Metadata-Version: 2.1
Name: guacamol
Version: 0.5.2
Summary: Guacamol: benchmarks for de novo molecular design
Home-page: https://github.com/BenevolentAI/guacamol
Author: BenevolentAI
Author-email: guacamol@benevolent.ai
License: MIT
Description: # GuacaMol
        
        [![Build Status](https://travis-ci.com/BenevolentAI/guacamol.svg?branch=master)](https://travis-ci.com/BenevolentAI/guacamol)
        
        **GuacaMol** is an open source Python package for benchmarking of models for 
        *de novo* molecular design.
        
        For an in-depth explanation of the types of benchmarks and baseline scores,
        please consult our paper 
        [Benchmarking Models for De Novo Molecular Design](https://arxiv.org/abs/1811.09621)
        
        ## Installation
        
        The easiest way to install `guacamol` is with `pip`:
        ```bash
        pip install guacamol
        ```
        
        `guacamol` requires the [RDKit library](http://rdkit.org/) (version `2018.09.1.0` or newer).
        
        
        ## Benchmarking models
        
        For the distribution-learning benchmarks, specialize `DistributionMatchingGenerator` 
        (from `guacamol.distribution_matching_generator`) for your model. 
        Instances of this class must be able to generate molecules similar to the training set.  
        For the actual benchmarks, call `assess_distribution_learning` 
        (from `guacamol.assess_distribution_learning`) with an instance of your class. 
        You must also provide the location of the training set file (See section "Data" below).
        
        For the goal-directed benchmarks, specialize `GoalDirectedGenerator` 
        (from `guacamol.goal_directed_generator`) for your model. 
        Instances of this class must be able to generate a specified number of molecules 
        that achieve high scores for a given scoring function.  
        For the actual benchmarks, call `assess_goal_directed_generation` 
        (from `guacamol.assess_goal_directed_generation`) with an instance of your class. 
        
        Example implementations for baseline methods are available from https://github.com/BenevolentAI/guacamol_baselines.
        
        
        ## Data
        
        For fairness in the evaluation of the benchmarks and comparability of the results, 
        you should use a training set containing molecules from the ChEMBL dataset.
        Follow the procedure described below to get standardized datasets.
        
        
        ### Download
        
        You can download pre-built datasets [here](https://figshare.com/projects/GuacaMol/56639):
        
        md5 `05ad85d871958a05c02ab51a4fde8530` [training](https://ndownloader.figshare.com/files/13612760 )  
        md5 `e53db4bff7dc4784123ae6df72e3b1f0` [validation](https://ndownloader.figshare.com/files/13612766)  
        md5 `677b757ccec4809febd83850b43e1616` [test](https://ndownloader.figshare.com/files/13612757)  
        md5 `7d45bc95c33c10cb96ef5e78c38ac0b6` [all](https://ndownloader.figshare.com/files/13612745)  
        
        
        ### Generation
        
        To generate the training data yourself, run 
        ```
        python -m guacamol.data.get_data -o [output_directory]
        ```
        which will download and process ChEMBL for you in your current folder.
        
        This script will use the molecules from 
        [`holdout_set_gcm_v1.smiles`](https://github.com/BenevolentAI/guacamol/blob/master/guacamol/data/holdout_set_gcm_v1.smiles)
        as a holdout set, and will exclude molecules very similar to these.
        
        Different versions of your Python packages may lead to differences in the generated dataset, which will cause the script to fail.
        See the section below ("Docker") to reproducibly generate the standardized dataset with the hashes given above.
        
        ### Docker
        
        To be sure that you have the right dependencies you can build a Docker image, run from the top-level directory:
        ```
        docker build -t guacamol-deps dockers/
        ```
        Then you can run:
        ```
        docker run --rm -it  -v `pwd`:/guacamol -w /guacamol guacamol-deps python -m guacamol.data.get_data -o guacamol/data --chembl
        ```
        
        
        ## Leaderboard
        
        Will come soon.
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: rdkit
