Planet Chemoinformatics

Correction to Design, Synthesis, and X-ray Crystallographic Analysis of a Novel Class of HIV-1 Protease Inhibitors

2012-05-18T15:23:46Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm300560g

Synthesis and Biological Evaluation of New Amino Acid and Dipeptide Derivatives of Neocryptolepine as Anticancer Agents

2012-05-18T14:52:55Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm300468t

Protein–Protein Interactions: Inhibition of Mammalian Carbonic Anhydrases I–XV by the Murine Inhibitor of Carbonic Anhydrase and Other Members of the Transferrin Family

2012-05-18T14:48:35Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm3004587

Identification of a Novel Family of BRAFV600E Inhibitors

2012-05-18T14:11:18Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm3004416

Water-Soluble Pyrazolo[4,3-e][1,2,4]triazolo[1,5-c]pyrimidines as Human A3 Adenosine Receptor Antagonists

2012-05-18T14:09:59Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm300323t

Topologically non-planar compounds

nobody@example.com — 2012-05-18T12:00:00Z

John MacCuish, from Mesa Analytics, pointed out that the MCS problem takes polynomial time if the graphs are planar. He writes: "Are there graphs in your likely sets, that are not planar? I have never seen a non-planar small molecule drug for example, but they may be out there. Tests for planarity are also in P. Of course it doesn't mean that solutions in P will be faster than the usual methods since N is small an the overhead for planar MCS may be large, such that N may need to be large for the planar method to beat the non-planar heuristics."

This leads to a couple of questions. One is, what is the shape of the run-time of my MCS algorithm? I don't actually know. Some tests take a very long time, but that's not enough to establish that it's polynomial for real-world compounds or exponential. I'm not going to research this now.

Another is, are their real-world small molecules which are non-planar, in the topological sense, and not the chemistry sense where all of the non-hydrogen atoms line on or near a plane?

Previous work in topologically non-planar compounds

A quick literature seach finds Synthesis of the first topologically non-planar molecule, which says:

We report here the synthesis and characterization of the tris-ether 2,5,14-trioxahexacyclo-[5.5.2.1.2^4,10.O^4,17.O^10,17]-heptadecane 3; this topologically unique (graph theory) molecule is prepared via a novel intramolecular rearrangement of either of two isomeric propellane spiro-epoxides, 1 and 2.

There's also Topological stereochemistry. 9. Synthesis and cutting "in-half" of a molecular Möbius strip by Walba, Homan, Richards, and Haltiwanger in New. J. Chem., 1993, 17, 661-681.

Quoting from the above link to Modern Physical Organic Chemistry by Ansyln and Dougherty:

We mention briefly here another topological issue that has fascinated chemists. For the overwhelming majority of organic molecules, we can draw a two-dimensional representation with no bonds crossing each other. ... It may seem surprising, but most molecules have planar graphs.

Recent efforts have produced chemical structures that successfully realize many interesting and novel topologies. A landmark was certainly the synthesis of a trefoil knot using Sauvage's Cu+/phenanthroline templating strategy.... Vögtle and co-workers have described an "all organic" approach to amide-containing trefoil knots, and have been able to separate the two enantiomeric knots using chiral chromatography. Another seminal advance in the field was the synthesis and characterization of a 'Möbius strip' molecule..."

So it's well-established that there are topologically non-planar structures. But are they in compound databases which I can access?

Searching PubChem for topologially non-planar compounds

Take a look at Scaffold Topologies II: Analysis of Chemical Databases by Wester, Pollock, Coutsias, Allu, Muresan and Oprea in PMC 2010 January 15., Published in final edited form as: J Chem Inf Model 2008 July; 48(7): 1311-1324.

Only 12 nonplanar and 2,099 spiro node topologies (all of which are planar) are present in the merged database. 9 of the nonplanar topologies are found only in PubChem and the total number of molecules represented by such topologies in the merged database is a mere 44, agreeing with Walba's assessment concerning the rarity of chemicals with nonplanar graphs.

This establishes that in 2008 there were no more than 44 topologically non-planar structures in PubChem. Can I find them?

