Saturday, 21 November 2015

Bringing Molfile Sgroups to the CDK - Rendering Tips

In the last but one post I gave a demonstration of S(ubstructure)group rendering in the CDK. Now I want to give some implementation insights.

Abbreviations (Superatoms)

Abbreviations contract part of a structure to a line formula or common abbreviation.


Abbreviating too much or using unfamiliar terms (e.g. maybe using CAR for carbazole) can make a depiction worse. However some structures, like CHEMBL590010, can be dramatically improved.


One way to implement abbreviations would be by modifying the molecule data structure with literal collapse/contract and expand operations. Whilst this approach is perfectly reasonable, deleting atoms/bonds is expensive (in most toolkits) and it somewhat subtracts the "display shortcut" nature of this Sgroup.

For efficiency abbreviations are implemented by hiding parts of the depictions and remapping symbols. Just before rendering we iterator over the Sgroups and set hint flags that these atoms/bonds should not be included in the final image. If there is one attachment (e.g. Phenyl) we remap the attach point symbol text to the abbreviation label ('C'->'Ph'). When there are no attachments (e.g. AlCl3) we add a new symbol to the centroid of the hidden atoms.

Hide atoms and bonds Symbol Remap Abbreviated Result

For two or more attachments (e.g. SO2) you also need coordinate remapping.

Multiple Group

Multiple groups allow, contraction of a discrete number of repeating units. They are handled similarly to the abbreviations except we don't need to remap parts.


All atoms are present in the data structure but are laid out on top of each other (demonstrated below). We have a list of parent atoms that form the repeat unit. Therefore to display multiple groups we hide all atoms and bonds in the Sgroup except for parent atoms and the crossing bonds.

It's worth mentioning that hidden symbols are still generated but simply excluded from the final result. This allows bond back off for hetero atoms to be calculated correctly as is seen in this somewhat tangled example:


Polymer and Multiple group Sgroups require rendering of brackets. Encoded in the molfile (and when laid out) brackets are described only two points, a line. It is therefore up to the rendered to decide which side of the line the tick marks should face.

I've seen some implementations use the order of the points to convey bracket direction. Another method would be to point the brackets at each other. As shown for CHEBI:59342 this is not always correct.

Poor bracket direction Preferred bracket direction

I originally thought the solution might involve a graph traversal/flood-fill but it turns out there is a very easy way to get the direction correct. First we consider that brackets may or may not be placed on bonds, if a bracket is on a bond this information is available (crossing bonds).

  • For a bracket on a crossing bond exactly one end atom will be contained in the Sgroup, the bracket should point towards this atom.
  • If a bracket doesn't cross a bond then the direction should point to the centroid of all atoms in the Sgroup.

Saturday, 17 October 2015

Java Serialization: Great power but at what cost?

The default Java serialization framework provides a convenient mechanism for streaming in-memory Objects to another computer or storing them on disk. Beyond the obvious badness of being tied to the internal object layout (i.e. not stable through changes), serialization can be very inefficient. Externalization and libraries like Kyro are popular for improving performance.

SMILES: CO[C@@H]([C@H](OC(C)=O)[C@@H](OC(C)=O)[C@H](OC(C)=O)[C@H](OC(C)=O)COC(C)=O)SC

In the domain of Chemistry we have a rich variety of formats (e.g. SMILES) with which we can store molecules and reactions (in memory these are labelled graphs). Although these formats do not completely fulfil the utility of Object serialization they can be used as building block upon which we build. Not only are these defacto standards but they can be much faster and compact than default serialization of the in-memory connection table (graph) representation.

Recent History

Crafting efficient (de)serialization is beneficial and you can get great speed with simple setup. A few years ago I ran some experiments on writing an externalization stream for the Chemistry Development Kit (CDK) molecules (thread - High Performance Structure IO). Since the objects are huge any improvement over the default would be useful. This partly fed into the needs of CDK-Knime (a workflow tool) where I think CML was being used originally. From testing on ChEBI (~20,000 molecules) we see actually the ObjectInputStream was about as fast as an SDfile and much faster than CML. SDfiles are now much faster but that would be another post.

Read Performance
Method Time Size Throughput
AtomContainerStream 346 ms 11.1 MiB 63739 s-1
SDfile 4159 ms 51.7 MiB 5302 s-1
CML 18605 ms 91.5 MiB 1185 s-1
ObjectInputStream 5552 ms 93.9 MiB 3972 s-1

It was around that time that Andrew Dalke payed a visit to EMBL-EBI. In discussing what I was currently working on he promptly showed me how fast OEChem could read/write SMILES. Needless to say – pretty quick and as fast if not faster than my attempt at 'High Throughput' streaming.

The CDK now also has fast SMILES processing and I wanted to compare this to the serialization to see how much of a performance penalty there is.


For a benchmark I used 100,000 structures for ChEMBL 20.

