Prerequisites

• jchem-psql service must run

• the extension named chemaxon_type must be created.

Example:

The created column can be propagated with any of the well-known chemical structural data file formats. Due to postgres type system, the provided string value is automatically converted to MOLECULE type. For importing different molecule formats from local files, see Auxiliary functions for importing into a table.

Import from SD file

Import from local SD/mol files containing added fields besides the chemical structure data

The content of all the added fields in the SD file will be stored in a hashmap like type column.Optionally selected added fields from the SD file can be stored in separate columns as well.

Follow the next steps:

1. Set the content of the SD file to a variable (e. g.: content)

   \set variable_name `cat sdf_file_name`

   Example:

   \set content `cat ~/a.sdf`

   You can check the table generated by the parse_sdf function

   SELECT * FROM parse_sdf(:'content');

   The parse_sdf function returns the content of the sdf file as a table, which has two columns:

   molSrc = the full sdf source of the original entry in the sdf file

   props = hstore type column, which contains the properties of the sdf entry

   You can also create a table similar to the table created by the parse_sdf function by:

   CREATE TABLE tableName (mol MOLECULE('sample'), p hstore);

2. Create a table with create table as:

   CREATE TABLE table_name AS

   SELECT molSrc::molecule('molecule_type_name') AS structure_column_name[, props ->'sdf_field_name' AS column_name]

   FROM parse_sdf(:'');

   Example:

   CREATE TABLE mysdftable AS

   SELECT molSrc::molecule('sample') AS mol,

   props -> 'MOLFORMULA' AS formula, CAST(props -> 'CDID' AS INTEGER) AS id

   FROM parse_sdf(:'content');

   where

   mysdftable = the name of the table

   mol = the name of the column for storing structural data

   molecule('sample') = the type of column mol

   sample = molecule type (must be a defined type in /etc/chemaxon/types/ folder)
   

   MOLFORMULA = the name of the field in the SD file for storing chemical formula

   formula = the name of the column for storing chemical formula

   CDID = the name of the field in the SD file storing the identifier

   id = the name of the column for storing the identifier integer

   You can also store all properties from the SD file entry in a separate column in the structure table, and later, optionally, add additional columns to separately store selected properties.

   Example:

   \set content `cat ~/a.sdf`

    

   CREATE TABLE mytable AS

   SELECT molSrc::molecule('sample') AS mol,

   props AS properties

   FROM parse_sdf(:'content');

    

   ALTER TABLE mytable ADD COLUMN formula TEXT;

    

   UPDATE mytable SET formula = properties -> 'MOLFORMULA'

Collect invalid molecules

Available from version 1.8.

Running the steps below, the molecules in SD/mol files will be checked during the import and the invalid molecules will be stored in a separate table named <new_table_name>_error.

Steps:

1. Run the import_sdf.sql script to create function import_sdf. This script file is a customizable tool; you can update it according to your needs.

   \i import_sdf.sql;

2. Run the following commands including import_sdf function with the appropriate parameters:

   \set <variable_name> `cat <path_to_your_sdf_file>`;

   SELECT import_sdf(:'<variable_name>', '<new_table_name>', '<molecule_type>');

   Example:

   \set content `cat ~/a.sdf`;

   SELECT import_sdf(:'content', 'molecules', 'sample');

Import from (cx)smiles or (cx)smarts files

Import from (cx)smiles or (cx)smarts files by the standard copy sql function

Example:

Import from(cx)smiles or (cx)smarts files while collecting the invalid molecules

Available from version 1.8

Running the steps below, the molecules in smiles/cxsmiles/smarts/cxsmarts files will be checked during the import and the invalid molecules will be stored in a separate table named <new_table_name>_error . We propose the following two methods: PL/pgSQL script method and SQL commands method.

PL/pgSQL script method

We suggest applying this script method when the server and the client are on the same machine; or at least the molecule files are available on the server and their absolute path is known. Otherwise, apply the steps of SQL commands method.

