MolPAL: Molecular Pool-based Active Learning

Efficient Exploration of Virtual Chemical
Libraries through Active Learning

Overview

This repository contains the source of MolPAL, a software for the accelerated discovery of compounds in high-throughput virtual screening environments, as originally detailed in the paper Accelerating high-throughput virtual screening through molecular pool-based active learning. The original code used in that paper lives at the publication tag. This repository also contains the updated code used in the paper "Self-focusing virtual screening with active design space pruning," the code for which lives at the dsp-pub tag. To reproduce results from either publication, please see the Reproducing Experimental Results section

Table of Contents

• Overview
• Table of Contents
• Requirements
• Installation
• Running MolPAL
  • Setting up a ray cluster
  • Preprocessing
  • Configuration files
  • Examples
  • Required Settings
  • Optional Settings
• Reproducing Experimental Results
• Object Model
• Future Directions
• Citation

Requirements

• Python (>= 3.8)

if utilizing GPU-accelerated model training and inference

• CUDA (>= 10.2)

if utilizing distributed GPU-accelerated model training and inference

• CUDA (>= 11.1)

if performing docking online

• the appropriate requirements as listed in the pyscreener README

Installation

The general steps in installing MolPAL are:

1. cloning the repo: git clone [email protected]:coleygroup/molpal.git
2. installing the dependencies (see below)
3. installing the repo: pip install -e . (note that this is typically done after dependencies are installed)

The easiest way to install all dependencies is to use conda along with the supplied environment.yml file, but you may also install them manually, if desired. All libraries listed in that file are required before using MolPAL

The following packages are optional to install before running MolPAL:

• cudatoolkit: whichever version matches your CUDA build if utilizing GPU acceleration for PyTorch-based models (MPN)
• map4 and tmap: if utilizing the map4 fingerprint
• optuna: if planning to perform hyperparameter optimization
• matplotlib: to generate plots from the publication
• seaborn: to generate plots from the publication

setup via conda

NOTE: the environment.yml must be edited to reflect your machine's setup. To do this, uncomment out the appropriate line depending on your CUDA version or if you lack a GPU entirely. If you need a lower CUDA version than those specified in the environment YAML file, comment out the PyTorch line as well and go to the pytorch wesbite to set the channels and versions of both the pytorch and cudatoolkit packages properly.

0. (if necessary) install conda
1. cd /path/to/molpal
2. conda env create -f environment.yml

Before running MolPAL, be sure to first activate the environment: conda activate molpal

Running MolPAL

Setting up a ray cluster

MolPAL parallelizes objective function calculation and model inference (training coming later) using the ray library. MolPAL will automatically start a ray cluster if none exists, but this is highly limiting because it can't leverage distributed resources nor will it accurately reflect allocated resources (i.e, it will think you have access to all N cores on a cluster node, regardless of your allocation.)

Ex.: To specify a local ray cluster with all the resources on your machine, type: ray start --head

Ex.: To restrict the ray cluster to using only N CPUs and M GPUs, type: ray start --head --num-cpus N --num-gpus M

To properly leverage multi-node allocations, you must set up a ray cluster manually before running MolPAL. The documentation has several examples of how to set up a ray cluster, and the only thing specific to MolPAL is the reliance on two environment variables: redis_password and ip_head. MolPAL will use the values of these environment variables to connect to the proper ray cluster. An example of this may be seen in the SLURM submission script run_molpal.batch

GPU usage

MolPAL will automatically use a GPU if it detects one in the ray cluster. If this is undesired, you can specify --num-gpus 0 when starting ray and running export CUDA_VISIBLE_DEVICES='' before starting MolPAL

Preprocessing

For models expecting vectors as inputs (e.g., random forest and feed-forward neural network models,) molecular fingerprints must be calculated first. Given that the set of fingerprints used for inference is the same each time, it makes sense to cache these fingerprints, and that's exactly what the base MoleculePool (also referred to as an EagerMoleculePool) does. However, the complete set of fingerprints for most libraries would be too large to cache entirely in memory on most systems, so we instead store them on disk in an HDF5 file that is transparently prepared for the user during MolPAL startup (if not already provided with the --fps option.)

