Contains code for encoding UVCBs (mixtures) into machine-readable formats (MInChIs)

View the Project on GitHub ninasachdev/UVCB-MInChI

Cheminformatics of Unknown, Variable, Complex, and Biological (UVCB) substances

Unknown, Variable, Complex, and Biological (UVCB) substances are commonly found in our environment, but their complex chemical makeup can be challenging to understand. Since there is currently no way to standardize UVCB substances, this project seeks to encode these mixtures into a machine-readable format developed by Clark et. al.1 , called the Mixtures InChI (MInChI) string. This article will provide a step-by-step tutorial on how to generate MInChI strings from UVCB data.

I would also like to acknowledge Adelene Lai and Dr. Zhanyun Wang for their mentorship and support throughout this project.

Research Questions

Here are the research questions motivating this project:

  1. To what extent can UVCBs (mixtures) be encoded in machine-readable formats i.e., as MInChIs?
  2. What challenges exist in encoding MInChIs using currently available UVCB data2?
  3. How can UVCB data providers improve the communication and dissemination of UVCB compounds?

Throughout this article, these research questions will be addressed as we walk through the Python code used to generate the MInChI strings.

Installing and Running Mixtures Github

It is useful to begin this project by understanding the work done by Clark et. al. You can run their Github repository3 to generate several Mixfiles that can be analyzed in raw text form and viewed using the Electron app. When installing Clark et. al.’s mixtures project, if you encounter an error when running the command electron app, try these commands instead:

cd app
./node_modules/.bin/electron .

Parsing the Data

The UVCB data was collected by Dr. Wang and sorted into three different files. The “EU_REACH” file comes from the European Union regulation called the Registration, Evaluation, Authorisation and Restriction of Chemicals. The “mixtures” file contains single mixtures, while the “random” file contains mixtures that are more difficult to characterize.

In each row of the file, there are 150 UVCB substances. The columns correspond to different chemical characteristics of the substance. Of the 53 columns called chem_keys, the columns needed to generate the Mixfile and MInChI are “Chemical_Name_Final”, “InChIKey_Final”, “InChI_Final”, and “SMILES_Final”. Although the remaining 49 column entries are not needed, they will still be extracted from the file.

with open (filepath, 'r') as infile:
    lines = infile.readlines()
    chem_keys =  lines[0].split('\t')

The entire file is parsed into a list of dictionaries called chem_dict, with each list entry corresponding to a dictionary of keys and values for a given UVCB substance.

chem_dict = []
for i in range(1, len(lines)): #want to skip the first row of labels
    current_dict = {}
    for k in range(len(chem_keys)):
        current_dict[chem_keys[k]] = lines[i].split('\t')[k]

For example, the second row in the “EU_REACH” file would be represented as a dictionary where:

current_dict = {
        “ID” : "149363"
        “CAS_No” : "66455-17-2"
        “Chemical_Name” : "Alcohols, C9-11"
        and so on…

In case this list of dictionaries needs to be accessed later on for a different project, chem_dict will be saved as a JSON file.

Extracting Relevant Keys

As mentioned previously, “Chemical_Name_Final”, “InChIKey_Final”, “InChI_Final”, and “SMILES_Final” are the keys needed to generate the Mixfile and MInChI for each substance. As described by Clark et. al., a key component of the MInChI string is the structure identifier, or the InChI. Before extracting these four keys, let’s filter chem_dict to include only the entries that have an InChI value. These substances will be stored in filter_InChI.

for i in range(len(chem_dict)):
    if len(chem_dict[i]['InChI_Final']) > 0:

Next, the four keys needed to create the Mixfile and MInChI for each substance can be stored in separate lists.

for i in range(len(filter_InChI)):

These four lists will be used later on.

Generating the Molfile

One of the characteristics of a substance’s Mixfile is its corresponding Molfile. To generate the Molfile, the Chem module from an open-source cheminformatics library called rdkit is used to convert the substance’s SMILES value to its Molfile. Each molfile is stored in molfile_list.

for i in range(len(SMILES_list)):
    m = Chem.MolFromSmiles(SMILES_list[i])
    molfile_string = Chem.MolToMolBlock(m)

Finding Substance Concentrations

In Clark et. al.’s paper, they list the different ways a chemical structure can be quantified (%, mol/L, w/v%, etc.). However, one limitation of this data is that there is no column that specifies the quantity of the substance and its corresponding units. The only indication of a quantity comes from a few of the chemical names that contain a ratio concentration, such as (1:1) or (1:2). These values are extracted and stored in quantities. The MInChI format denotes the ratio with “vp” units.

for n in range(len(names)):
    name = names[n]
    for l in range(len(name)):
        if name[l] == '(':
            if name[l+2] == ':' and name[l+4] == ')':
                quantities[n] = name[l:l+5]
                units[n] = 'vp' #MInChI units for ratio

Creating the Mixfile

The next step is to create a Mixfile for each substance, which is formatted as a JSON file. Each Mixfile contains the Mixfile version, chemical name, Molfile, InChI, InChI key, SMILES, and concentration ratio if it exists.

