LEGO & SCRACH

子供の夏休みに合わせて地元でものづくり体験的なイベントがありました。パン屋さんだったり、銀行だったり、いろんな体験ができます。その中でプログラミングで機械の動作原理を知ろうというセッションがあったので応募したら当選しました。
ということで、子供に体験させてみました。
今回の講座ではLEGO Webdoというレゴブロックを使いました。これはセンサーやモーターがついておりそれらはPC上でコーディングすることでいろんな動作制御が可能です。言語はScrachなので子供でもわかりやすい。
http://www.rika.com/lego/wedo2_1
今回はコマとそれを回すスピナーを作って動かしてみようというものでした。
まずはレゴでコマとスピナーを作ります。
スピナーができてきた↓

コマを作ってそのあとはPCでコード作ります。コードといってもブロックを組み合わせるパターンですので直感的です。
今回のコードは
モーター回す=>音を鳴らす=>スピナーをコマから離したら=>モーター止める。
です。
↓完成して実行するところ

コード書いてもの作って動くまで体験できるって良いですね。
僕のこともの頃はこんなのなかったな。電子工作ではんだこて使って、お風呂の水位センサーとか作ってなった時嬉しかった。もの作って動くと感動するよなぁ。
LEGO奥が深いわ。

Advertisements

Create MMPDB ( matched molecular pair )!

Matched molecular pair analysis is very common method to analyze SAR for medicinal chemists. There are lots of publications about it and applications in these area.
I often use rdkit/Contrib/mmpa to make my own MMP dataset.
The origin of the algorithm is described in following URL.
https://www.ncbi.nlm.nih.gov/pubmed/20121045

Yesterday, good news announced by @RDKit_org. It is release the package that can make MMPDB.
I tried to use the package immediately.
This package is provided from github repo. And to use the package, I need to install apsw at first. APSW can install by using conda.
And the install mmpdb by python script.

iwatobipen$ conda install -c conda-forge apsw
iwatobipen$ git clone https://github.com/rdkit/mmpdb.git
iwatobipen$ cd mmpdb
iwatobipen$ python setup.py install

After success of installation, I could found mmpdb command in terminal.
I used CYP3A4 inhibition data from ChEMBL for the test.
I prepared two files, one has smiles and id, and the another has id and ic50 value.
* means missing value. In the following case, I provided single property ( IC50 ) but the package can handle multiple properties. If reader who is interested the package, please show more details by using mmpdb –help command.

iwatobipen$ head -n 10 chembl_cyp3a4.csv 
CANONICAL_SMILES,MOLREGNO
Cc1ccccc1c2cc(C(=O)n3cccn3)c4cc(Cl)ccc4n2,924282
CN(C)CCCN1c2ccccc2CCc3ccccc13,605
Cc1ccc(cc1)S(=O)(=O)\N=C(/c2ccc(F)cc2)\n3c(C)nc4ccccc34,1698776
NC[C@@H]1O[C@@H](Cc2c(O)c(O)ccc12)C34CC5CC(CC(C5)C3)C4,59721
Cc1ccc(cc1)S(=O)(=O)N(Cc2ccccc2)c3ccccc3C(=O)NCc4occc4,759749
O=C(N1CCC2(CC1)CN(C2)c3ccc(cc3)c4ccccc4)c5ccncc5,819161
iwatobipen$ head -n 10 prop.csv 
ID	STANDARD_VALUE
924282	*
605	*
1698776	*
59721	19952.62
759749	2511.89
819161	2511.89

mmdb fragment has –cut-smarts option.
It seems attractive for me! ;-)
”’
–cut-smarts SMARTS alternate SMARTS pattern to use for cutting (default:
‘[#6+0;!$(*=,#[!#6])]!@!=!#[!#0;!#1;!$([CH2]);!$([CH3]
[CH2])]’), or use one of: ‘default’,
‘cut_AlkylChains’, ‘cut_Amides’, ‘cut_all’,
‘exocyclic’, ‘exocyclic_NoMethyl’
”’
Next step, make mmpdb and join the property to db.