I don't think any of the database search engines support this capability, so I need to write a program.

For that I need a method for planarity testing. Various sources say that the linear time algorithm is "widely regarded as being quite complex", but that one of the linear time planarity algorithms is part of Sage

Sage is a comprehensive mathematical software system, which uses Python. There's a pre-built binary distribution for my OS, which I downloaded. It includes Python, the IPython shell, and everything else it needs, so it really is a system and not a set of Python modules.

With Sage it's easy to make a graph and test for planarity.

% sage
----------------------------------------------------------------------
| Sage Version 5.0, Release Date: 2012-05-14                         |
| Type notebook() for the GUI, and license() for information.        |
----------------------------------------------------------------------
sage: d = {1: [2,3], 2: [3], 3: [4, 6], 4: [5]}
sage: g = Graph(d)
sage: g.is_planar()
True
sage: 
sage: k3_3 = {1: [2,3,4,5,6], 2: [3,4,5,6], 3: [4, 5, 6]}
sage: Graph(k3_3).is_planar()
False
sage:

The dictionary I use as input to "Graph" contains an upper-triangle connection matrix. So to test if a molecule is topologically planar, I just need to convert its connectivity information into a dictionary of the right form, turn the dictionary into a Graph, and test the graph for is_planar().

Roadblock! My Python 2.6 modules and Sage's 2.7 Python don't mix

The prebuilt binaries include its own Python distribution, and when you use "sage", it replaces the PYTHONPATH with its own settings. This means when I run sage I don't have access to the cheminformatics tools I've already set up for my system:

% sage
----------------------------------------------------------------------
| Sage Version 5.0, Release Date: 2012-05-14                         |
| Type notebook() for the GUI, and license() for information.        |
----------------------------------------------------------------------
sage: import rdkit
---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)

/Users/dalke/<ipython console> in <module>()

ImportError: No module named rdkit
sage: from openeye.oechem import *
---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)

/Users/dalke/<ipython console> in <module>()

ImportError: No module named openeye.oechem
sage: os.environ["PYTHONPATH"]
'/Users/dalke/ftps/sage/local/lib/python'

I tried to force it to include the right path, and that failed.

% printenv PYTHONPATH
/Users/dalke/ftps/openeye//wrappers/v2011.Oct.1/python/:/Users/dalke/envs/RDKit_2011_12-svn
% sage
   ...
sage: import sys
sage: sys.path.insert(0, "/Users/dalke/envs/RDKit_2011_12-svn")
sage: import os
sage: os.environ["DYLD_LIBRARY_PATH"]
'/Users/dalke/ftps/sage/local/lib:/Users/dalke/ftps/sage/local/lib/R/lib::/Users/dalke/ftps/openeye//wrappers/libs:/Users/dalke/envs/RDKit_2011_12-svn/lib:/Users/dalke/ftps/sage/local/lib/R/lib'
sage: from rdkit import Chem
Fatal Python error: Interpreter not initialized (version mismatch?)

------------------------------------------------------------------------
Unhandled SIGABRT: An abort() occurred in Sage.
This probably occurred because a *compiled* component of Sage has a bug
in it and is not properly wrapped with sig_on(), sig_off(). You might
want to run Sage under gdb with 'sage -gdb' to debug this.
Sage will now terminate.
------------------------------------------------------------------------
/Users/dalke/ftps/sage/spkg/bin/sage: line 312: 56741 Abort trap              sage-ipython "$@" -i

What happened here is that Sage ships with Python 2.7, while the locally installed cheminformatics toolkits use Python 2.6.

I decided to use another technique. I would have sage call out to another program to handle the cheminformatics. For each structure it will output a line containing the identifier, the SMILES, and the needed upper-triangle dictionary data structure. One line of output will look like:

('15', 'OC1C2(C(C3C(C4(C(CC3)CC(=O)CC4)C)CC2)CC1)C', {0: [1], 1: [2, 19],
 2: [3, 20, 17], 3: [4, 18], 4: [5, 9], 5: [6, 16], 6: [7, 15, 14],
 7: [8, 10], 8: [9], 10: [11], 11: [12, 13], 13: [14], 16: [17], 18: [19]})

All the sage code needs to do is read those lines, extract the data, pass the graph into Sage for analysis, and print out those which are non-planar.