Steps:

1. Run the import_single_line_format.sql script to create function import_single_line_format. This script file is a customizable tool; you can update it according to your needs.

   \i import_single_line_format.sql;

2. Run the following SELECT using import_single_line_format function with the appropriate parameters:

   SELECT import_single_line_format('<absolute_path_to_my_file_on_server>', '<new_table_name>', '<molecule_type>');

   -- example:

   Example:

   SELECT import_single_line_format('/home/myuser/molecules.smiles', 'molecules', 'sample');

SQL commands method

1. Create a table which will contain the valid molecules

   CREATE TABLE my_table(mol TEXT);

2. Import from file

   \COPY my_table FROM '~/molecules.smiles' (FORMAT csv);

3. Create and load a table for the invalid molecule sources

   CREATE TABLE my_table_error AS SELECT mol FROM my_table WHERE NOT is_valid_molecule(mol);

4. Remove invalid molecules from my_table

   DELETE FROM my_table WHERE mol IN (SELECT mol FROM my_table_error);

5. Convert the valid molecule sources into molecule

   ALTER TABLE my_table ALTER COLUMN mol TYPE molecule('sample') USING mol::molecule('sample');

Convert molecule source text to molecule from an existing table

Follow the steps (starting from step 3) of the SQL commands method above.

Casting a string to any Molecule type defined in /etc/chemaxon/types/

Interpreting a string using a specific format

As molecule strings are ambiguous in some cases, it is possible to interpret the given molecule string according to the given molecule format using the molecule(String, String) function:

where
structure_string = molecule string representation
molecule_format = molecule format string, see file formats

A typical usecase is the differentiation between SMILES and SMARTS strings:

• 'CC' with SMILES notation is interpreted as two aromatic or aliphatic carbon atoms connected with a single bond.

• 'CC' with SMARTS notation is interpreted as two aliphatic carbon atoms connected with a single bond.

Example:

Transformations

Transformation function is provided to transform the molecule structure according to specific needs. Multiple transformation strings can be provided separated by two dots.

where

molecule = "molecule string" | Molecule object | table column
transformation string = "fullfragment" | "dbsmarkedonly"

Molecule transformation combined with substructure search:

• Double bond stereo option: dbsmarkedonly

By default, CIS query matches only with CIS target and TRANS query matches only with TRANS target. If matching of CIS query with both CIS and TRANS targets or matching of TRANS query with both CIS and TRANS targets is aimed, then the query molecule has to be transformed.
The query_transform function with dbsmarkedonly option is provided to accomplish this transformation.

Examples:

• Full fragment (exact fragment) search option: fullfragment

Full fragment search can be executed by transforming the query structure in a way that it matches only a full fragment of the target structure and by executing a substructure search on this modified query structure. The transformation adds 's*' query search property to all atoms.

Examples:

Fingerprint generation

Fingerprints can be generated using the following function:

or

where size is the number of bits in the fingerprint bit string; it must be divisible by 32, e.g.: 512.

Example:

Chemical terms

(Available since version 1.4.)

Molconvert

The use of ChemAxon's MolConverter is supported with some limitations:

where

structure = a Molecule in any of the following formats

format = mrv, mol, rgf, sdf, rdf, csmol, csrgf, cssdf, csrdf, cml, smiles, cxsmiles, abbrevgroup, sybyl, mol2, pdb, xyz, inchi, or name

Example:

Molecule validation

Available from version 1.8.

The following function is provided to check the validity of the molecules:

where

structure_text = a Molecule in any of the following formats:

mrv, mol, rgf, sdf, rdf, csmol, csrgf, cssdf, csrdf, cml, smiles, cxsmiles, abbrevgroup, sybyl, mol2, pdb, xyz, inchi, or name

Example:

You can filter out the invalid structures from a table using the following SELECT statement:

Substructure search

Substructure search is performed using the symmetrical sub-/super-structure search operator: |<|.

where

query_mol = "molecule string" | Molecule object | table column
target_mol = "molecule string" | Molecule object | table column

Examples:

See also molecule functions about defining the query structure format.