If you wish to prepare this file ahead of time, you can use scripts/fingerprints.py to do just this. While this process can be parallelized over an infinitely large ray cluster (see above,) we found this was I/O limited above 12 cores, which takes about 4 hours to prepare an HDF5 file of 100M fingerprints. Note: if MolPAL prepares the file for you, it prints a message saying where the file was written to (usually under the $TMP directory) and whether there were invalid SMILES. To reuse this fingerprints file, simply move this file to a persistent directory after MolPAL has completed its run. Additionally, it will tell you which lines in your library file were invalid. You should use this value for the --invalid-idxs argument to further speed up MolPAL startup.

Ex.: To prepare the fingerprints file corresopnding to the sample command below, issue the following command:

python scripts/fingerprints.py --library libraries/Enamine50k.csv.gz --fingerprint pair --length 2048 --radius 2 --name libraries/fps_enamine50k

The resulting fingerprint file will be located in your current working directory as libraries/fps_enamine50k.h5. To use this in the sample command below, add --fps libraries/fps_enamine50k.h5 to the argument list.

Configuration files

The general command to run MolPAL is as follows:

molpal run -o {LOOKUP,DOCKING} --objective-config <path/to/objective_config> --libary <path/to/library.csv[.gz]> [additional library arguments] [additional model/encoding/acquistion/stopping/logging arguments]

Alternatively, you may use a configuration file to run MolPAL, like so:

molpal run --config <path/to/config_file>

Two sample configuration files are provided: minimal_config.ini, a configuration file specifying only the necessary arguments to run MolPAL, and sample_config.ini, a configuration file containing a few common options to specify (but not all possible options.)

Configuration files accept the following syntaxes:

• --arg value (argparse)
• arg: value (YAML)
• arg = value (INI)
• arg value

Examples

A sample command to run one of the experiments used to generate data in the initial publication is as follows:

molpal run --config examples/config/Enamine50k_retrain.ini --name molpal_50k --metric greedy --init-size 0.01 --batch-sizes 0.01 --model rf

or the full command:

molpal run --name molpal_50k --write-intermediate --write-final --retrain-from-scratch --library libraries/Enamine50k.csv.gz --validated --metric greedy --init-size 0.01 --batch-sizes 0.01 --model rf --fingerprint pair --length 2048 --radius 2 --objective lookup --objective-config examples/objective/Enamine50k_lookup.ini --top-k 0.01 --window-size 10 --delta 0.01 --max-iters 5

Required Settings

The primary purpose of MolPAL is to accelerate virtual screens in a prospective manner. Currently (December 2020), MolPAL supports computational docking screens using the pyscreener library

-o or --objective: The objective function you would like to use. Choices include docking for docking objectives and lookup for lookup objectives and this dictates the options that must be specified in the objective-config file:

• docking: a pyscreener-style config file. An example may be seen here
• lookup: see any of the lookup examples in this folder

--libraries: the filepaths of CSV files containing the virtual library as SMILES (or CXSMILES) strings. If CXSMILES, pass the additional --cxsmiles flag

Optional Settings

• --fps: the filepath of an HDF5 file containing the precomputed fingerprints of your virtual library. MolPAL relies on the assumption that the ordering of the fingerprints in this file is exactly the same as that of the library file and that the encoder used to generate these fingerprints is exactly the same as the one used for model training. MolPAL handles writing this file for you if unspecified, so this option is mostly useful for avoiding the overhead at startup of running MolPAL again with the same library/featurizer settings.
• --invalid-idxs: unless MolPAl prepares your fingerprints file for you, it must validate each SMILES string in the library and determine the set of invalid indices in the virtual library. This can be time-consuming for large virtual libraries, so passing the set of lines containing invalid SMILES strings to MolPAL, if known, can save time. Preparing the fingerprints file as in the preprocessing section will output this set for you.

MolPAL also has a number of different model architectures, encodings, acquisition metrics, and stopping criteria to choose from. Many of these choices have default settings that were arrived at through hyperparameter optimization, but your circumstances may call for modifying these choices. To see the full list, run MolPAL with either the -h or --help flags. A few common options to specify are shown below.