Even this wasn't as easy as I thought. The PYTHONPATH environment variable persists in spawned processes, which the python subprocess I started uses the wrong PYTHONPATH. I ended up setting the environment variables myself - including PYTHONHOME - before it would work correctly:

import sys
import os
env = os.environ.copy()
env["DYLD_LIBRARY_PATH"] = "/Users/dalke/ftps/openeye//wrappers/libs:/Users/dalke/envs/RDKit_2011_12-svn/lib"
env["PYTHONPATH"] = "/Users/dalke/envs/RDKit_2011_12-svn"
env["RDBASE"] = "/Users/dalke/envs/RDKit_2011_12-svn"
env["PYTHONHOME"] = "/System/Library/Frameworks/Python.framework/Versions/2.6"

import subprocess

# Import the "Graph" constructor
from sage.all import *

# Use the version of Python for which RDKit was built to use
p = subprocess.Popen(["/usr/bin/python2.6", "pubchem_connectivity.py"],
                     env = env,
                     stdout = subprocess.PIPE)
                 
for i, line in enumerate(p.stdout):
    id, smiles, d = eval(line)
    G = Graph(d)
    if not G.is_planar():
        print "======= Found one!!!"
        print id, smiles
    if i % 100 == 0:
        sys.stderr.write("%d ...\r" % (i,))
        sys.stderr.flush()

Now I just need the "pubchem_connectivity.py" program to generate the connectivity information.

Call another program to generate the connectivity information

I've been using RDKit these days, because the funding I got for the MCS project said I needed to use RDKit. I usually use OEChem, which (among other things) has much faster structure parsers than RDKit. Since I'm going to read tens of millions of structures, I wanted a way to reduce the number of structures to process.

Now, if a graph has no cycles then it's always planar. Even if it has one, two, or three cycles, it's still impossible for it to be non-planar. The number of cycles in a single component graph is E-V+1, which is the number of edges (bonds), minus the number of vertices (atoms), plus 1. So a simple test is to exclude molecules where the number of bonds is less than three greater than the number of atoms.

Mind you, this is an exclusion test which rejects graphs that cannot be planar. There are plenty of molecules with dozens or rings which are topologically planar. Also, PubChem has plenty of records with multiple components, which means I may miss a few cases. But mind you, my goal is to find some non-planar structures in PubChem, not find all of them.

I had previously converted my local copy of PubChem to a set of compressed SMILES files. A few months ago I wrote a set of Ragel definitions for SMILES, which includes a demonstration of how to use count the number of atoms and bonds in a SMILES file. I can use that unmodified as a co-process to do high-speed counting for me.

import glob
import subprocess
import gzip
import sys
from collections import defaultdict
from rdkit import Chem

# Start a co-process to count the number of atoms and bonds in a SMILES string
p = subprocess.Popen(["/Users/dalke/opensmiles-ragel/smiles_counts"],
                     stdin = subprocess.PIPE,
                     stdout = subprocess.PIPE)

filenames = glob.glob("/Users/dalke/databases/pubchem/*.smi.gz")
for i, filename in enumerate(filenames):
    msg = "Processing %d/%d\n" % (i+1, len(filenames))
    sys.stderr.write(msg)
    sys.stderr.flush()

    for line in gzip.open(filename):
        # Read a line from the data file
        smiles, id = line.split()

        # Send it to the co-process
        p.stdin.write(smiles + "\n")
        p.stdin.flush()

        # Get the counts (looks like "atoms: 34 bonds: 38")
        line = p.stdout.readline()
        _, atoms, _, bonds = line.split()
        num_atoms = int(atoms)
        num_bonds = int(bonds)

        # Skip records which cannot be non-planar
        # (Note: assumes single component structures!)
        if num_bonds < num_atoms + 3:
            continue