Superstructure search

Superstructure search is performed using the sub-/super-structure search operator: |<|.

Example:

Full fragment (exact fragment) search

Full fragment search can be executed by transforming the query structure in a way that it matches only a full fragment of the target structure and by executing a substructure search on this modified query structure.
See details in transformations for full fragment search.

Example:

Duplicate search

Duplicate search is performed using the |=| operator.

Example:

Tautomer search

You have to apply such a molecule type which has tautomer = GENERIC tautomer mode.

How is chemical matching of the query and the target executed in tautomer search? The generic tautomer - representing all theoretically possible tautomers - of the target is matched with the query structure itself. This method is applied in substructure search, full fragment search, duplicate, and superstructure search.

Limitations:

• SMARTS atoms and SMARTS bonds in the query structures are not supported.

• The use of bond lists in query structures may slow down the search.

Similarity search

For similarity searches you must have a column filled with fingerprints. These fingerprints can correspond to molecules in another table (method 1) or to molecules in the same table (method 2). We strongly advise using the other table method (method 1) because of the performance. The fingerprint data are usually small and PostgreSQL database engine can store them in memory.

Fingerprints can be generated as described above.

The following statements should be used for running similarity search using method 1:

where

operator can be <, <=, =, >, >=

similarity_value is a number between 0 and 1

fp is the recommended name of the fingerprint column

Example:

Insert molecules into moltable and/or create a trigger, then continue with the followings:

The following statements should be used for running similarity search using method 2:

Example:

You can create a trigger to update the fingerprint column in the fingerprint table (fptable) when new records are inserted into the molecule table (moltable).

Example:

Combination of structure query AND structure/non-structure query

• WHERE condition referring to more than one column containing chemical structure data is not supported.

• WHERE condition referring to one structure column and one or more columns containing non-chemical structure data is supported.

Relevance sorting

Available from version 1.8.

As in standard SQL, the user can order his results using ORDER BY commands.

For ordering search results, JChem PostgreSQL function relevance(Molecule) is provided, which gives back a numeric type value based on the atom counts and further topological features of the molecule.

It is suggested that relevance values be stored in the table for further query. It is also suggested that an index is created on the relevance column and further queries return their results ordered by the relevance value. This facilitates the usage of LIMIT <n> conditions as the most relevant hits are at the beginning of the result set.

Example (assuming <mytable> has column "mol" of type Molecule):

Other chemical features/measures may also be used for ordering, the chemterm function provides help for their definition.

You can create a trigger to update the relevance column when new records are inserted into the table.

Example:

Debugging

Raising the log level of the psql-client for debugging purposes:

Performance log

You can log the performance of the current session as:

You can clear the log as (available since version 1.4):

Performance tuning of combined queries

Available since version 1.4.

The performance of searching with combined queries - when chemical structure query condition is combined with a non-structure query condition - might be improved by executing the following calibration steps.

1. Prerequisite: the column structure_column_name in table table_name used for the calibration must be indexed using indextype chemindex.

2. Calibration of cost factors

   select calibrate_cost_factors('table_name', 'structure_column_name', 'query_structure');

   where

   query_structure is an optional parameter. If not specified, chlorobenzene (Clc1ccccc1) is used by default.

   The applied query_structure is an important key factor of the calibration. The best specified query_structure has almost the same (+/- 10%) estimated selectivity in the given table as the number of its search hits. We recommend the next explain statement to run in order to get the estimated selectivity. The number of rows given in the output of the next statement is the estimated selectivity.

   explain select * from table_name where 'query_structure' |<| structure_column_name;

   In order to get better performance, you may need to increase the default_statistics_target value for PostgreSQL analyzer. Its default value 100 can be increased to maximum 10000.