# run fragmentation and my input file has header, delimiter is comma ( default is white space ). Output file is cyp3a4.fragments.
# Each line of inputfile must be unique!
iwatobipen$ mmpdb fragment chembl_cyp3a4.csv --has-header --delimiter 'comma' -o cyp3a4.fragments
# rung indexing with fragmented file and create a mmpdb. 
iwatobipen$ mmpdb index cyp3a4.fragments -o cyp3a4.mmpdb

OK I got cyp3a4.mmpdb file. (sqlite3 format)
Add properties to a DB.
Type following command.

iwatobipen$ mmpdb loadprops -p prop.csv cyp3a4.mmpdb
Using dataset: MMPs from 'cyp3a4.fragments'
Reading properties from 'prop.csv'
Read 1 properties for 17143 compounds from 'prop.csv'
5944 compounds from 'prop.csv' are not in the dataset at 'cyp3a4.mmpdb'
Imported 5586 'STANDARD_VALUE' records (5586 new, 0 updated).
Generated 83759 rule statistics (1329408 rule environments, 1 properties)
Number of rule statistics added: 83759 updated: 0 deleted: 0
Loaded all properties and re-computed all rule statistics.

Ready to use DB. Let’s play with the DB.
Identify possible transforms.

iwatobipen$ mmpdb transform --smiles 'c1ccc(O)cc1' cyp3a4.mmpdb --min-pair 10 -o transfom_res.txt
iwatobipen$ head -n3 transfom_res.txt 
ID	SMILES	STANDARD_VALUE_from_smiles	STANDARD_VALUE_to_smiles	STANDARD_VALUE_radius	STANDARD_VALUE_fingerprint	STANDARD_VALUE_rule_environment_id	STANDARD_VALUE_counSTANDARD_VALUE_avg	STANDARD_VALUE_std	STANDARD_VALUE_kurtosis	STANDARD_VALUE_skewness	STANDARD_VALUE_min	STANDARD_VALUE_q1	STANDARD_VALUE_median	STANDARD_VALUE_q3	STANDARD_VALUE_max	STANDARD_VALUE_paired_t	STANDARD_VALUE_p_value
1	CC(=O)NCCO	[*:1]c1ccccc1	[*:1]CCNC(C)=O	0	59SlQURkWt98BOD1VlKTGRkiqFDbG6JVkeTJ3ex3bOA	1049493	14	3632	5313.6	-0.71409	-0.033683	-6279.7	498.81	2190.5	7363.4	12530	-2.5576	0.023849
2	CC(C)CO	[*:1]c1ccccc1	[*:1]CC(C)C	0	59SlQURkWt98BOD1VlKTGRkiqFDbG6JVkeTJ3ex3bOA	1026671	20	7390.7	8556.1	-1.1253	-0.082107	-6503.9	-0	8666.3	13903	23534	-3.863	0.0010478

Output file has information of transformation with statistics values.
And the db can use to make a prediction.
Following command can generate two files with prefix CYP3A-.
CYP3A_pairs.txt
CYP3A_rules.txt

iwatobipen$ mmpdb predict --reference 'c1ccc(O)cc1' --smiles 'c1ccccc1' cyp3a4.mmpdb  -p STANDARD_VALUE --save-details --prefix CYP3A
iwatobipen$ head -n 3 CYP3A_pairs.txt
rule_environment_id	from_smiles	to_smiles	radius	fingerprint	lhs_public_id	rhs_public_id	lhs_smiles	rhs_smiles	lhs_value	rhs_value	delta
868610	[*:1]O	[*:1][H]	0	59SlQURkWt98BOD1VlKTGRkiqFDbG6JVkeTJ3ex3bOA	1016823	839661	C[C@]12CC[C@@H]3[C@H](CC[C@H]4C[C@@H](O)CC[C@@]43C)[C@@H]1CC[C@H]2C(=O)CO	CC(=O)[C@@H]1CC[C@H]2[C@H]3CC[C@H]4C[C@@H](O)CC[C@]4(C)[C@@H]3CC[C@@]21C	1000	15849	14849
868610	[*:1]O	[*:1][H]	0	59SlQURkWt98BOD1VlKTGRkiqFDbG6JVkeTJ3ex3bOA	3666	47209	O=c1c(O)c(-c2ccc(O)c(O)c2)oc2cc(O)cc(O)c12	O=c1cc(-c2ccc(O)c(O)c2)oc2cc(O)cc(O)c12	15849	5011.9	-10837
iwatobipen$ head -n 3 CYP3A_rules.txt 
rule_environment_statistics_id	rule_id	rule_environment_id	radius	fingerprint	from_smiles	to_smiles	count	avg	std	kurtosis	skewness	min	q1	median	q3	max	paired_t	p_value
28699	143276	868610	0	59SlQURkWt98BOD1VlKTGRkiqFDbG6JVkeTJ3ex3bOA	[*:1]O	[*:1][H]	16	-587.88	14102	-0.47579	-0.065761	-28460	-8991.5	-3247.8	10238	23962	0.16674	0.8698
54091	143276	1140189	1	tLP3hvftAkp3EUY+MHSruGd0iZ/pu5nwnEwNA+NiAh8	[*:1]O	[*:1][H]	15	-1617	13962	-0.25757	-0.18897	-28460	-9534.4	-4646	7271.1	23962	0.44855	0.66062