        # Extract the topology into upper-triangle dictionary form
        mol = Chem.MolFromSmiles(smiles)
        if mol is None:
            continue
        d = defaultdict(list)
        for bond in mol.GetBonds():
            b1 = bond.GetBeginAtomIdx()
            b2 = bond.GetEndAtomIdx()
            if b1 < b2:
                d[b1].append(b2)
            else:
                d[b2].append(b1)

        # print to stdout so the Sage program can read it
        data = (id, smiles, dict(d))
        print repr(data)

BTW, if you haven't been paying attention, I have one process which tests if a SMILES string has enough bonds in it, another to convert structure information into a simple topology, and a third program to do the graph planarity test. "Bailing-wire and chewing gum!" to repeat an old phrase.

The structures!

It took many hours for my computer to chug along (while I slept). I ended up with 224 of the non-planar SMILES in the subset of 28.5 million PubChem structures I have on my machine. (In other words, bear in mind that this is not a complete search!)

Here are a few structures to show you what they look like:

That first structure, silicon nitride, is a ceramic, and which cannot be expressed in SMILES. (That is "[C]" is an equally poor SMILES representation of graphite as it is for diamond.) The others look like progressively more realistic chemical representations.

The non-planar structures were a bit too verbose to show as a SMILES file. Instead, here's the full list of identifiers, which you can easily use to get the structures yourself (or follow the hyperlink to look at the non-planar depictions).

390566, 498002, 636755, 3084099, 4868274, 5104674, 6712449, 10019039, 10882690, 10895017, 10898366, 10994362, 11027681, 11126005, 11131973, 11187418, 11340053, 11350583, 11360285, 11384163, 11407796, 11672382, 14381365, 14381430, 14381432, 14381435, 16132679, 16132681, 16132995, 16132999, 16133150, 16133259, 16133262, 16133397, 16133412, 16133413, ee16133414, 16133878, 16145580, 16146229, 16146230, 16148442, 16148609, 16148632, 16148888, 16148900, 16149007, 16149114, 16149222, 16149238, 16149361, 16149362, 16149482, 16149579, 16149602, 16149827, 16149958, 16150054, 16150193, 16150399, 16150419, 16150654, 16150658, 16150833, 16150994, 16151169, 16151337, 16151360, 16151567, 16151729, 16151960, 16152362, 16152566, 16152608, 16152699, 16152729, 16153098, 16153207, 16153649, 16154275, 16154327, 16154453, 16154584, 16154971, 16155013, 16155014, 16155015, 16155069, 16155075, 16155076, 16155130, 16155140, 16155152, 16155193, 16155194, 16155197, 16155399, 16155442, 16155443, 16155456, 16155607, 16155626, 16155630, 16155631, 16155632, 16155633, 16155652, 16155784, 16155884, 16155916, 16155917, 16155920, 16155972, 16156042, 16156080, 16156082, 16156145, 16156198, 16156260, 16156261, 16156264, 16156265, 16156312, 16156411, 16156413, 16156417, 16156432, 16156495, 16156539, 16156544, 16156545, 16156609, 16156610, 16156613, 16157557, 16214951, 17749011, 21597602, 21597607, 21597610, 21597611, 21770498, 22294696, 22835058, 22835161, 22835262, 22835624, 22835636, 22835637, 23327291, 23584643, 23726086, 23727886, 23955822, 24764125, 24770227, 24770228, 24770229, 24770290, 24770291, 24871221, 24940071, 25200029, 44239114, 44303783, 44303799, 44303821, 44382489, 44397933, 44397934, 44566282, 44566284, 44575206, 44575207, 44575208, 44592641, 44592642, 44592645, 44592646, 44592647, 44592648, 44592945, 44593582, 44606373, 44606374, 46882313, 46882314, 46882315, 46882316, 46882317, 46882318, 46882319, 46882320, 46882321, 46882322, 46882323, 46882324, 46882325, 46882326, 46882327, 46882328, 46891923, 46895835, 49799159, 49799160, 49873810, 50897242, 50900298, 50900299, 50900300, 50919058, 51004304, 51026319, 52945815, 52952313, 52952314, 52952315, 52952316, 52952317, 53468167, 53468168, 53468169, 53468170, 53468171