3. Application of the calibrated cost factors
   Please follow the suggested solutions given in the output of the select calibrate_cost_factors statement.

   Example:

To set the values permanently, add the lines

chemaxon.cost_slope_factor = -5.14e-07

chemaxon.cost_intercept_factor = 0.03866

to the PostgreSQL configuration file (e.g.: /etc/postgresql/9.4/main/postgresql.conf)

To set values only for the current session, execute the following commands:

Performance tuning of searches in big tables

Available since version 1.8

Searching in big table can be a very time consuming operation if the available memory cannot store all the necessary data of the whole table. The memory management operations could increase the search times.

Performance of searches in big tables can be tuned by applying the following JVM heap size and PostgreSQL settings, furthermore, the SELECT statements used for querying can be refined.

1. JVM heap size settings
   In order to ensure that the biggest table fits into the memory, the approximate minimum heap size can be calculated as:

   heap size > <number of rows in the biggest table in millions> * 800 MB + 700 MB

   Example on a table of 5 million rows:

   heap size > 4700 MB

   Heap size can be set by specifying the Xmx parameter in the JCHEM_PSQL_OPTS variable of the /etc/default/jchem-psql properties file.

2. PostgreSQL settings
   If the user would like to execute queries

   1. on big tables - containing many entries and big row data. E.g., the molecule source is a verbose one (sdf, mrv, ...)

   2. where psql needs to fetch majority of the rows. E.g., sql query without any additional restricting condition or limit parameter

   then PostgreSQL needs to be able to fetch all rows rapidly requiring large memory because of the big table. In order to achieve this, set the shared_buffers parameter in postgresql.conf to be able to store your table.

   Table size can be checked by \dt+ <tablename> command from the psql client.

   Additionally, to enhance PostgreSQL performance, it may be advisable to increase linux's shared memory with the following command:

   sysctl -w kernel.shmall = <shared memory size in bytes>/<page size>

   The default value of page size is usually 4096. It can be checked by getconf PAGESIZE.To store the value of kernel.shmall permanently, add it it to the sysctl.conf file. For more details about required shared memory settings for PostgreSQL server please visit the PostgreSQL documentation.

   To reduce the needed buffer size you may consider the following possibilities.

   1. Change the input format to a concise one: smiles, smarts, ...
      (e.g., 8M rows of a PubChem dataset need ca. 17 GB when the molecules are in sdf format, but only ca. 1.4 GB when they are in smiles format).

   2. Avoid queries that require the fetching of all table data through adding additional "where" condition or limit parameter.

      For example, instead of

      SELECT <id_column_name> FROM <table_name> WHERE 'c1ccccc1' |<| mol;

      which may return several millions of hits, use the following statement applying LIMIT <n> and relevance sorting in order to obtain the most relevant n hits:

      SELECT <id_column_name> FROM <table_name> WHERE 'c1ccccc1' |<| mol ORDER BY relevance LIMIT 100;

   3. Increase the fillfactor to 90% instead of the above recommended 50% and perform vacuum regularly.

   4. As PostgreSQL is not optimized for the COUNT() method, it may take a long time if it returns large value. For obtaining an estimation of the count of hits we rather suggest using the EXPLAIN command.

   Please also check that the JVM heap size plus the PostgreSQL shared buffer size is not more than 2/3-rd of the total available memory to avoid slow-down of the operation system.

3. Troubleshooting
   If searches on big tables are slow then probably the previously mentioned memory parameters need to be adjusted properly. However if searches on small tables are slow even after repeated execution, it may be a result of too large cache of the jchem-psql service.
   The cache size is estimated automatically based on heap size and based on average size molecules. It may cause too much heap consumption for big molecules. In such cases, it is suggested to define the two cached object count parameters manually in /etc/chemaxon/jchem-psql.conf. Set them to a value smaller than Xmx [in GB] * 1000000.