It is worth that the package ca handle not only structure based information but also properties.
I learned a lot of things from the source code.
RDKit org is cool community!
I pushed my code to my repo.
https://github.com/iwatobipen/mmpdb_test

original repo URL is
https://github.com/rdkit/mmpdb
Do not miss it!

3d conformer fingerprint calculation using RDKit # RDKit

Recently, attractive article was published in ACS journal.
The article describes how to calculate 3D structure based fingerprint and compare some finger prints that are well known in these area.
New method called “E3FP” is algorithm to calculate 3D conformer fingerprint like Extended Connectivity Fingerprint(ECFP). E3FP encodes information only atoms that are connected but also atoms that are not connected.
http://pubs.acs.org/doi/abs/10.1021/acs.jmedchem.7b00696

The author showed several examples. Very similar in 2D but not similar in 3D and vice versa.
Also compare E3FP similarity and ROCS score( TANIMOTO COMBO ) and showed good performance.
I was interested in the fingerprint. Fortunately, the author published the code in Anaconda cloud!!!!!!!
Install it and use it ASAP. ;-D
I am mac user, so installation is very very very easy! Just type.
I found some tips to use the package.
At first, molecules need _Name property to perform calculation.
Second, mol_from_sdf can read molecule from sdf but the function can not read sdf that has multiple molecules. So, I recommend to use molecule list instead of SDF.

conda install -c sdaxen sdaxen_python_utilities
conda install -c keiserlab e3fp

I used CDK2.sdf for test.
E3FP calculates unfolded finger print. But it can convert folded fingerprint and rdkit fingerprint using flod and to_rdkit function.

%matplotlib inline
import pandas as pd
import numpy as np
from rdkit import Chem
from e3fp.fingerprint.generate import fp, fprints_dict_from_mol
from e3fp.conformer.generate import generate_conformers
from rdkit.Chem.Draw import IPythonConsole
from rdkit.Chem import Draw
from rdkit.Chem import DataStructs
from rdkit.Chem import AllChem
IPythonConsole.ipython_useSVG=True
# this sdf has 3D conformer, so I do not need to generate 3D conf.
mols = [ mol for mol in Chem.SDMolSupplier( "cdk2.sdf", removeHs=False ) ]
fpdicts = [ fprints_dict_from_mol( mol ) for mol in mols ]
# get e3fp fingerprint
# if molecule has multiple conformers the function will generate multiple fingerprints.
fps = [ fp[5][0] for fp in fpdicts]
# convert to rdkit fp from e3fp fingerprint
binfp = [ fp.fold().to_rdkit() for fp in fps ]
# getmorganfp
morganfp = [ AllChem.GetMorganFingerprintAsBitVect(mol,2) for mol in mols ]

# calculate pair wise TC
df = {"MOLI":[], "MOLJ":[], "E3FPTC":[], "MORGANTC":[],"pairidx":[]}
for i in range( len(binfp) ):
    for j in range( i ):
        e3fpTC = DataStructs.TanimotoSimilarity( binfp[i], binfp[j] )
        morganTC = DataStructs.TanimotoSimilarity( morganfp[i], morganfp[j] )
        moli = mols[i].GetProp("_Name")
        molj = mols[j].GetProp("_Name")
        df["MOLI"].append( moli )
        df["MOLJ"].append( molj )
        df["E3FPTC"].append( e3fpTC )
        df["MORGANTC"].append( morganTC )
        df["pairidx"].append( str(i)+"_vs_"+str(j) )

The method is fast and easy to use. Bottle neck is how to generate suitable conformer(s).
Readers who interested in the package, please check the authors article.

I pushed my sample code to my repo.
https://github.com/iwatobipen/3dfp/blob/master/sample_script.ipynb