Still, having some SMILES on-hand is nice so here are some of the shorter SMILES strings. At the least, you can use these as test-cases for an MCS search engine, and perhaps force a linear-time planar-graph-only method to fail :)

O1C23C4C5(C16OCC7C4(CC(C2(OC(=O)C3(CC5C8C6CC=C9C8(C(=O)C=CC9)C)O)C)OC7=O)C)O 390566
O=C1N2C34N5CCC(C3CCC4(C1)C=CC5)c6c2cccc6 498002
O=C1N2C34N5CCC(C3CCC4(C1)C=CC5)c6c2cc(cc6)OC 636755
[Si]123N4[Si]56N1[Si]4(N25)N36 3084099
C123C45C16C24C6C7C(C35)CC(C(C7)C)C 4868274
O1C23C4C5(C16OCC7C4(CC(C2(OC(=O)C3(CC5C8C6CC=C9C8(C(=O)C=CC9)C)O)C)OC7=O)C)O 5104674
O1C23C4C5(C16OCC7C4(CC(C2(OC(=O)C3(CC5C8C6CC=C9C8(C(=O)C=CC9)C)O)C)OC7=O)C)O 6712449
O=C1N2C34N5CCC(C3CCC4(C1)C=CC5)c6c2cc(cc6)OC 10019039
O=C1N2C34N5CCC(C3CCC4(C1)C=CC5)c6c2cc(c(c6)OC)OC 10882690
O1C2C34C5C(CN(C3C(C5OC)c6c(cc(c(c6)OC)O)C4C1)CC)(C(C2)O)CO 10895017
O1c2c3cccc2Cc4c5c(ccc4)Cc6c7c(ccc6)Cc8c(c(ccc8)C3)OCCOCCN(CCOCCO5)CCOCCOc9c(cccc9)OCCOCCN(CCOCC1)CCOCCO7 10898366
O1C2C34C5C(C(C2)OC(=O)C)(CN(C3C(C5OC)c6c(cc(c(c6)OC)O)C4C1)CC)COC 10994362
C123C45c6c(cccc6)C17c8c(cccc8)C2(c9c(cccc9)C3(c1c4cccc1)c1c7cccc1)c1c5cccc1 11027681
OC1N2C34N5CCC(C3CCC4(C1)C=CC5)c6c2cc(c(c6)OC)OC 11131973
O1c2c3c(ccc2)OB4Oc5c6c(ccc5)OB1Oc7c(c(ccc7)O4)N63 11187418
C123C45C6C7C8C19C1C(C4C4C%10C22C%11C(C5C5C%12C33C(C6C5)C8CC5C9C(C2C(C35)CC%12%11)CC1%10)C4)C7 11340053
O1C2C34C5C(CN(C3C(C5OC)c6c(cc(c(c6)O)O)C4C1)CC)(C(C2)O)COC 11350583
C123C4C5C1C6C5(C4C26)C=CC#CC=CC78C9C1C7C2C1(C9C82)C=CC#CC=C3 11360285
C123C4C5C1C6C5(C4C26)C=CC#CC#CC=CC78C9C1C7C2C1(C9C82)C=CC#CC#CC=C3 11384163
O1C2C34C5C(CN(C3C(C5OC)c6c(cc(c(c6)OC)O)C4C1)CC)(C(C2)O)COC 11407796
O(c1cc2c(cc1OC)C34C56C27c8c(cc(c(c8)OC)OC)C5(c9c3cc(c(c9)OC)OC)c1c(cc(c(c1)OC)OC)C6(c1c7cc(c(c1)OC)OC)c1c4cc(c(c1)OC)OC)C 11672382
O(C12NC(=O)C3C4C56C1C7C8C59C15C6(C3C3C1C(C9C43)C#N)C2C7C5C8=O)C(=O)C 14381365
