updating package version and README to match

README.md

@@ -15,14 +15,15 @@ The goal of model-based optimization is to find an input **x** that maximizes an
 Design-Bench can be installed with the complete set of benchmarks via our pip package.
 
 ```bash
-pip install design-bench[all]==2.0.18
+conda install rdkit -c conda-forge
+pip install design-bench[all]==2.0.19
 pip install morphing-agents==1.5.1
 ```
 
 Alternatively, if you do not have MuJoCo, you may opt for a minimal install.
 
 ```bash
-pip install design-bench==2.0.18
+pip install design-bench==2.0.19
 ```
 
 ## Available Tasks

setup.py

@@ -7,7 +7,7 @@
     LONG_DESCRIPTION = readme.read()
 
 
-setup(name='design-bench', version='2.0.18', license='MIT',
+setup(name='design-bench', version='2.0.19', license='MIT',
       packages=find_packages(include=['design_bench', 'design_bench.*']),
       description='Design-Bench: Benchmarks for '
                   'Data-Driven Offline Model-Based Optimization',
@@ -17,13 +17,14 @@
       author_email='brandon@btrabucco.com',
       url='https://github.com/brandontrabucco/design-bench',
       download_url='https://github.com/'
-                   'brandontrabucco/design-bench/archive/v2_0_18.tar.gz',
+                   'brandontrabucco/design-bench/archive/v2_0_19.tar.gz',
       keywords=['Deep Learning', 'Neural Networks',
                 'Benchmark', 'Model-Based Optimization'],
       extras_require={'all': ['gym[mujoco]'], 'cma': ['cma']},
       install_requires=['pandas', 'requests', 'scikit-learn',
-                        'torch', 'torchvision',
-                        'numpy', 'tensorflow>=2.2', 'transformers'],
+                        'torch', 'torchvision', 'numpy',
+                        'tensorflow>=2.2', 'transformers',
+                        'tokenizers', 'deepchem'],
       classifiers=[
           'Intended Audience :: Developers',
           'Intended Audience :: Science/Research',