(-c3ccco3)nn12,2.199151482008772,,1.0 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-C=C1C(C)C2C(Cc3ccccc3)NC(=O)C23C(OC(C)=O)C=CC(C)(O)CC(C)CC=CC3C1O,1.5047261485929797,, -C=CCCCCCCCCCCOCCOCCOCCOCCOc1ccc(-c2nc3c(=O)n(C)c(=O)n(C)c3[nH]2)cc1,,1.0, -C=CCCCCCCCCCOCCOCCOCCOCCn1cnc2c1c(=O)n(C)c(=O)n2C,1.5357473710381453,1.0, -C=CCCOc1ccc(CCCn2ncc3c2nc(N)n2nc(-c4ccco4)nc32)cc1,,0.0,2.0 -C=CCCn1c(=O)c2[nH]cnc2n(CCC)c1=O,,1.0,0.0 -C=CCCn1c(=O)c2c(nc3cc(OC)ccn32)n(Cc2ccccc2)c1=O,3.8629805703697873,,1.0 -C=CCCn1c(Br)nc2c(N)ncnc21,0.2034553180695986,,1.0 -C=CCCn1cnc2c(N)ncnc21,,0.0,0.0 -C=CCN1C(=O)N2CC(C)N=C2C2=NC(c3cc(C)nn3C)N=C21,,0.0,0.0 -C=CCN1C(=O)N2CC(C)N=C2C2=NC(c3cc(OC)nn3C)N=C21,7.330832984679464,0.0, -C=CCN1C(=O)N2CC(C)N=C2C2=NC(c3cc(OC)no3)N=C21,2.3784049956114894,0.0,2.0 -C=CCN1C(=O)N2CC(C)N=C2C2=NC(c3cc(OCc4ccccc4)nn3C)N=C21,7.619103784353412,0.0, -C=CCN1C(=O)N2CC(C)N=C2c2nc(-c3cc(C)n(C)n3)[nH]c21,,1.0,2.0 -C=CCN1C(=O)N2CC(C)N=C2c2nc(-c3cc(C)nn3C)[nH]c21,9.4392328551265,,2.0 -C=CCN1C(=O)N2CC(C)N=C2c2nc(-c3cc(OC)nn3C)[nH]c21,,,0.0 -C=CCN1C(=O)N2CC(C)N=C2c2nc(-c3cc(OC)no3)[nH]c21,6.162948378423357,, -C=CCN1C(=O)N2CC(C)N=C2c2nc(-c3cc(OCc4ccccc4)nn3C)[nH]c21,3.2090148806983,, -C=CCN1C(=O)N2CC(CC)N=C2C2=NC(c3cc(C)n(C)n3)N=C21,,,2.0 -C=CCN1C(=O)N2CC(CC)N=C2C2=NC(c3cc(C)nn3C)N=C21,,0.0,0.0 -C=CCN1C(=O)N2CC(CC)N=C2C2=NC(c3cc(OC)nn3C)N=C21,5.075071792390785,0.0,0.0 -C=CCN1C(=O)N2CC(CC)N=C2C2=NC(c3cc(OC)no3)N=C21,9.363821733020384,,1.0 -C=CCN1C(=O)N2CC(CC)N=C2c2nc(-c3cc(C)nn3C)[nH]c21,1.2304178207064287,,2.0 -C=CCN1C(=O)N2CC(CC)N=C2c2nc(-c3cc(OC)nn3C)[nH]c21,,0.0,2.0 -C=CCN1C(=O)N2CC(CC)N=C2c2nc(-c3cc(OC)no3)[nH]c21,,1.0, -C=CCN1C(=O)N2CC(CC)N=C2c2nc(-c3cc(OCc4ccccc4)nn3C)[nH]c21,,1.0,0.0 -C=CCN1CCC(NC(=O)Nc2nc3nn(C)cc3c3nc(-c4ccco4)nn23)CC1,,0.0, -C=CCOC(O)=C1C(C)=NC(c2ccccc2)=C(C(=O)OCC)C1C=Cc1ccc([N+](=O)[O-])cc1,2.891945039594379,0.0,1.0 -C=CCOc1ccc(-c2c(C#N)c(N)nc(SCC(N)=O)c2C#N)cc1,0.4777895075841754,0.0, -C=CCOc1ccc(-c2c(C#N)c(N)nc(SCc3cc[nH]n3)c2C#N)cc1,8.887924115312323,,1.0 -C=CCOc1ccc(CCCn2ncc3c2nc(N)n2nc(-c4ccco4)nc32)cc1,4.101932488681879,,0.0 -C=CCOc1cccc(-c2nc(O)c3c4ccccc4c(=O)n(CC)n23)c1,1.8831975945740764,, -C=CCn1c(=O)[nH]c2[nH]cnc2c1=O,0.7929368506032264,,1.0 -C=CCn1c(=O)[nH]c2nc3cc(NC(C)=O)ccc3nc2c1=O,,1.0, -C=CCn1c(=O)c2[nH]c(-c3ccccc3)nc2n(C)c1=O,,1.0, -C=CCn1c(=O)c2c([nH]c3nc(C)cn32)n(Cc2ccccc2)c1=O,,1.0,1.0 -C=CCn1c(=O)c2c(nc(-c3ccccc3)n2C)n(C)c1=O,8.065804915139132,0.0,1.0 -C=CCn1c(=O)c2c(nc(-c3ccccc3)n2CCCl)n(C)c1=O,8.287037295021594,1.0,0.0 -C=CCn1c(=O)c2c(nc3cc(OC)ccn32)n(Cc2ccccc2)c1=O,,0.0, -C=CCn1c(=O)c2c(nc3ccccn32)n(Cc2ccccc2)c1=O,2.03119557601842,,0.0 -C=CCn1c(=O)c2c(ncn2C)n(CC=C)c1=O,,0.0,0.0 -C=CCn1c(=O)c2nc(-c3cc(OCC(=O)N4CCN(c5ccccc5)CC4)nn3C)[nH]c2n(CC=C)c1=O,,,1.0 -C=CCn1c(=O)c2nc(-c3ccc(OCC(=O)O)cc3)[nH]c2n(CC=C)c1=O,7.595450605595877,, -C=CCn1c(=O)c2nc[nH]c2n(CC=C)c1=O,5.847855907728232,1.0,2.0 -C=CCn1c(Br)nc2c(N)ncnc21,2.662811994671986,0.0, -C=CCn1cc2c(nc(NC(=O)Nc3ccc(OC)cc3)n3nc(-c4ccco4)nc23)n1,1.556360135491841,, -C=CCn1cnc2c1c(=O)n(C)c(=O)n2C,2.7436069750589898,,2.0 -C=Cn1cc2c(nc(NC(=O)Nc3ccc(OC)cc3)n3nc(-c4ccco4)nc23)n1,8.707559886440006,0.0,2.0 -C=Cn1ncc2c1nc(N)n1c(=O)n(CC3CC3)nc21,2.4586531509385425,1.0,0.0 -C=Cn1ncc2c1nc(N)n1c(=O)n(Cc3cccc(Cl)c3)nc21,2.9769037263077323,,1.0 -C=Cn1ncc2c1nc(N)n1c(=O)n(Cc3ccccc3)nc21,5.205668244499135,,2.0 -C=S(O)(O)=C1C(=O)N(C)c2ccc(OCC(=O)NCc3ccccc3)cc2C1=CCc1ccccc1,,0.0, -CC#CCn1cc(-c2nc3c(=O)n(CCC)c(=O)n(CCC)c3[nH]2)cn1,0.3722936001756416,, -CC(=O)C1(O)CCC2C3CCC4=CC(=O)CCC4(C)C3CCC21C,,0.0,2.0 -CC(=O)CCCCn1c(=O)c2c(ncn2C)n(C)c1=O,3.159949939842331,1.0,0.0 -CC(=O)CCc1nc(N)c2nc(-n3nccn3)n(C)c2n1,,1.0,2.0 -CC(=O)CNc1nc(-c2ccccc2)nc2nc(C(C)C)cn12,6.23184942135415,0.0, -CC(=O)CNc1nc(-c2ccccc2)nc2nc(C)cn12,5.328408512451793,, -CC(=O)COc1ccc(N2CCN(CCn3ncc4c3nc(N)n3nc(-c5ccco5)nc43)CC2)cc1,1.4302641379358605,, -CC(=O)Cn1c(=O)c2c([nH]c3nc(C)cn32)n(Cc2ccccc2)c1=O,,,1.0 -CC(=O)Cn1c(=O)c2c(ncn2C)n(C)c1=O,7.55875713181603,,1.0 -CC(=O)N(C(C)=O)c1ccc(CNC(=O)c2cnc3nc(C)ccc3c2O)cc1,,0.0,2.0 -CC(=O)N1CCC(Nc2nc(-c3ccco3)c(C(=O)c3ccccc3)s2)CC1,1.5083262177009105,0.0,1.0 -CC(=O)N1CCN(CC(=O)Nc2cc(-n3nc(C)cc3C)nc(-c3ccc(C)o3)n2)CC1,,0.0, -CC(=O)N1CCN(Cc2cccc3c2-c2nc(N)nc(-c4ccccc4)c2C3=O)CC1,4.901930472702343,, -CC(=O)NCC1C(CO)OC(n2cnc3c(NCc4cccc(I)c4)ncnc32)C1O,5.165355568150703,1.0, -CC(=O)NCCNC(=O)C12CC1C(n1cnc3c(NC(C4CC4)C4CC4)nc(Cl)nc31)C(O)C2O,6.205867051698712,1.0,2.0 -CC(=O)NCCNc1nc(-c2ccccc2)nc2[nH]c(C(=O)N3CCN(CCCc4ccccc4)CC3)cc12,,1.0, -CC(=O)NCCNc1nc(-c2ccccc2)nc2[nH]c(C(=O)N3CCN(CCCc4ccccc4)CC3)nc12,,1.0,0.0 -CC(=O)NCCNc1nc(-c2ccccc2)nc2[nH]c(C)c(C)c12,5.720423193636492,, -CC(=O)NCCNc1nc(-c2ccco2)c(C(=O)c2ccccc2)s1,2.7964923217728286,, -CC(=O)NCCn1c(NC(=O)c2cccc(C#N)c2)nc2cc(C(=O)N3CCCCC3)cnc21,,,2.0 -CC(=O)NCCn1cc(CCCCC(=O)NCCNC(=O)COc2ccc(CCCn3ncc4c3nc(N)n3nc(-c5ccco5)nc43)cc2)nn1,3.5131628495514033,0.0, -CC(=O)Nc1cc(-c2cc(C)cc(C)c2)nc(-n2nc(C)cc2C)n1,,0.0, -CC(=O)Nc1cc(-c2cc(C)ccc2C)nc(-n2nc(C)cc2C)n1,,1.0,0.0 -CC(=O)Nc1cc(-c2cc(F)cc(F)c2)nc(-n2nc(C)cc2C)n1,,0.0, -CC(=O)Nc1cc(-c2cc(F)cc(OC3CCN(C)C3)c2)nc(-n2nc(C)cc2C)n1,,1.0,2.0 -CC(=O)Nc1cc(-c2cc(N3CCOCC3)ccn2)nc(-n2nc(C)cc2C)n1,4.855480534544697,0.0,1.0 -CC(=O)Nc1cc(-c2cc(O)cc(F)c2)nc(-n2nc(C)cc2C)n1,,,2.0 -CC(=O)Nc1cc(-c2ccc(C(C)=O)cc2)nc(-c2ccc(C(C)=O)cc2)n1,0.8836580427642671,0.0, -CC(=O)Nc1cc(-c2ccc(C(F)(F)F)cc2)nc(-c2ccc(C(F)(F)F)cc2)n1,6.167688094512565,1.0,2.0 -CC(=O)Nc1cc(-c2ccc(C)cc2)cc(-c2ccc(C)cc2)n1,,0.0, -CC(=O)Nc1cc(-c2ccc(C)cc2)nc(-c2ccc(C)cc2)n1,7.907565992061859,0.0, -CC(=O)Nc1cc(-c2ccc(F)cc2)nc(-c2ccc(F)cc2)n1,,0.0, -CC(=O)Nc1cc(-c2ccc(OC(F)(F)F)cc2)nc(-c2ccc(OC(F)(F)F)cc2)n1,9.760684547465072,1.0,2.0 -CC(=O)Nc1cc(-c2ccc3c(c2)OCO3)cc(-c2ccc3c(c2)OCO3)n1,,,1.0 -CC(=O)Nc1cc(-c2ccc3c(c2)OCO3)nc(-c2ccc3c(c2)OCO3)n1,9.50031732864039,, -CC(=O)Nc1cc(-c2cccc(C#N)c2)nc(-n2nc(C)cc2C)n1,,1.0, -CC(=O)Nc1cc(-c2cccc(C(F)(F)F)c2)nc(-n2nc(C)cc2C)n1,,0.0,0.0 -CC(=O)Nc1cc(-c2cccc(C)c2)nc(-n2nc(C)cc2C)n1,7.669086554452403,1.0, -CC(=O)Nc1cc(-c2cccc(CC#N)c2)nc(-n2nc(C)cc2C)n1,6.325752711998836,,2.0 -CC(=O)Nc1cc(-c2cccc(N(C)C)n2)nc(-n2nc(C)cc2C)n1,,,0.0 -CC(=O)Nc1cc(-c2cccc(N)n2)nc(-n2nc(C)cc2C)n1,8.858450077602324,, -CC(=O)Nc1cc(-c2cccc(N3CCCC3)n2)nc(-n2nc(C)cc2C)n1,8.879794417323803,,0.0 -CC(=O)Nc1cc(-c2cccc(N3CCCOCC3)n2)nc(-n2nc(C)cc2C)n1,8.907085919186436,,0.0 -CC(=O)Nc1cc(-c2cccc(N3CCN(C)CC3)n2)nc(-n2nc(C)cc2C)n1,0.4805304606924987,0.0,1.0 -CC(=O)Nc1cc(-c2cccc(N3CCOCC3)n2)nc(-n2nc(C)cc2C)n1,1.0728861304763115,0.0,2.0 -CC(=O)Nc1cc(-c2cccc(O)c2)nc(-n2nc(C)cc2C)n1,1.6510214984584037,1.0, -CC(=O)Nc1cc(-c2cccc(OC3CCCN(C)C3)c2)nc(-n2nc(C)cc2C)n1,3.8021620075375386,,2.0 -CC(=O)Nc1cc(-c2cccc(OC3CCN(C)CC3)c2)nc(-n2nc(C)cc2C)n1,1.446313251421495,1.0,0.0 -CC(=O)Nc1cc(-c2cccc(OCCN(C)C)c2)nc(-n2nc(C)cc2C)n1,8.00351766498242,1.0, -CC(=O)Nc1cc(-c2ccccc2)cc(-c2ccccc2)n1,,,2.0 -CC(=O)Nc1cc(-c2ccccc2)nc(-n2nc(C)cc2C)n1,4.961480272452704,, -CC(=O)Nc1cc(-c2ccccc2F)nc(-c2ccccc2F)n1,4.135871700295874,1.0, -CC(=O)Nc1cc(-c2ccccc2O)nc(-n2nc(C)cc2C)n1,9.89658368759737,0.0,0.0 -CC(=O)Nc1cc(-c2cccnc2)nc(-n2nc(C)cc2C)n1,,1.0, -CC(=O)Nc1cc(-c2ccco2)nc(-c2ccco2)n1,0.30377104518994336,1.0, -CC(=O)Nc1cc(-c2ccnc(N3CCOCC3)c2)nc(-n2nc(C)cc2C)n1,,,1.0 -CC(=O)Nc1cc(-c2ccncc2)nc(-n2nc(C)cc2C)n1,,1.0,0.0 -CC(=O)Nc1cc(-c2ccoc2)nc(-c2ccoc2)n1,7.361105329307865,1.0,0.0 -CC(=O)Nc1cc(-c2cncc(N3CCOCC3)c2)nc(-n2nc(C)cc2C)n1,,0.0, -CC(=O)Nc1cc(-c2cncc(O)c2)nc(-n2nc(C)cc2C)n1,8.875173923940574,,2.0 -CC(=O)Nc1cc(-n2cccn2)nc(-c2ccco2)n1,0.488116872219615,,1.0 -CC(=O)Nc1cc(C2C=CC=CC2=O)nc(-n2nc(C)cc2C)n1,,1.0,0.0 -CC(=O)Nc1cc(C=Cc2ccccc2)nc(C=Cc2ccccc2)n1,8.21116315861656,,2.0 -CC(=O)Nc1cc(N2CCCC2)nc(-n2nc(C)cc2C)n1,0.17121588705152124,0.0,2.0 -CC(=O)Nc1cc(N2CCCC2=O)nc(-n2nc(C)cc2C)n1,0.4758037733076581,0.0,2.0 -CC(=O)Nc1cc(N2CCCC2C)nc(-n2nc(C)cc2C)n1,,0.0,1.0 -CC(=O)Nc1cc(N2CCCC2CO)nc(-n2nc(C)cc2C)n1,,1.0,0.0 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-CC(C)(C)CC(=O)Nc1ccc(CNc2nccs2)cc1,,,2.0 -CC(C)(C)CCC(c1ccc(C(=O)NCc2nn[nH]n2)cc1)N1C(=O)C(c2cc(Cl)cc(Cl)c2)=NC12CCC(C(C)(C)C)CC2,,0.0,0.0 -CC(C)(C)CNC(=O)c1ccc(C(=O)Nc2nccs2)cc1,0.021413415183093587,, -CC(C)(C)Cn1cc2c(C(N)=O)nc(N)nc2n1,,1.0, -CC(C)(C)Cn1cc2c(Cl)nc(N(C(=O)c3ccccc3)C(=O)c3ccccc3)nc2n1,,0.0,2.0 -CC(C)(C)Cn1cc2c(Cl)nc(N)nc2n1,7.85887685689045,, -CC(C)(C)Cn1cc2c(Cl)nc(NC(=O)Cc3ccccc3)nc2n1,,0.0, -CC(C)(C)Cn1cc2c(Cl)nc(NC(=O)c3ccc(C(F)(F)F)cc3)nc2n1,9.563450987346465,,0.0 -CC(C)(C)Cn1cc2c(Cl)nc(NC(=O)c3ccc(F)cc3)nc2n1,2.830350750312064,1.0,2.0 -CC(C)(C)NC(=O)COc1ccc2nc(O)c(-c3nccs3)c(CCc3ccccc3)c2c1,6.490840604340634,, -CC(C)(C)NC(=O)Nc1nc2nn(CCCc3ccccc3)cc2c2nc(-c3ccco3)nn12,,1.0, -CC(C)(C)NC(=O)Nc1nc2nn(CCc3ccccc3)cc2c2nc(-c3ccco3)nn12,3.7696393360889435,, -CC(C)(C)NS(=O)(=O)c1ccc(-c2sc(C(=O)NC3CC(C(=O)O)C3)nc2CC2CCCCC2)c2ccccc12,7.846878004500968,1.0, -CC(C)(C)OC(=O)C1CCCN1CC(O)C(Cc1ccccc1)NC(=O)C(CC(N)=O)NC(=O)OCc1ccccc1,,,2.0 -CC(C)(C)OC(=O)N1CCC(Nc2nc(N)n3nc(-c4ccco4)nc3n2)CC1,,,0.0 -CC(C)(C)OC(=O)N1CCN(CCn2ncc3c2nc(N)n2c(=O)n(Cc4cccc(Cl)c4)nc32)CC1,,,1.0 -CC(C)(C)OC(=O)N1CCN(CCn2ncc3c2nc(N)n2c(=O)n(Cc4ccccc4)nc32)CC1,4.159534468237202,0.0, -CC(C)(C)OC(=O)N1CCN(c2cccc(-c3cc4nc(-c5ccco5)nn4c(N)n3)c2)CC1,,,1.0 -CC(C)(C)OC(=O)N1CCN(c2nc(N)n3nc(-c4ccco4)nc3n2)CC1,,,1.0 -CC(C)(C)OC(=O)NCC1CCC(CNc2nc(N)n3nc(-c4ccco4)nc3n2)CC1,2.9099791714811287,,2.0 -CC(C)(C)OC(=O)NCCC(=O)Nc1nc2ccc(Cl)cc2c2nc(-c3ccco3)nn12,5.954771899036427,0.0,1.0 -CC(C)(C)OC(=O)NCCCC(=O)Nc1nc2ccc(Cl)cc2c2nc(-c3ccco3)nn12,3.014740294317778,, -CC(C)(C)OC(=O)NCCCCC(=O)Nc1nc2ccc(Cl)cc2c2nc(-c3ccco3)nn12,2.0584892756604534,, -CC(C)(C)OC(=O)NCCCCCC(=O)Nc1nc2ccc(Cl)cc2c2nc(-c3ccco3)nn12,8.269791444312034,0.0,2.0 -CC(C)(C)OC(=O)NCCCCCCC(=O)Nc1nc2ccc(Cl)cc2c2nc(-c3ccco3)nn12,,1.0,1.0 -CC(C)(C)OC(=O)NCCCNc1nc(N)n2nc(-c3ccco3)nc2n1,,0.0,1.0 -CC(C)(C)OC(=O)NCCNc1nc(N)n2nc(-c3ccco3)nc2n1,,1.0, -CC(C)(C)OC(=O)NCCNc1ncc(C(=O)O)c2nc(-c3ccco3)nn12,0.042071894195983006,1.0, -CC(C)(C)OC(=O)NCCOCCOCCNc1nc(N)n2nc(-c3ccco3)nc2n1,,,0.0 -CC(C)(C)OC(=O)Nc1nc2ccc(Cl)cc2c2nc(-c3ccco3)nn12,1.7182973511934096,, -CC(C)(C)c1ccc(C(=O)CCCN2CCC(OC(c3ccccc3)c3ccccc3)CC2)cc1,,,0.0 -CC(C)(C)c1ccn2c(c1)nc1c2c(=O)[nH]c(=O)n1Cc1ccccc1,,0.0,0.0 -CC(C)(C)c1nc(N2CCN(CCCCN3C(=O)CCC(=O)c4ccccc43)CC2)cc(C(F)(F)F)n1,7.90116723500229,1.0, -CC(C)(C)c1nc2c(-c3ccccc3)cc(-c3ccccc3)nc2[nH]1,3.759507103023293,1.0,1.0 -CC(C)(C)n1cc2c(nc(NC(=O)Nc3cccc(Cl)c3)n3nc(-c4ccco4)nc23)n1,,1.0,0.0 -CC(C)(NC(=O)COc1ccc2nc(O)c(-c3ccccn3)c(CCc3ccccc3)c2c1)c1ccccc1,2.7156477769953846,1.0,1.0 -CC(C)(NC(=O)COc1ccc2nc(O)c(-c3cccnc3)c(CCc3ccccc3)c2c1)c1ccccc1,5.000212439219618,1.0,2.0 -CC(C)(NC(=O)COc1ccc2nc(O)c(-c3ccncc3)c(CCc3ccccc3)c2c1)c1ccccc1,,0.0, -CC(C)(NC(=O)COc1ccc2nc(O)c(-c3nccs3)c(CCc3ccc(F)cc3)c2c1)c1ccccc1,8.685328136835428,1.0, -CC(C)(NC(=O)COc1ccc2nc(O)c(-c3nccs3)c(CCc3ccccc3)c2c1)c1ccccc1,5.760639110640277,,1.0 -CC(C)(NC(=O)COc1ccc2nc(O)c(-c3nccs3)c(COc3ccccc3)c2c1)c1ccccc1,,0.0, -CC(C)(NC(=O)NCc1ccc2nc(O)c(-c3nccs3)c(CCc3ccccc3)c2c1)c1ccccc1,,,2.0 -CC(C)(O)CC(=O)Nc1ccc(C(=O)Nc2nccs2)cc1,0.2834038497477409,0.0,2.0 -CC(C)(O)c1ccccc1CCC(SCC1(CC(=O)O)CC1)c1cccc(C=Cc2ccc3ccc(Cl)cc3n2)c1,,0.0,0.0 -CC(C)(Oc1ccc(Cl)cc1)C(=O)Nc1n[nH]c(-c2ccccc2)n1,5.826389671347133,,0.0 -CC(C)C(=O)NC1CC(n2cnc3c(NCC(c4ccccc4)c4ccccc4)nc(NCCc4cn(C(C)C)cn4)nc32)C(O)C1O,,1.0,0.0 -CC(C)C(=O)Nc1cc(-c2ccccc2)nc(-c2ccccc2)n1,,0.0, -CC(C)C(=O)Nc1ccc(C(=O)Nc2nccs2)cc1,,,2.0 -CC(C)C(=O)Nc1cnc(-c2ccncc2)c(-c2ccco2)n1,1.5158871618234804,0.0, -CC(C)C(=O)Nc1nc(-c2ccccc2)c(C#N)c(-c2ccccc2)n1,4.6189991744539824,, -CC(C)C(NC(=O)C(Cc1ccc([N+](=O)[O-])cc1)NC(=O)C1C=C2c3cccc4[nH]cc(c34)CC2N(C)C1)C(=O)N1c2ccccc2CC1C(N)=O,,1.0,1.0 -CC(C)C(NC(=O)C1C=C2c3cccc4[nH]cc(c34)CC2N(C)C1)C(=O)NC(CCC1CCCCC1)C(=O)N1CCCC(C(N)=O)C1,6.068666063932591,0.0,0.0 -CC(C)C(NC(=O)C1C=C2c3cccc4[nH]cc(c34)CC2N(C)C1)C(=O)NC(CCC1CCCCC1)C(=O)N1c2ccccc2CC1C(N)=O,,1.0,0.0 -CC(C)C(Nc1nc(Cl)nc2c1ncn2C1C(O)C(O)C2(CO)CC12)C1CC1,7.978267272651096,1.0,0.0 -CC(C)C(Nc1nc(Cl)nc2c1ncn2C1C(O)C(O)C2CC21)C1CC1,,0.0,1.0 -CC(C)CC(=O)Nc1cc(-c2ccccc2)nc(-c2ccccc2)n1,,,1.0 -CC(C)CC(=O)Nc1ccc(C(=O)Nc2nccs2)cc1,6.113977245185611,, -CC(C)CC(=O)Nc1nc(-c2ccccc2)c(-c2ncon2)s1,5.529345294221429,, -CC(C)CC(CO)Nc1nc(SC(C)c2ccccc2)nc2nc(N)sc12,0.5906997295867467,,2.0 -CC(C)CCNC(=O)C1SC(n2cnc3c(NCc4cccc(I)c4)nc(Cl)nc32)C(O)C1O,,1.0, -CC(C)CCNc1ccc(C(=O)Nc2nccs2)cc1,0.44044578809822266,,1.0 -CC(C)CCc1c(-c2nccs2)c(O)nc2ccc(OCC(=O)NC(C)(C)c3ccccc3)cc12,8.190287315109318,0.0, -CC(C)CCn1c(=O)c2nc(-c3ccc(S(=O)(=O)O)cc3)[nH]c2n(CC(C)C)c1=O,,0.0, -CC(C)CCn1c(=O)c2nc(-c3ccccc3)[nH]c2n(CC(C)C)c1=O,,1.0,2.0 -CC(C)CCn1c(=O)c2nc[nH]c2n(CC(C)C)c1=O,,,0.0 -CC(C)CCn1cc2c(nc(N)n3nc(-c4ccco4)nc23)n1,8.990130505829462,0.0, -CC(C)CCn1cc2c(nc(NC(=O)C(c3ccccc3)c3ccccc3)n3nc(-c4ccco4)nc23)n1,7.044272600482234,, -CC(C)CCn1cc2c(nc(NC(=O)CCCNC(=O)OC(C)(C)C)n3nc(-c4ccco4)nc23)n1,,1.0, -CC(C)CCn1cc2c(nc(NC(=O)COc3ccc(Cl)cc3)n3nc(-c4ccco4)nc23)n1,6.134610491087345,,0.0 -CC(C)CCn1cc2c(nc(NC(=O)COc3ccccc3)n3nc(-c4ccco4)nc23)n1,3.2038436659219913,1.0, -CC(C)CCn1cc2c(nc(NC(=O)Cc3ccc(-c4ccccc4)cc3)n3nc(-c4ccco4)nc23)n1,2.2015597039217227,, -CC(C)CCn1cc2c(nc(NC(=O)Cc3ccc(C(F)(F)F)cc3)n3nc(-c4ccco4)nc23)n1,,1.0, -CC(C)CCn1cc2c(nc(NC(=O)Cc3ccc(Cl)cc3)n3nc(-c4ccco4)nc23)n1,1.61506541727237,,1.0 -CC(C)CCn1cc2c(nc(NC(=O)Cc3ccc(F)cc3)n3nc(-c4ccco4)nc23)n1,,0.0, -CC(C)CCn1cc2c(nc(NC(=O)Cc3ccc4ccccc4c3)n3nc(-c4ccco4)nc23)n1,7.152182726049584,1.0,2.0 -CC(C)CCn1cc2c(nc(NC(=O)Cc3cccc(Cl)c3)n3nc(-c4ccco4)nc23)n1,,1.0,0.0 -CC(C)CCn1cc2c(nc(NC(=O)Cc3cccc4ccccc34)n3nc(-c4ccco4)nc23)n1,,0.0, -CC(C)CCn1cc2c(nc(NC(=O)Cc3ccccc3)n3nc(-c4ccco4)nc23)n1,,1.0,2.0 -CC(C)CCn1cc2c(nc(NC(=O)NC(C)(C)C)n3nc(-c4ccco4)nc23)n1,3.414875974720016,,0.0 -CC(C)CCn1cc2c(nc(NC(=O)NC(C)C)n3nc(-c4ccco4)nc23)n1,8.392456669476534,, -CC(C)CCn1cc2c(nc(NC(=O)NC3CCCCC3)n3nc(-c4ccco4)nc23)n1,6.769997239370706,1.0,1.0 -CC(C)CN1C(C)Cc2cc(O)ccc2C1c1ccc(C=CC(=O)O)cc1,6.708405596288365,,2.0 -CC(C)CNc1nc(-c2ccco2)c(C(=O)c2ccccc2)s1,,0.0,0.0 -CC(C)COc1ccc(-c2c(C#N)c(N)nc(SCC(N)=O)c2C#N)cc1,5.742095780972173,,0.0 -CC(C)Cn1c(=O)[nH]c2[nH]c(-c3cnn(Cc4cc(-c5ccccc5)on4)c3)nc2c1=O,,1.0,1.0 -CC(C)Cn1c(=O)[nH]c2[nH]c(-c3cnn(Cc4cccc(C(F)(F)F)c4)c3)nc2c1=O,,1.0,2.0 -CC(C)Cn1c(=O)[nH]c2[nH]c(-c3cnn(Cc4cccc(F)c4)c3)nc2c1=O,,0.0,0.0 -CC(C)Cn1c(=O)[nH]c2[nH]c(-c3cnn(Cc4ccccc4)c3)nc2c1=O,3.702399513166056,0.0, -CC(C)Cn1c(=O)[nH]c2[nH]c(-c3cnn(Cc4noc(-c5ccc(C(F)(F)F)cc5)n4)c3)nc2c1=O,5.5174095991594605,1.0,0.0 -CC(C)Cn1c(=O)c2c([nH]c3nc(-c4ccccc4)cn32)n(CC(C)C)c1=O,,0.0, -CC(C)Cn1c(=O)c2c(nc3ccccn32)n(Cc2ccccc2)c1=O,5.945295285951532,,2.0 -CC(C)Cn1c(=O)c2nc(-c3cc(OCC(=O)Nc4ccc(Br)cc4)nn3C)[nH]c2n(CC(C)C)c1=O,,0.0, -CC(C)Cn1c(=O)c2nc(-c3cc(OCC(=O)Nc4ccc(F)cc4)nn3C)[nH]c2n(CC(C)C)c1=O,,,1.0 -CC(C)Cn1c(=O)c2nc(-c3cn[nH]c3)[nH]c2n(C)c1=O,,,0.0 -CC(C)Cn1c(=O)c2nc(-c3cnn(Cc4cccc(C(F)(F)F)c4)c3)[nH]c2n(C)c1=O,,0.0,2.0 -CC(C)Cn1c(=O)c2nc(-c3cnn(Cc4cccc(C(F)(F)F)c4)c3)[nH]c2n(CC(C)C)c1=O,8.4076793249246,,2.0 -CC(C)Cn1c(=O)c2nc(-c3cnn(Cc4cccc(F)c4)c3)[nH]c2n(C)c1=O,,,2.0 -CC(C)Cn1c(=O)c2nc(-c3cnn(Cc4cccc(F)c4)c3)[nH]c2n(CC(C)C)c1=O,,1.0, -CC(C)Cn1c(=O)c2nc(-c3cnn(Cc4ccccc4)c3)[nH]c2n(C)c1=O,2.817090956247995,, -CC(C)Cn1c(=O)c2nc(-c3cnn(Cc4ccccc4)c3)[nH]c2n(CC(C)C)c1=O,,,2.0 -CC(C)Cn1c(=O)c2nc[nH]c2n(CC(C)C)c1=O,0.35150938936610365,0.0,0.0 -CC(C)Cn1c(=O)n(C)c(=O)c2[nH]cnc21,,1.0,2.0 -CC(C)Cn1c2ccc(Nc3ncccn3)cc2c2c3c(c4c(c21)CCc1nn(C)cc1-4)C(=O)NC3,,1.0, -CC(C)Cn1cnc2c(N)nc3ccccc3c21,5.490786757914404,, -CC(C)Cn1cnc2c(N)ncnc21,,,1.0 -CC(C)Cn1cnc2ncnc(N)c21,,0.0,2.0 -CC(C)N(C)C(=O)c1cccc(Cn2nnc3c(-c4ccco4)nc(N)nc32)c1,,0.0,2.0 -CC(C)N(C)c1nc2c(NC3CC4CC3CC4O)ncnc2n1C,3.4097695261526018,0.0,0.0 -CC(C)N(CCOc1ccc2c(c1)-c1nc(N)nc(-c3ccccc3)c1C2=O)C(C)C,,,0.0 -CC(C)N1CCC(NC(=O)c2ccc3c(c2)-c2nc(N)nc(-c4ccccc4)c2C3=O)CC1,3.15990369690458,0.0, -CC(C)N1CCN(C(=O)c2ccc3c(c2)-c2nc(N)nc(-c4ccccc4)c2C3=O)CC1,8.674694171025125,, -CC(C)N=C(Nc1nc2nn(C)cc2c2nc(-c3ccco3)nn12)NC(C)C,7.951322525742299,,1.0 -CC(C)NC(=O)N1CCC(NC(=O)Nc2nc3nn(C)cc3c3nc(-c4ccco4)nn23)CC1,,0.0, -CC(C)NC(=O)Nc1nc2nn(CCCc3ccccc3)cc2c2nc(-c3ccco3)nn12,1.4103875888136042,0.0, -CC(C)NC(=O)Nc1nc2nn(CCc3ccccc3)cc2c2nc(-c3ccco3)nn12,,1.0,1.0 -CC(C)NC(=O)c1cccc(Cn2nnc3c(-c4ccco4)nc(N)nc32)c1,4.857742438375984,0.0,2.0 -CC(C)NCC(O)COc1ccc(CC(N)=O)cc1,0.9685357181339838,0.0, -CC(C)NS(=O)(=O)c1ccc(-c2nc3c(=O)n(C)c(=O)n(C)c3[nH]2)cc1,,,0.0 -CC(C)Nc1nc(Cl)nc2c1ncn2C12CC(O)C(O)C1C2,5.753759871424323,,1.0 -CC(C)Nc1nc(Cl)nc2c1ncn2C1C(O)C(O)C2CC21,3.2393900778854965,1.0, -CC(C)Nc1nc(N)n2nc(-c3ccco3)nc2n1,3.94154427040501,1.0, -CC(C)Nc1nc(NC(=O)Cc2ccccc2)n2nc(-c3ccco3)nc2n1,,,2.0 -CC(C)Nc1nc(NC(=O)c2ccccc2)n2nc(-c3ccco3)nc2n1,7.57214247641095,,2.0 -CC(C)Nc1nc(NC2CCCCC2)n2nc(-c3ccco3)nc2n1,7.638226390317397,, -CC(C)Nc1nc(NCc2ccccc2)n2nc(-c3ccco3)nc2n1,,,2.0 -CC(C)Nc1ncc(C(=O)O)c2nc(-c3ccco3)nn12,,,1.0 -CC(C)OC(=O)C(C)(C)Oc1ccc(C(=O)c2ccc(Cl)cc2)cc1,3.553998388944466,1.0,0.0 -CC(C)OC(=O)c1cc2c3ccccc3nc(O)n2n1,,1.0,0.0 -CC(C)OC(=O)c1cnc2c(cnn2CC(Cl)c2ccccc2)c1N1CCCC1,,1.0,1.0 -CC(C)OC(=O)c1cnc2c(cnn2CC(Cl)c2ccccc2)c1N1CCOCC1,0.33120057899587607,, -CC(C)OC(=O)c1cnc2c(cnn2CC(Cl)c2ccccc2)c1NC1CC1,2.7665969022515213,0.0, -CC(C)OC(=O)c1cnc2c(cnn2CC(Cl)c2ccccc2)c1NCCc1ccc(Cl)cc1,,1.0, -CC(C)OC(=O)c1cnc2c(cnn2CC(Cl)c2ccccc2)c1NCCc1cccc(Cl)c1,6.727563166551497,0.0,1.0 -CC(C)OC(=O)c1cnc2c(cnn2CC(Cl)c2ccccc2)c1NCCc1ccccc1,,,0.0 -CC(C)OC(=O)c1cnc2c(cnn2CC(Cl)c2ccccc2)c1NCCc1ccccc1Cl,1.2931848787421796,,1.0 -CC(C)OC(=O)c1cnc2c(cnn2CC(Cl)c2ccccc2)c1NCc1ccccc1,,1.0,2.0 -CC(C)OC(=O)c1nc2c(O)nc3ccccc3n2n1,6.116949030672574,,1.0 -CC(C)Oc1ccc(-c2cn3c(=O)n(-c4ccccc4)nc3c(N)n2)cc1,,,0.0 -CC(C)Oc1ccc(C(=O)Nc2nc(-c3ccccn3)cs2)cc1,,0.0, -CC(C)Oc1ccc(CCCn2ncc3c2nc(N)n2nc(-c4ccco4)nc32)cc1,3.8155210423888275,,1.0 -CC(C)Sc1nc(-n2cccn2)nc(N)c1Br,,,1.0 -CC(C)c1c[nH]c(C(O)O)c1CC(=O)O,3.194271087517433,1.0,2.0 -CC(C)c1cc(Cn2nnc3c(-c4ccco4)nc(N)nc32)ccc1N,,1.0, -CC(C)c1ccc(Cn2cnc3c(-c4ccco4)nc(N)nc32)cc1,0.5898940383599305,, -CC(C)c1ccc2c(c1)-c1nc(N)nc(-c3ccc(Br)o3)c1C2,4.9292325309161376,1.0,2.0 -CC(C)c1cn2c(NC(=O)C3CCCC3)nc(-c3ccccc3)nc2n1,7.868205972206903,, -CC(C)c1nc(N)c2nc(-n3nccn3)n(C)c2n1,4.14471672286655,1.0, -CC(C)c1nc(N)nc(-c2ccccc2O)n1,0.44007493104140094,,2.0 -CC(C)c1nc2c(-c3ccccc3)cc(-c3ccccc3)nc2[nH]1,2.9077070771613656,,0.0 -CC(C)c1nc2c(=O)n(Cc3ccccc3)nc(-c3ccncc3)c2c2cc(-c3ccccc3)nn12,,1.0,2.0 -CC(C)c1noc(OCC(=O)NCc2ccc3c(c2)OCO3)n1,,0.0,0.0 -CC(C)n1c(=O)n(C)c(=O)c2[nH]cnc21,,0.0, -CC(C)n1c(Br)nc2c(N)ncnc21,3.906446625931678,, -CC(C)n1cc(-c2ncc(N)nc2-c2ccc(F)cc2)ccc1=O,5.499569750444751,0.0,1.0 -CC(C)n1cc2c(C(N)=O)nc(N)nc2n1,,1.0, -CC(C)n1cc2c(Cl)nc(N(C(=O)c3ccccc3)C(=O)c3ccccc3)nc2n1,,1.0, -CC(C)n1cc2c(Cl)nc(N)nc2n1,,,0.0 -CC(C)n1cc2c(Cl)nc(NC(=O)Cc3ccccc3)nc2n1,9.381693825316017,1.0, -CC(C)n1cc2c(Cl)nc(NC(=O)c3ccc(C(F)(F)F)cc3)nc2n1,,,1.0 -CC(C)n1cc2c(Cl)nc(NC(=O)c3ccc(F)cc3)nc2n1,,,1.0 -CC(C)n1cc2c(Cl)nc(NC(=O)c3ccccc3)nc2n1,0.8126387239893296,, -CC(C)n1cnc(CCNc2nc(NCC(c3ccccc3)c3ccccc3)c3ncn(C4CC(NC(=O)C(C)(C)C)C(O)C4O)c3n2)c1,8.94570356653636,0.0, -CC(C)n1cnc(CCNc2nc(NCC(c3ccccc3)c3ccccc3)c3ncn(C4CC(NC(=O)C5CC5)C(O)C4O)c3n2)c1,4.912114667239592,1.0, -CC(C)n1cnc(CCNc2nc(NCC(c3ccccc3)c3ccccc3)c3ncn(C4CC(NC(=O)C5CCC5)C(O)C4O)c3n2)c1,7.860393358595439,,0.0 -CC(C)n1cnc(CCNc2nc(NCC(c3ccccc3)c3ccccc3)c3ncn(C4CC(NC(=O)CCc5ccccc5)C(O)C4O)c3n2)c1,9.101561036653678,,1.0 -CC(C)n1cnc(CCNc2nc(NCC(c3ccccc3)c3ccccc3)c3ncn(C4CC(NC(=O)Cc5ccccc5)C(O)C4O)c3n2)c1,,0.0,1.0 -CC(C)n1cnc2c(N)ncnc21,3.2789994096768007,0.0, -CC(C)n1cnc2ncnc(N)c21,,0.0, -CC(C=CC1=C(C)CCCC1(C)C)=CC=CC(C)=CC(=O)O,5.523454708442809,0.0,0.0 -CC(CCN1CCOCC1)CC(=O)Nc1ccc(C(=O)Nc2nccs2)cc1,5.964443933746214,1.0,2.0 -CC(NC(=O)C(N)Cc1ccc(O)cc1)C(=O)NCC(=O)N(C)C(Cc1ccccc1)C(=O)NCCO,,1.0,1.0 -CC(NC(=O)COc1ccc(-c2cc3c([nH]2)c(=O)n(C)c(=O)n3C)cc1)c1ccccc1,9.367369186739294,1.0, -CC(NC(=O)COc1ccc2nc(O)c(-c3nccs3)c(CCc3ccccc3)c2c1)c1ccccc1,3.4085255307370224,,1.0 -CC(NC(=O)OC(C)(C)C)C(=O)Nc1nc2ccc(Cl)cc2c2nc(-c3ccco3)nn12,,0.0, -CC(NC(=O)c1nc(N)nc2c(F)cccc12)c1cccc2cccnc12,9.7686957015302,,0.0 -CC(NC(=O)n1cnc2c(-c3ccco3)nc(N)nc21)c1ccccc1,6.001591928253525,,2.0 -CC(Nc1nc(Cl)nc2c1ncn2C12CC(O)C(O)C1C2)C1CC1,,1.0, -CC(Nc1nc(Cl)nc2c1ncn2C1C(O)C(O)C2CC21)C1CC1,,0.0, -CC(Nc1nc2nn(C)cc2c2nc(-c3ccco3)nn12)c1ccccc1,,1.0,2.0 -CC(O)(C#Cc1cn2nc(-c3ccco3)nc2c(N)n1)Cc1ccncc1,,0.0,0.0 -CC(O)CCc1nc(N)c2nc(-n3nccn3)n(C)c2n1,,,0.0 -CC(O)CNc1nc2ccccc2n2nc(-c3ccco3)nc12,,0.0,1.0 -CC(O)CNc1nc2nn(C)cc2c2nc(-c3ccco3)nn12,,0.0, -CC(O)Cn1c(Br)nc2c(N)ncnc21,,1.0, -CC(O)Cn1cnc2c(N)ncnc21,,1.0, -CC(OC(=O)c1nccnc1N)C(=O)NCc1ccc2c(c1)OCO2,4.106868799848068,0.0,2.0 -CC(Oc1ccc(-c2cc3c([nH]2)c(=O)n(C)c(=O)n3C)cc1)C(=O)N1CCN(c2ccccc2)CC1,0.12393807116388489,, -CC(Oc1ccc(-c2cc3c([nH]2)c(=O)n(C)c(=O)n3C)cc1)C(=O)Nc1ccc(Br)cc1,4.956266795330114,0.0,2.0 -CC(Oc1ccc(-c2cc3c([nH]2)c(=O)n(C)c(=O)n3C)cc1)C(=O)Nc1ccc(F)cc1,0.03916409136272003,0.0, -CC(Oc1ccc(-c2cc3c([nH]2)c(=O)n(C)c(=O)n3C)cc1)C(=O)Nc1ccccc1,,,0.0 -CC(Oc1ccc(Cl)cc1)C(=O)Nc1n[nH]c(-c2ccccc2)n1,4.011464123567015,0.0, -CC(Oc1cccc(Cl)c1)C(=O)Nc1n[nH]c(-c2ccccc2)n1,2.3226712609201017,, -CC(Oc1ccccc1F)C(=O)Nc1n[nH]c(-c2ccccc2)n1,1.8162860667085134,,0.0 -CC(Sc1ccccc1)C(=O)Nc1n[nH]c(-c2ccccc2)n1,7.056021531722674,1.0,0.0 -CC(Sc1nc(N)c(C#N)c(-c2ccc(OCCO)cc2)c1C#N)c1cccc(C(N)=O)c1,,1.0,2.0 -CC(Sc1nc(N)c(C(=O)Nc2ccccc2)cc1C#N)C(=O)NCc1ccccc1Cl,3.336348537503422,1.0, -CC(c1ccc2cc(CCn3ncc4c3nc(N)n3nc(-c5ccco5)nc43)ccc2n1)N1CCOCC1,5.893660052610439,,2.0 -CC(c1ccccc1)n1c2ccccc2c2c(N)nc(-c3ccccc3)nc21,,,2.0 -CC(c1ccccc1)n1c2ccccc2c2c(N)nc(-c3ccncc3)nc21,,0.0, -CC(c1ccccc1)n1cnc2c(-c3ccco3)nc(N)nc21,9.043448792468848,1.0,0.0 -CC1(C)C(C=CC=CC=C2N(CCCS(=O)(=O)O)c3ccc(S(=O)(=O)O)cc3C2(C)CCCCCC(=O)NCCCCNC(=O)COc2ccc(CCCn3ncc4c3nc(N)n3nc(-c5ccco5)nc43)cc2)=[N+](CCCS(=O)(=O)O)c2ccc(S(=O)(=O)O)cc21,,1.0,0.0 -CC1(C)CCCN(c2nnc(N)nc2-c2ccccc2)C1,,1.0,2.0 -CC1(COc2ccc(CC3SC(=O)NC3=O)cc2)CCCCC1,9.777023798728555,,2.0 -CC12CCC3c4ccc(OC(=O)c5ccccc5)cc4CCC3C1CCC2O,4.302559292870292,, -CC12Cc3cn[nH]c3CC1CCC1C2CCC2(C)C1CCC2(C)O,8.316501999095632,, -CC1=C(C(=O)Nc2nc3ccccc3s2)C(c2ccco2)C(C#N)=C(SCC(N)=O)N1,2.11904420676462,,2.0 -CC1=C(C(=O)O)C(C#Cc2ccccc2)C(C(=O)O)=C(c2ccccc2)N1,,,0.0 -CC1=C(C(=O)OC(C)C)C(c2ccco2)NC(=O)N1,,0.0,2.0 -CC1=C(C(=O)OC(C)C)C(c2ccco2)NC(=O)N1C,,1.0,2.0 -CC1=C(C(=O)OC(C)C)C(c2ccco2)NC(NC#N)=N1,,0.0,0.0 -CC1=C(C(=O)OC(C)C)C(c2ccoc2)NC(=O)N1,7.892029599154055,0.0,2.0 -CC1=C(C(=O)OC(C)C)C(c2ccoc2)NC(NC#N)=N1,6.702579292615315,1.0,1.0 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-CC1C(=O)c2nc(CN3N=C(Cl)CCC3=O)[nH]c2N(C)C1=O,0.6959543602982399,1.0,2.0 -CC1C(=O)c2nc(CN3N=C(c4ccccc4)CCC3=O)[nH]c2N(C)C1=O,,,0.0 -CC1CC(c2ccccc2)n2nc(N3C(=O)C4CC=CCC4C3=O)nc2N1,,0.0,2.0 -CC1CCC(C)N1Cc1ccc2c(c1)-c1nc(N)nc(-c3ccccc3)c1C2=O,0.05047998843228707,, -CC1CCC(C)N1Cc1cccc2c1-c1nc(N)nc(-c3ccccc3)c1C2=O,3.596987229361621,, -CC1CCC(N2CCCn3c2nc2c3c(=O)n(C)c(=O)n2C)CC1,6.297737919122945,1.0, -CC1CCC(Nc2nc(-c3ccccc3)nc3[nH]ccc23)CC1,,1.0, -CC1CCCCN1C(=O)c1cc(N)c2nc(-c3ccc(Br)o3)nn2c1,0.9719726599785716,,1.0 -CC1CCCCN1C(=O)c1cc(N)n2nc(-c3ccc(Br)o3)nc2c1,9.253571459917522,, -CC1CCCN(C(=O)c2cc(N)n3nc(-c4ccc(Br)o4)nc3c2)C1,,1.0,1.0 -CC1CCCN1C(=O)c1cc(N)c2nc(-c3ccc(Br)o3)nn2c1,4.413501120202414,1.0,0.0 -CC1CCCN1C(=O)c1cc(N)n2nc(-c3ccc(Br)o3)nc2c1,9.463661962617193,,2.0 -CC1CCN(C(=O)c2ccc3c(O)nc4c(-c5ccccc5)nnn4c3c2)CC1,9.198365097314365,0.0, -CC1CN(Cc2ccccc2)C(N)=N1,8.91857842603273,1.0, -CC1CN(c2nnc(N)nc2-c2ccccc2)CC(C)O1,,1.0, 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-CCC(=O)NC1CC(n2cnc3c(NCC(c4ccccc4)c4ccccc4)nc(NCCc4cn(C)cn4)nc32)C(O)C1O,6.060888681073758,0.0, -CCC(=O)NC1CC(n2cnc3c(NCC(c4ccccc4)c4ccccc4)nc(NCCc4cn(CC(=O)O)cn4)nc32)C(O)C1O,8.641657211694577,0.0,2.0 -CCC(=O)Nc1cc(-c2ccc(C(C)=O)cc2)nc(-c2ccc(C(C)=O)cc2)n1,9.3859193471833,, -CCC(=O)Nc1cc(-c2ccc(C)cc2)nc(-c2ccc(C)cc2)n1,2.8419309252848435,,1.0 -CCC(=O)Nc1cc(-c2ccc(Cl)cc2)nc(-c2ccc(Cl)cc2)n1,,1.0,1.0 -CCC(=O)Nc1cc(-c2ccc(F)cc2)nc(-c2ccc(F)cc2)n1,3.347439726040343,, -CCC(=O)Nc1cc(-c2ccc(OC)cc2)nc(-c2ccc(OC)cc2)n1,,1.0,1.0 -CCC(=O)Nc1cc(-c2ccc(OC)cc2OC)nc(-c2ccc(OC)cc2OC)n1,1.7420196369750185,,0.0 -CCC(=O)Nc1cc(-c2ccc(SC)cc2)nc(-c2ccc(SC)cc2)n1,,0.0,0.0 -CCC(=O)Nc1cc(-c2ccc3c(c2)OCO3)nc(-c2ccc3c(c2)OCO3)n1,5.879411261822235,, -CCC(=O)Nc1cc(-c2ccccc2)nc(-c2ccccc2)n1,7.288442577166039,, -CCC(=O)Nc1cc(-c2ccccc2F)nc(-c2ccccc2F)n1,,0.0, -CCC(=O)Nc1cc(-c2ccccc2OC)nc(-c2ccccc2OC)n1,,1.0, -CCC(=O)Nc1cc(-c2ccco2)nc(-c2ccco2)n1,,0.0,2.0 -CCC(=O)Nc1cc(-c2cncc(OC)c2)nc(-n2nc(C)cc2C)n1,,0.0, -CCC(=O)Nc1cc(C=Cc2ccccc2)nc(C=Cc2ccccc2)n1,3.967918789170036,1.0,0.0 -CCC(=O)Nc1cc(N2CCCC2COC)nc(-n2nc(C)cc2C)n1,7.575239211263815,1.0,1.0 -CCC(=O)Nc1ccc(C(=O)Nc2nccs2)cc1,0.5659795728942663,, -CCC(=O)Nc1cnc(-c2ccncc2)c(-c2ccco2)n1,,0.0,0.0 -CCC(=O)Nc1n[nH]c(-c2ccccc2)n1,,0.0,2.0 -CCC(=O)Nc1nc(-c2ccc(C)cc2)cc(-c2ccc(C)cc2)n1,9.43424164448922,,2.0 -CCC(=O)Nc1nc(-c2ccc(SC)cc2)cc(-c2ccc(SC)cc2)n1,4.239411315398649,, -CCC(=O)Nc1nc(-c2ccc3c(c2)OCO3)cc(-c2ccc3c(c2)OCO3)n1,,1.0, -CCC(=O)Nc1nc(-c2ccccc2)cc(-c2ccccc2)n1,7.898821956196955,1.0, -CCC(=O)Nc1nc(-c2ccccc2F)cc(-c2ccccc2F)n1,2.9297750659565844,1.0, -CCC(=O)Nc1nc(C=Cc2ccccc2)cc(C=Cc2ccccc2)n1,0.6747094673719956,1.0,1.0 -CCC(=O)Nc1nc2ccc(Cl)cc2c2nc(-c3ccco3)nn12,6.528017053219006,,1.0 -CCC(=O)Nc1nc2ccccc2n2c(=O)n(-c3ccccc3)nc12,0.0042171805353119485,,0.0 -CCC(C)C(=O)Nc1cc(-c2ccccc2)nc(-c2ccccc2)n1,,1.0,0.0 -CCC(C)C(=O)OC1CC(C)C=C2C=CC(C)C(CCC3CC(O)CC(=O)O3)C21,4.075572785551138,, -CCC(C)Nc1nc2nn(C)cc2c2nc(-c3ccco3)nn12,5.183138166209035,1.0, -CCC(C)OC(=O)c1cnc2c(cnn2CC(Cl)c2ccccc2)c1NCCc1ccccc1,0.2531744125300417,0.0,1.0 -CCC(C)Oc1cc(NC(C)=O)nc(SCc2ccccc2)n1,6.71701735851391,0.0,2.0 -CCC(C)Oc1ccc(CCCn2ncc3c2nc(N)n2nc(-c4ccco4)nc32)cc1,7.848518086199487,1.0, -CCC(C)n1c(Br)nc2c(N)ncnc21,1.5611976349772538,1.0,1.0 -CCC(C)n1cnc2ncnc(N)c21,1.0319474647216287,1.0, -CCC(CC)C(=O)Nc1cc(-c2ccc(OC)cc2)nc(-c2ccc(OC)cc2)n1,3.4411126635843994,,0.0 -CCC(CC)C(=O)Nc1cc(-c2ccc(OC)cc2OC)nc(-c2ccc(OC)cc2OC)n1,,1.0,2.0 -CCC(CC)C(=O)Nc1cc(-c2ccc3c(c2)OCO3)nc(-c2ccccc2)n1,0.6920026339134577,0.0, -CCC(CC)C(=O)Nc1cc(-c2ccccc2)nc(-c2ccccc2)n1,4.141066732473791,1.0, -CCC(CC)C(=O)Nc1nc(-c2ccccc2)c(C#N)c(-c2ccccc2)n1,6.743278630627927,0.0,2.0 -CCC(CC)C(=O)Nc1nc(-c2ccccc2)cc(-c2ccc3c(c2)OCO3)n1,1.222211267703135,,1.0 -CCC(CC)C(=O)Nc1nc(-c2ccccc2)cc(-c2ccccc2)n1,9.588931779019024,0.0, -CCC(CC)C(=O)Nc1nc(-c2ccccc2)nc(-c2ccc3c(c2)OCO3)c1C#N,,,0.0 -CCC(CC)CNc1nc(NCCc2cn(CC)cn2)nc2c1ncn2C1CC(n2cc(CO)cn2)C(O)C1O,6.399438118572447,, -CCC(CC)Nc1nc(Cl)nc2c1ncn2C12CC(O)C(O)C1C2,,0.0,1.0 -CCC(CC)Nc1nc(Cl)nc2c1ncn2C1C(O)C(O)C2(CO)CC12,,1.0, -CCC(CC)c1nc2c(-c3ccccc3)cc(-c3ccccc3)nc2[nH]1,5.6184631025469365,, -CCC(CO)Nc1nc(-c2ccccc2)nc2c1c(C)c(C)n2C1CCCC1,,0.0, -CCC(CO)Nc1nc2nn(C)cc2c2nc(-c3ccco3)nn12,7.400209193900044,, -CCC(NC(=O)COc1ccc2nc(O)c(-c3nccs3)c(CCc3ccccc3)c2c1)c1ccccc1,,0.0, -CCC(Nc1nc(Cl)nc2c1ncn2C1C(O)C(O)C2(CO)CC12)C1CC1,5.7970418975545455,1.0,1.0 -CCC(Nc1nc(Cl)nc2c1ncn2C1C(O)C(O)C2CC21)C1CC1,2.185587144753355,1.0, -CCC(O)(C#Cc1nc(N)c2nc(-c3cccc(F)c3)n(C)c2n1)CC,,1.0, -CCC(O)Cc1nc(N)c2nc(-n3nccn3)n(C)c2n1,,1.0,0.0 -CCC(Oc1ccccc1)C(=O)Nc1n[nH]c(-c2ccccc2)n1,,,0.0 -CCC1(O)CCC(C)(C(=O)Nc2nc3c(OC)ccc(N4CCOCC4)c3s2)CC1,8.101884639641801,1.0,0.0 -CCC1CN2C(=O)N(C)c3[nH]c(-c4cc(Cl)cc(Cl)c4Cl)nc3C2=N1,,,2.0 -CCC1CN2C(=O)N(C)c3[nH]c(-c4ccccc4)nc3C2=N1,7.555626686587278,0.0,1.0 -CCC1CN2C(=O)N(C)c3[nH]c(C=Cc4ccccc4)nc3C2=N1,8.66947360152562,,2.0 -CCC1CN2C(=O)N(C)c3nc(C=Cc4ccccc4)n(C)c3C2=N1,,1.0, -CCC1CN2C(=O)N(Cc3ccccc3)C3=NC(c4cc(C)n(C)n4)N=C3C2=N1,1.019227434029305,0.0,0.0 -CCC1CN2C(=O)N(Cc3ccccc3)C3=NC(c4cc(OC)no4)N=C3C2=N1,,,2.0 -CCC1CN2C(=O)N(Cc3ccccc3)c3[nH]c(-c4cc(C)n(C)n4)nc3C2=N1,,,1.0 -CCC1CN2C(=O)N(Cc3ccccc3)c3[nH]c(-c4cc(C)nn4C)nc3C2=N1,,1.0,2.0 -CCC1CN2C(=O)N(Cc3ccccc3)c3[nH]c(-c4cc(OC)nn4C)nc3C2=N1,3.781608360387201,0.0, -CCC1CN2C(=O)N(Cc3ccccc3)c3[nH]c(-c4cc(OC)no4)nc3C2=N1,9.29615374777908,0.0,0.0 -CCC1CN2C(=O)N(Cc3ccccc3)c3[nH]c(-c4cc(OCc5ccccc5)nn4C)nc3C2=N1,4.743190104906293,,1.0 -CCCC(=O)Nc1cc(-c2ccc(Cl)cc2)nc(-c2ccc(Cl)cc2)n1,7.005162586438073,,2.0 -CCCC(=O)Nc1cc(-c2ccc(F)cc2)nc(-c2ccc(F)cc2)n1,,1.0,2.0 -CCCC(=O)Nc1cc(-c2ccc(OC)cc2OC)nc(-c2ccc(OC)cc2OC)n1,8.760498108856027,0.0,2.0 -CCCC(=O)Nc1cc(-c2ccc3c(c2)OCO3)nc(-c2ccc3c(c2)OCO3)n1,,0.0,2.0 -CCCC(=O)Nc1cc(-c2ccc3c(c2)OCO3)nc(-c2ccccc2)n1,2.504032086862961,1.0,0.0 -CCCC(=O)Nc1cc(-c2ccccc2)nc(-c2ccccc2)n1,0.9950777884334894,1.0, -CCCC(=O)Nc1cc(-c2ccccc2F)nc(-c2ccccc2F)n1,,,0.0 -CCCC(=O)Nc1cc(-c2ccccc2OC)nc(-c2ccccc2OC)n1,9.202326693282815,, -CCCC(=O)Nc1cc(-c2ccoc2)nc(-c2ccoc2)n1,,,1.0 -CCCC(=O)Nc1ccc(C(=O)Nc2nccs2)cc1,,0.0, -CCCC(=O)Nc1n[nH]c(-c2ccccc2)n1,5.823673360326467,1.0,0.0 -CCCC(=O)Nc1nc(-c2ccc(C)cc2)cc(-c2ccc(C)cc2)n1,1.356610211112711,, -CCCC(=O)Nc1nc(-c2ccc3c(c2)OCO3)cc(-c2ccc3c(c2)OCO3)n1,,,0.0 -CCCC(=O)Nc1nc(-c2ccccc2)c(C#N)c(-c2ccccc2)n1,,1.0,0.0 -CCCC(=O)Nc1nc(-c2ccccc2)cc(-c2ccc3c(c2)OCO3)n1,,0.0,1.0 -CCCC(=O)Nc1nc(-c2ccccc2)cc(-c2ccccc2)n1,2.5115730635925626,1.0,2.0 -CCCC(=O)Nc1nc(-c2ccccc2)nc(-c2ccccc2)c1C#N,9.589643003145708,, -CCCC(=O)Nc1nc(-c2ccccc2F)cc(-c2ccccc2F)n1,,1.0, -CCCC(=O)Nc1ncc(-c2cccc(Cl)c2)c(-c2ccccc2)n1,4.24734205003347,,1.0 -CCCC(=O)Oc1ccc(CCNc2nc(N)c3nc(-n4nccn4)n(C)c3n2)cc1,,0.0,2.0 -CCCC(=O)Oc1ccc(CCNc2nc(N)n3nc(-c4ccco4)nc3n2)cc1,9.86322210525857,0.0, -CCCC(=O)c1nc(N)c2nc(-n3nccn3)n(C)c2n1,,,2.0 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