O1C2C3C45C67C2C8C3C9C42C6(C8C9=O)C3C4C7C(C5C4C2C3C#N)C1=O 14381430
O1C2C3C45C67C2C8C3C9C42C63C8C9OC(=O)C4C2C2C5C(C7C2C34)C1=O 14381432
BrC12C34C56C7(C8C9C5C5C3C9C1C8C(=O)OC1C2C2C4C(C6C2C71)OC5=O)Br 14381435
O1c2c3c4c5c6c7c3c(cc8c7c(cc6Oc9cc(ccc9)OCCOCCOc3cc1ccc3)C(=O)N(C8=O)C(CCCCCC)C)Oc1cc(ccc1)OCCOCCOc1cc(ccc1)Oc5cc1c4c(c2)C(=O)N(C1=O)C(CCCCCC)C 16214951
BrC12C34C56C7(C8C9C5C5C3C9C1C8C(=O)OC1(C2C2C4C(C6C2C71)OC5=O)Br)Br 21597602
BrC12C34C56C7C8C9C3C7C(=O)OC3C4C4C1C(=O)C(C5(C8C(C29)C(=O)OCC)Br)C4C63 21597607
BrC12C34C56C7C8C9C5C5C3C9C1C8C(=O)OC1C2C2C4C(C6C2C71)OC5=O 21597610
O1C2C3C45C67C2C8C3C9C42C6(C8C9O)C3C4C7C(C5C4C2C3C(=O)O)C1 21597611
O1C23OC(OC4C25C6C7(C1=O)C(C3OC(OC5C(=C)C4CC6)(C)C)C(CCC7)(C)C)(C)C 21770498
ClC1=CC2OC3C1C4OC5C2C4C3C5 22294696
O=C1N2C34N5CCC(C3CCC4(C1)C=CC5)c6c2cc(cc6)OC 23327291
O1c2cc3c4cc2OCCOCCOCCOc5c(cc6c(c5)c(c7c(c6C)cc8c(c7)OCCOCCOCCOc9cc2c(cc9OCCOCCOCCO8)C4(c4c(cccc4)C32C)C)C)OCCOCCOCC1 23584643
O1C2(OCC34C5C2(CCC3OC(=O)C67C4C(OC51)CC(C6)C(=C)C7=O)C)C 23727886
O=C1N2C34N5CCC(C3CCC4(C1)C=CC5)c6c2cc(c(c6)OC)OC 23955822
O1C2C3C45C6C1OC(C6(CCC4OC(=O)C37CC(C2)C(=C)C7=O)C)OC5 24764125
O=C1N2C34N5CCC(C3CCC4(C1)C=CC5)C6=C2CC(=C(C6)OC)OC 24871221
O1C2C34C56C(C(C2(C=C5)OC)C(O)(CCCC)C)CCN(C6Cc7c3c1c(cc7)O)CC4 44303783
O1C2C34C56C(C(C2(C=C5)OC)C(O)(CCCC)C)CN(C6Cc7c3c1c(cc7)O)CC4 44303799
O1C2C34C56C(C(C2(C=C5)OC)C(O)(CCCC)C)CCN(C6Cc7c3c1c(cc7)O)CC4 44303821
S(=O)(=O)(OCC12C3C4C(C1)CC(C3)CC4C2)N 44382489
O=C1N2C34N5CCC(C3CCC4(C1)C=CC5)c6c2cc(c(c6)OC)OC 44592945
O1C23C4C5(C16OCC7C4(CC(C2(OC(=O)C3(CC5C8C6CC=C9C8(C(=O)C=CC9)C)O)C)OC7=O)C)O 49873810
O=C1N2C34N5CCC(C3CCC4(C1)C=CC5)c6c2cccc6 50897242
O=C1N2C34N5CCC(C3CCC4(C1)C=CC5)c6c2cc(cc6)OC 50919058
O1C23C4C5(C16OCC7C4(CC(C2(OC(=O)C3(CC5C8C6CC=C9C8(C(=O)C=CC9)C)O)C)OC7=O)C)O 51004304
O=C1N2C34N5CCC(C3CCC4(C1)C=CC5)c6c2cc(cc6)OC 51026319

Feel free to leave a comment.