$ shuf chembl_20.smi | head -n 100,000 > chembl_20_subset.smi

Writing it to a ObjectOutputStream takes 28.78 seconds. The SMILES subset file takes up 6.8 MiB on disk whilst the serialized objects take up 295 MiB. Ouch, that's 42x larger.

Code 1 - Writing to an ObjectOutputStream
IChemObjectBuilder bldr = SilentChemObjectBuilder.getInstance();
SmilesParser smipar = new SmilesParser(bldr);

String srcname = "/data/chembl_20_subset.smi";
String destname = "/data/chembl_20_subset.obj";

try (InputStream in = new FileInputStream(srcname);
     Reader rdr = new InputStreamReader(in, StandardCharsets.UTF_8);
     BufferedReader brdr = new BufferedReader(rdr);
     ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(destname))) {
    String line;
    long t0 = System.nanoTime();
    while ((line = brdr.readLine()) != null) {
        try {
            IAtomContainer mol = smipar.parseSmiles(line);

            // stereochemistry does not implement serializable...
            // so need to remove it
            mol.setStereoElements(new ArrayList(0));

        } catch (CDKException e) {
    long t1 = System.nanoTime();
    System.err.printf("write time: %.2f s\n", (t1 - t0) / 1e9);

In CDK we first read SMILES with Beam and then convert to the CDK objects so we'll also look at that small overhead. Here I compare the time to read the 100,000 SMILES using Beam, CDK, and the objects using an ObjectInputStream. Both CDK and Beam take less than 1 second whilst the ObjectInputStream takes more than 50.

In terms of throughput (mol per sec) here is the kind of speed we hit. I also show the total elapsed time for all 15 repeats.

MethodMinMaxElapsed TimeSize
Deserialization1961 s-12089 s-112 m 16 s295 MiB
Kryo (Auto)42401 s-144557 s-133.9 s186 MiB
Kryo Unsafe (Auto)44854 s-147331 s-131.9 s231 MiB
CDK135286 s-1142126 s-110.7 s6.8 MiB
Beam347534 s-1489545 s-13.2 s6.8 MiB

Auxiliary Data

With a performance difference that huge why would anyone want to use Serialization? Some use-cases might be that a format doesn't store the parts we need. A common argument against SMILES is the lack of coordinates but we can simply store this supplementary to the SMILES if we no what the input order will be (Code 2).

Code 2 - Writing Coordinates with SMILES
IAtomContainer  mol = ...;
// 'Generic' - avoid canon SMILES we are not doing identity check
SmilesGenerator sg  = SmilesGenerator.generic(); 

int   n     = mol.getAtomCount();
int[] order = new int[n];

// the order array is filled up as the SMILES is generated
String smi = sg.create(mol, order);

// load the coordinates array such that they are in the order the atoms
// are read when parsing the SMILES
Point2d[] coords = new Point2d[mol.getAtomCount()];
for (int i = 0; i < coords.length; i++)
  coords[order[i]] = container.getAtom(i).getPoint2d();

// SMILES string suffixed by the coordinates
String smi2d = smi + " " + Arrays.toString(coords);

Using that same technique it's relatively simply to extend this to handle arbitrary data fields and it even forms the basis of ChemAxon's extended SMILES. A more advanced method would be combining the SMILES with a DataOutputStream since we know how many coordinates there are expected to be.


I'm certainly not against a performant AtomContainerInputStream but the default Java serialization should never be the first choice. Hopefully this post has put some numbers on why and will discourage knee-jerk usage.


Added Kryo performance

Tuesday, 8 September 2015

Bringing Molfile Sgroups to the CDK - Demo

Despite the flaws, the molfile has been a defacto standard for chemical representation for several decades. The core format (atom and bond block) is well supported in many toolkits but more advanced features (dark corners) of the property block may be skipped.

At this year's Fall ACS (Boston '15) I bumped into an old colleague from ChEBI who told me they (ChEBI) couldn't use CDK because they wanted to display repeating brackets on records and CDK didn't do that.

Polymer representation (more precisely Structural Repeat Unit) used by ChEBI falls under the category of a Ctab Sgroup. I'd wanted to add support for Sgroups for some time and now had motivation to do so.

Substructure (or Substance) Groups

Over the years there seems to have been a shift in definition. The original literature[1] uses the term "substructure groups" but more recent materials use "substance groups"[2,3]. Personally I prefer "substructure" since it concisely summarises what they really are about.

Essentially an Sgroup annotates some part of the connection table (a substructure) with meta-information (data). There are several types of Sgroup that formalise the types of annotation present:

  • Display Shortcuts
    • Abbreviations
    • Multiple Groups
  • Polymers
    • Structural Repeat Unit (SRU)
    • Monomer
    • Copolymer (alternating, block, or random)
    • Mer
    • Crosslink
    • Graft
    • Modified
    • Any
  • Mixtures
    • Unordered Mixture
    • Ordered Mixture (formulation)
    • Component
  • Generic
  • Data

Example ChEBI Depictions

Egon reviewed the first patch (pull/149) last week that focussed on representation and molfile round tripping. The second patch enhances the rendering code to handle more than basic SRUs (e.g. >2 brackets) and display shortcuts.