for i in range(len(InChI_list)):
    mixfile_dict = {}
    mixfile_dict['mixfileVersion'] = 0.01
    mixfile_dict['name'] = names[i]
    mixfile_dict['molfile'] = molfiles[i]
    mixfile_dict['inchi'] = InChI_list[i]
    mixfile_dict['inchiKey'] = InChI_key_list[i]
    mixfile_dict['smiles'] = SMILES[i]
    if len(quantities[i]) > 0:
        ratio = '[' + str(quantities[i][1]) + ', ' + str(quantities[i][3] + ']')
        mixfile_dict['ratio'] = ratio
    with open('mixfiles/' + filename + '_mixture' + str(i) + '.mixfile', 'w') as outfile:
        json.dump(mixfile_dict, outfile)

As shown from the code above, only one dictionary is created for the entire Mixfile, even though there are multiple components that make up each UVCB substance. A major challenge of creating Mixfiles from this data has to do with the components of a mixture not being identifiable. The InChI values, for example, don’t contain the “&” symbol, which denotes separate components.

Ideally, there would be an InChI, InChI key, and SMILES for each component of the mixture. However, the dataset only contains, at most, one InChI, one InChI key, and one SMILES for each mixture. Thus, the corresponding MInChI string will only contain one index.

Generating the MInChI String

We are finally ready to generate the MInChI strings! We have collected all the pieces and now they can be assembled from the Mixfiles. As mentioned earlier, because there is only one component in each Mixfile, it is relatively straightforward to generate the MInChI. Clark et. al. provide a traversal algorithm, which would be used if these mixtures had more than one component.

for mixfile in mixfiles:
    header = 'MInChI=0.00.1S'
    identifier = mixfile['inchi'][9:]
    indexing = 'n1'
    concentration = ''
    if 'ratio' in mixfile.keys():
        concentration = mixfile['ratio'][1] + ':' + mixfile['ratio'][3] + 'vp'
    minchi = header + '/' + identifier + '/' + indexing + '/' + concentration
    minchis[mixfile['name']] = minchi

Each MInChI string is separated into four parts: the header, structure identifier, indexing, and concentration. All of the strings are stored in a dictionary called minchis, with each key corresponding to the name of the substance.

Analyzing MInChI Strings

Here are a few examples of the MInChI strings that were generated from the EU_REACH dataset. All MInChI strings have the same header: MInChI=0.00.1S.

Example #1:

Minchi Example #1

Example #2:

Minchi Example #2

Example #3:

Minchi Example #3

Finalizing MInChI Data

Once the MInChI strings are generated, they can be added back into the original datasets. A new column called “MInChI” will contain the strings that were generated for several mixtures in the datasets.

outfile = open(filename + '_MInChI.txt', 'w')

chem_keys[-1] = 'Iodo'
for i in range(len(chem_dict)):
    name = chem_dict[i]['Chemical_Name_Final']
    elts = list(chem_dict[i].values())
    elts[-1] = elts[-1].strip('\n')
    if name in minchis:
    outfile.write('\t'.join(elts) + '\n')

Summary Statistics

Summary statistics can be computed in order to better understand the data available to use in these three UVCB files. Here are some notable observations:

  EU_REACH mixtures random
Number of substances that have a MInChI (out of 150 total substances) 26 (17.3% of all substances) 36 (24% of all substances) 6 (4% of all substances)
Number of substances with InChI, SMILES, and concentration ratio 6 25 0
Number of substances with only InChI and SMILES 20 9 6

Out of the three files, the most MInChIs were generated from the “mixtures” UVCB data. There were also significantly more substances in the “mixtures” data had a concentration ratio. Nevertheless, it’s evident that most mixtures in all three files are don’t have enough information to encode the MInChI string.


To summarize, this project was able to parse through UVCB data and extract the information needed to create Mixfiles and MInChI strings. In order to create the Mixfile for a substance, the data should have information like the chemical name, SMILES , InChI, InChI key, quantity, and units. The minimum characteristics needed to create the MInChI string are the InChI and concentration (including units). Ideally, each substance would be separated into different components, such that each component has its own InChI and concentration.


  1. Clark, A. M.; McEwen, L. R.; Gedeck, P.; Bunin, B. A. Capturing Mixture Composition: An Open Machine-Readable Format for Representing Mixed Substances. Journal of Cheminformatics 2019, 11 (1), 33. https://doi.org/10.1186/s13321-019-0357-4.
  2. Wang, Z.; Walker, G. W.; Muir, D. C. G.; Nagatani-Yoshida, K. Toward a Global Understanding of Chemical Pollution: A First Comprehensive Analysis of National and Regional Chemical Inventories. Environ. Sci. Technol. 2020. https://doi.org/10.1021/acs.est.9b06379.
  3. cdd/Mixtures; Collaborative Drug Discovery, 2020. https://github.com/cdd/mixtures