Design, Synthesis, and Evaluation of 3,5-Disubstituted 7-Azaindoles as Trk Inhibitors with Anticancer and Antiangiogenic Activities

2012-05-18T11:16:24Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm3002982

A Selective, Orally Bioavailable 1,2,4-Triazolo[1,5-a]pyridine-Based Inhibitor of Janus Kinase 2 for Use in Anticancer Therapy: Discovery of CEP-33779

2012-05-18T11:16:02Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm300248q

In Vitro Anticancer Activity of cis-Diammineplatinum(II) Complexes with β-Diketonate Leaving Group Ligands

nobody@example.com (Justin J. Wilson and Stephen J. Lippard) — 2012-05-18T05:00:04Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm3002857

Thiophene Bioisosteres of Spirocyclic σ Receptor Ligands: Relationships between Substitution Pattern and σ Receptor Affinity

2012-05-17T20:34:41Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm300302p

Discovery of a Novel Series of Potent and Orally Bioavailable Phosphoinositide 3-Kinase γ Inhibitors

2012-05-17T17:42:50Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm300403a

Design, Synthesis, and Antiviral Evaluation of Purine-β-lactam and Purine-aminopropanol Hybrids

2012-05-17T17:41:23Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm300383k

Structure–Activity Relationship Study of Selective Excitatory Amino Acid Transporter Subtype 1 (EAAT1) Inhibitor 2-Amino-4-(4-methoxyphenyl)-7-(naphthalen-1-yl)-5-oxo-5,6,7,8-tetrahydro-4H-chromene-3-carbonitrile (UCPH-101) and Absolute Configurational Assignment Using Infrared and Vibrational Circular Dichroism Spectroscopy in Combination with ab Initio Hartree–Fock Calculations

2012-05-17T17:40:46Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm300345z

Rational Evolution of a Novel Type of Potent and Selective Proviral Integration Site in Moloney Murine Leukemia Virus Kinase 1 (PIM1) Inhibitor from a Screening-Hit Compound

2012-05-17T17:40:45Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm3001289

Indole-2-carboxamides as Allosteric Modulators of the Cannabinoid CB1 Receptor

2012-05-17T17:40:43Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm201485c

Use of Small-Molecule Crystal Structures To Address Solubility in a Novel Series of G Protein Coupled Receptor 119 Agonists: Optimization of a Lead and in Vivo Evaluation

2012-05-17T14:33:42Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm300310c

INFERCNMR: A 13C NMR Interpretive Library Search System

nobody@example.com (Plamen N. Penchev, Klaus-Peter Schulz and Morton E. Munk) — 2012-05-17T13:23:20Z

Journal of Chemical Information and Modeling

DOI: 10.1021/ci200619y

Balancing Chemical Equations in ReactionMate Part 1: More Than Meets the Eye

nobody@example.com — 2012-05-17T07:00:00Z

Balancing chemical equations is one of the first things taught in most introductory chemistry classes. As such, it might seem as if everything there is to know about the topic was published 75 years ago or more by chemists long since gone.

To my surprise, I found this was simply not so.

This post, the first in a series, discusses why a cheminformatics fan like myself would even consider the problem of balancing chemical equations as a serious topic, and what a deeper understanding of the subject might mean for you.

Origins: ReactionMate

ReactionMate (App Store) at the moment is a little iOS app with big ambitions. It aims to become a useful companion to anyone studying, performing, or using chemical reactions.

One of the fundamental requirements for processing chemical reactions computationally is the automatic balancing of chemical equations, a function ReactionMate performs relatively well.

Given this foundation, there are many interesting directions the app could be taken, both in education and research. Hopefully, I’ll be discussing some of them later.

When I started working on ReactionMate, my main motivation was to better learn iOS development with Objective-C. A reaction balancer, so I thought, would be an easy project.