• -k: the fraction (if between 0 and 1) or number (if greather than 1) of top scores to evaluate when calculating an average. (Default = 0.005)

• --window-size and --delta: the principle stopping criterion of MolPAL is whether or not the current top-k average score is better than the moving average of the window_size most recent top-k average scores by at least delta. (Default: window_size = 3, delta = 0.1)

• --budget: if you would like to limit MolPAL to exploring a fixed fraction of the libary or number of inputs, you can specify that by setting this value. (Default = 1.0)

• --max-iters: Alternatively, you may specify the maximum number of iterations of exploration. (Default = 50)

• --model: the type of model to use. Choices include rf, gp, nn, and mpn. (Default = rf)

  • --conf-method: the confidence estimation method to use for the NN or MPN models. Choices include ensemble, dropout, mve, and none. (Default = 'none'). NOTE: the MPN model does not support ensembling

• --metric: the acquisition metric to use. Choices include random, greedy, ucb, pi, ei, thompson, and threshold (Default = greedy.) Some metrics include additional settings (e.g. the β value for ucb.)

Reproducing Experimental Results

Generating data

The data used in the original publication were generated using the configuration files located in examples/config folder. The AmpC data was too large to include in this repo, but it may be downloaded from here. For more details on analyzing the data and generating the figures see the notebooks folder.

Timing

the following timings used Intel Xeon 6230 CPUs and Nvidia GeForce RTX 2080 TI GPUs

¹fingerprint code currently only support single process writing to limit total memory footprint. We have found it is I/O limited beyond 8 CPUs

²used Intel Xeon 6130 CPUs and Nvidia V100 GPUs

Object Model

MolPAL is a software for batched, Bayesian optimization in a virtual screening environment. At the core of this software is the molpal library, which implements several classes that handle specific elements of the optimization routine.

Explorer: An Explorer is the abstraction of the optimization routine. It ties together the MoleculePool, Acquirer, Encoder, Model, and Objective, which each handle (roughly) a single step of a Bayesian optimization loop, into a full optimization procedure. Its main functionality is defined by the run() method, which performs the optimization until a stopping condition is met, but it also defines other convenience functions that make it amenable to running a single iteration of the optimization loop and interrogating its current state if optimization is desired to be run interactively.

MoleculePool: A MoleculePool defines the virtual library (i.e., domain of inputs) and caches precomputed feature representations, if feasible.

Acquirer: An Acquirer handles acquisition of unlabeled inputs from the MoleculePool according to its metric and the prior distribution over the data. The metric is a function that takes an input array of predictions and returns an array of equal dimension containing acquisition utilities.

Featurizer: A Featurizer computes the uncompressed feature representation of an input based on its identifier for use with clustering and models that expect vectors as inputs.

Model: A Model is trained on labeled data to produce a posterior distribution that guides the sequential round of acquisition

Objective: An Objective handles calculation of the objective function for unlabeled inputs

Future Directions

Though MolPAL was originally intended for use with protein-ligand docking screens, it was designed with modularity in mind and is easily extendable to other settings as well. In principle, all that is required to adapt MolPAL to a new problem is to write a custom Objective subclass that implements the calc method. This method takes a sequence SMILES strings as an input and returns a mapping from SMILES string -> objective function value to be utilized by the Explorer. To this end, we are currently exploring the extension of MolPAL to subsequent stages of virtual discovery (MD, DFT, etc.) If you make use of the MolPAL library by implementing a new Objective subclass, we would be happy to include your work in the main branch.

Citation

If you used MolPAL in your work, we would appreciate you citing us!

@article{graff_accelerating_2021,
  title = {Accelerating high-throughput virtual screening through molecular pool-based active learning},
  author = {Graff, David E. and Shakhnovich, Eugene I. and Coley, Connor W.},
  journal = {Chemical Science},
  year = {2021},
  volume = {12},
  number = {22},
  pages = {7866--7881},
  doi = {10.1039/D0SC06805E},
}

If you additonally used the design space pruning feature, we would also appreciate you citing that work:

@article{graff_self-focusing_2022,
  title = {Self-{Focusing} {Virtual} {Screening} with {Active} {Design} {Space} {Pruning}},
  volume = {62},
  issn = {1549-9596},
  url = {https://doi.org/10.1021/acs.jcim.2c00554},
  doi = {10.1021/acs.jcim.2c00554},
  number = {16},
  urldate = {2022-09-12},
  journal = {Journal of Chemical Information and Modeling},
  author = {Graff, David E. and Aldeghi, Matteo and Morrone, Joseph A. and Jordan, Kirk E. and Pyzer-Knapp, Edward O. and Coley, Connor W.},
  month = aug,
  year = {2022},
  pages = {3854--3862},
}