As of ChEBI 131 there are 809 entries with at least one Sgroup. Generating the depictions of these from an SDfile took < 3 seconds, then a further 11 to actually write the files to disk. The rest of this post demonstrates some example of those depictions.

Display Shortcut, Abbreviations

Previously referred to as "superatoms", parts of a structure can be abbreviated to a more concise name (e.g. Ph for a phenyl substituent). The full structure is present but is only displayed when the expansion flag is set.

CHEBI:29441 CHEBI:7725

Display Shortcut, Multiple Group

Multiple groups allow structures with fixed repeating parts to be drawn more concisely. Similar to abbreviations, all the atoms and bonds are present but are hidden from display. They're actually all overlaid on one another with duplicated coordinates but for rendering you still want omit them from display.

CHEBI:1233 CHEBI:79399

Polymer, SRUs

The most common Sgroup used in ChEBI is the Structure Repeat Unit (SRU), an SRU defines a repeat unit of variable length. The brackets do not necessarily come in pairs, are parallel, or point towards each other.

CHEBI:16838 CHEBI:4294
CHEBI:53422 CHEBI:59342

Polymer, Others

A few entries encode copolymers and source-based representations (monomer).

CHEBI:59599 CHEBI:3814 (overlap in original)


A structure can have more than one Sgroup and they can be nested. Here we see a multiple group within an SRU. There is also a data Sgroup attached to the Zn-N bond marking it as a coordination bond for Marvin. I've not decided whether to render those yet, but we have the information there.


Additional Reading

  1. Gushurst et al. The substance module: the representation, storage, and searching of complex structures. J. Chem. Inf. Comput. Sci. (1991)
  2. Blanke G. Sgroups – Abbreviations, Mixtures, Formulations, Polymers, Structures with Statistical Distribution and Other Special Cases. Online - StructurePendium Technologies GmbH
  3. Accelrys Chemical Representation
  4. CTfile Formats Specification

Sunday, 9 August 2015

MMFF Partial Charges Improvements in CDK

Some time last year Mark Williamson brought to my attention discrepancies in CDK's MMFF partial charge calculation. Investigating further it seemed to mainly be a problem with atom typing. There were two existing classes that could assigned MMFF atom types using a combination of a decision tree and string matching hose codes. The 761 molecules from the MMFF94 Validation Suite provided by Paul Kersey were used to give a more comprehensive overview then our current tests.

The results showed reasonable precision per-atom in the validation suite but were less favourable per-molecule, the best implementation assigned types to <90% of the molecules with <16% assigned correctly.

Assigned Types
Correct Types
Assigned Types
Correct Types
ForceFieldConfigurator 15576 90.1% 12932 74.8% 678 89.1% 118 15.5%
MMFF94AtomTypeMatcher 17120 99.1% 12309 71.2% 659 86.6% 75 9.9%
MmffAtomTypeMatcher 17279 100.0% 17279 100.0% 761 100.0% 761 100.0%

I wasn't keen to hard code the atom typing procedure but was delighted to find Robert Hanson of JMol had some SMARTS patterns that could be used as a starting point. After about a month of tweaking I managed to simplify the SMARTS patterns and achieve 100% precision on the validation suite. You can find the SMARTS patterns here: /org/openscience/cdk/forcefield/mmff/MMFFSYMB.sma.

Apart from improving atom type assignments the charge assignment also needed updating to include charge sharing and bond class differences. This wasn't quite as simple as I first thought as the parameter set parsing also needed reworking. After many months of analysis paralysis I decided last week to just rewrite what was needed and delegate calls from the existing implementation.

Now the patch is finished, charge assignments are much better. Notice that in the previous version (labelled CDK 1.5.10) equivalent terminal oxygens and the nitrogens in imidazole anion have different values. The overall charge was also inconsistent with the formal charges.

Improved charge assignment

Roger Sayle noted to me this week that MMFF charges should not be affected by representation, for example, charge separated pi bonds in nitro groups or phosphates.

Charges are independant of representation

Many thanks to Mark and Alison Choy for reporting the problem and adding patches for debugging and testing.

Thursday, 29 January 2015

PhD Thesis Now Available

I'm please to announce that my PhD thesis is now available from the Cambridge DSpace repository: One thing potentially of note is the description of fast Kekulisation that I originally intended to write as a blog post. Also following up from NextMove Software's recent post by Daniel on Cahn-Ingold-Prelog (CIP), the results of Chapter 6 contains some more CIP madness.