Although I did reach the first milestone and now have an app for sale to show for the effort, the path was much longer and darker than I imagined.

Balancing By Inspection

You may remember back to learning how to balance chemical equations in General Chemistry. A common method still taught is known as balancing by inspection. This explanation from Khan Academy is typical:

Although a fine approach for balancing simple equations by hand, balancing by inspection does not lend itself well to use in a software algorithm. I suspect that the same thing that makes balancing by inspection so difficult for students to master is the same thing that makes it unsuitable for use as algorithm: it’s hit or miss and then try again.

Balancing With Matricies/Linear Algebra

A more general and deterministic method for balancing chemical equations uses linear algebra. Given the constraint that the number of atoms of a given type on the left of the equation must equal the number of that type of atom on the right, it’s possible to set up a system of linear equations and use matrix operations to solve for coefficients. The video below shows how this works conceptually:

Issues

Implementing the necessary matrix algebra in Objective-C was not terribly difficult in a first pass. Coefficients for the first few chemical equations were found without problems.

However, as the number and complexity of tested equations grew, problems began to surface - each demanding a solution. The next installment will discuss some of the issues and how they were addressed.

Conclusions

Balancing chemical equations is a deep topic at the intersection between mathematics and chemistry. Balanced chemical equations also lie at the heart of many important areas in chemistry. With academic papers appearing as recently as a couple of years ago, this topic is full of surprises.

Development of LC-MS/MS-Based Receptor Occupancy Tracers and Positron Emission Tomography Radioligands for the Nociceptin/Orphanin FQ (NOP) Receptor

2012-05-16T19:50:35Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm201629q

New Synthetic Inhibitors of Fatty Acid Synthase with Anticancer Activity

2012-05-16T19:50:33Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm2016045

Introducing the LASSO Graph for Compound Data Set Representation and Structure–Activity Relationship Analysis

nobody@example.com (Disha Gupta-Ostermann, Ye Hu and Jürgen Bajorath) — 2012-05-16T18:44:51Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm3004762

Design, Synthesis, and Pharmacological Characterization of Indol-3-ylacetamides, Indol-3-yloxoacetamides, and Indol-3-ylcarboxamides: Potent and Selective CB2 Cannabinoid Receptor Inverse Agonists

2012-05-16T18:43:03Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm3003334

Discovery of the First Irreversible Small Molecule Inhibitors of the Interaction between the Vitamin D Receptor and Coactivators

2012-05-16T15:35:43Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm300460c

Synthesis and Nicotinic Receptor Activity of Chemical Space Analogues of N-(3R)-1-Azabicyclo[2.2.2]oct-3-yl-4-chlorobenzamide (PNU-282,987) and 1,4-Diazabicyclo[3.2.2]nonane-4-carboxylic Acid 4-Bromophenyl Ester (SSR180711)

2012-05-16T15:35:31Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm300030r

Disulfide Prodrugs of Albitiazolium (T3/SAR97276): Synthesis and Biological Activities

2012-05-16T15:35:28Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm3000328

A New Class of Highly Potent Matrix Metalloproteinase Inhibitors Based on Triazole-Substituted Hydroxamates: (Radio)Synthesis and in Vitro and First in Vivo Evaluation

2012-05-16T15:34:47Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm300199g

Protonation States of the Catalytic Dyad of β-Secretase (BACE1) in the Presence of Chemically Diverse Inhibitors: A Molecular Docking Study

nobody@example.com (Arghya Barman and Rajeev Prabhakar) — 2012-05-16T14:24:00Z

Journal of Chemical Information and Modeling

DOI: 10.1021/ci200611t

Structure-Guided Design of A3 Adenosine Receptor-Selective Nucleosides: Combination of 2-Arylethynyl and Bicyclo[3.1.0]hexane Substitutions

2012-05-16T13:50:06Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm300396n

Preparation of 3-Substituted-1-Isopropyl-1H-pyrazolo[3,4-d]pyrimidin-4-amines as RET Kinase Inhibitors

2012-05-16T13:48:03Z

Journal of Medicinal Chemistry

DOI: 10.1021/jm3003944