第17回 サマーカップinエコパの応援行った #ドッジボール

 長男が所属しているドッチボールチームの参加する大会、第17回 サマーカップinエコパ ということで、掛川のエコパアリーナまで応援に行ってきました。
http://www.shizuoka-dba.com/keikaku.html
 今年は低学年の入団者が多く、オフィシャル1チーム、ジュニア2チーム、計3チームでの参戦でした。長男はチームに所属してちょうど一年くらい。今回はジュニア、1,2年生メインのチームでアタッカーとして頑張っていました。
ドッジボールのジュニアは4年生以下の小学生で構成されます。4年生がいるチームと比べると今回のチームはまだまだ小柄な子が多く、予選リーグを突破することはかないませんでしたが、高学年相手に果敢にボールを投げアウトを取ったり、相手のボールをキャッチしている姿を見ると、家でがグズグズしてるが成長してきたなって思いました。
他の子も、みんな元気に頑張ってました。頼もしい!

 オフィシャルともう一つのジュニアはいずれも決勝リーグに勝ち残りましたが、残念ながら優勝には届かず。チームの仲間と力を合わせて勝利を目指す姿を見るのはこっちも元気をもらって、とても楽しいものです。

 豪速球を投げるエースがいるチームは迫力がありますし強いですがそれだけでは勝ち残れないのがまた面白いところ。5分という短い時間での逆転劇などスリルもあるし面白い。

まだまだ暑い夏が続きますが、体調を崩さず元気に楽しく頑張ってほしいものです。
私も負けないように走りこんで体力つけないと!

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Expand opportunity to accessing new building blocks

Building Block(BB) is key part of medicinal chemistry. Advances in science medicinal chemists can have opportunity to access novel BBs and they do not consider how to make BBs these days.
In my personal opinion, unique BB will be the strong point of the their company. So medchem need to pay a lot of effort to designing and synthesis of new BB. In particular we often introduce fluorine atom to improve target potency, ADMET and physchem properties of molecule however introduction of fluorine atom in the desired position of the molecule is difficult. It is worth if there are lots of commercial available fluorinated BBs.

By the way, recently risk sharing model such as HTS library sharing, collaborative research model is spreading idea among pharmaceutical companies.
I found new approach reported from pfizer.
Here is an effort of pfizer.
https://www.ncbi.nlm.nih.gov/pubmed/29571837
The article is focused on fluorinated BB. The authors developed cross-pharma vendor buying group. The Buying group seek a single vendor, purchase BB from the vendor.

The pros of the model is that the group company can access novel, unpublished 1g of BBs with effective cost and have opportunity to discuss nature, concept of new BBs. This system is not fully exclusive but has 6 months advantage before publication.

It can not only reduce the cost of accessing BB but also can reduce the accessibility of complex fluorinated BBs.

Joint purchase model seems work well for same scale size of big pharma I think. I would like to know that whether dose the system work well for mid- small- size of pharma.

Investigation of PAINS filter with pharmaceutical company data set

PAINS filter is well know molecular filter to remove promiscuous compounds for HTS.
Many computational tools implement PAINS filter as a drug like filter. I also use the filter but I do not confirm the reliability with our in house dataset.
Here is a report from researchers in Lilly about investigation of PAINS filter with their in-house dataset.
It’s worth to read I think. And also the author is also published about Lilly’s MedChem filter some years ago.
URL is below.
https://pubs.acs.org/doi/10.1021/acsmedchemlett.8b00097

They used their compounds and assay data reported XC50. And single point data was excluded from the analysis. Also they omitted compound with less than 80% pure.
The analysis performed with several assay format, Alpha Screen, ELISA, FilterBinding, Fluorescence Polarization, FRET etc.
As interestingly only 2/6 assay formats showed more than 1.5 odd ratio of PAINS filter enrichment. The assay format is AS and FRET. This enrichment comes from very few number of alerts such as anil_di_alk_A.

They mentioned that High Hill slope (they defined >2.0 is high) compounds show high enrichment.
I need to care about what kind of assay format will use, and Hill slope of dose response to make right decision.

Get 3D distance matrix with rdkit #RDKit

I updated rdkit of my env from 20180301 to 20180303 with anaconda. ;-)
When I want to get 3D distance matrix of the molecule I use Get3DDistanceMatrix method.
But I found that rdDistGeom.GetMoleculeBoundsMatrix returns almost same results.
3DDistance matrix is useful for feature of 3D QSAR.
I would like to use these method.

from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import rdDistGeom as molDG

mol = Chem.MolFromSmiles('CCC')
bm = molDG.GetMoleculeBoundsMatrix(mol)
bm
Out[]: 
array([[0.        , 1.524     , 2.51279063],
       [1.504     , 0.        , 1.524     ],
       [2.43279063, 1.504     , 0.        ]])

AllChem.EmbedMolecule(mol)
Out[]: 0

dm=AllChem.Get3DDistanceMatrix(mol)
dm
Out[]: 
array([[0.        , 1.50401361, 2.47848679],
       [1.50401361, 0.        , 1.50913087],
       [2.47848679, 1.50913087, 0.        ]])

mol = Chem.MolFromSmiles('c1ccncc1')

bm = molDG.GetMoleculeBoundsMatrix(mol)
bm
Out[]: 
array([[0.        , 1.38925641, 2.42894217, 2.80158084, 2.42894217,
        1.38925641],
       [1.36925641, 0.        , 1.38925641, 2.39940019, 2.81851281,
        2.42894217],
       [2.34894217, 1.36925641, 0.        , 1.35507278, 2.3697344 ,
        2.81851281],
       [2.68158084, 2.31940019, 1.33507278, 0.        , 1.35507278,
        2.39940019],
       [2.34894217, 2.64739923, 2.2897344 , 1.33507278, 0.        ,
        1.38925641],
       [1.36925641, 2.34894217, 2.64739923, 2.31940019, 1.36925641,
        0.        ]])

IAllChem.EmbedMolecule(mol)
Out[31]: 0

dm=AllChem.Get3DDistanceMatrix(mol)
dm
Out[]: 
array([[0.        , 1.38579572, 2.3376015 , 2.70976216, 2.34726355,
        1.38818637],
       [1.38579572, 0.        , 1.39124965, 2.30040952, 2.68623726,
        2.37414223],
       [2.3376015 , 1.39124965, 0.        , 1.35847264, 2.28377475,
        2.61995998],
       [2.70976216, 2.30040952, 1.35847264, 0.        , 1.35652537,
        2.3746108 ],
       [2.34726355, 2.68623726, 2.28377475, 1.35652537, 0.        ,
        1.3905889 ],
       [1.38818637, 2.37414223, 2.61995998, 2.3746108 , 1.3905889 ,
        0.        ]])

And also I would like to extract 3D pharmacophore feature from molecule.
Example is below.

import os
from rdkit import Geometry
from rdkit import RDConfig
from rdkit.Chem import AllChem
from rdkit.Chem import ChemicalFeatures
from rdkit.Chem.Pharm3D import Pharmacophore
FEAT = os.path.join(RDConfig.RDDataDir, "BaseFeatures.fdef")
featfact = ChemicalFeatures.BuildFeatureFactory(FEAT)
mol = Chem.MolFromSmiles('c1cccnc1')
AllChem.EmbedMolecule(mol)
feats = featfact.GetFeauresFromMol(mol)
for feat in feats:
    ...:     print(feat.GetFamily())
    ...:     pos = feat.GetPos()
    ...:     print(pos.x, pos.y, pos.z)
    ...:     
    ...:     
Acceptor
-1.042491325161116 -0.7212337219142626 -0.7060687598321927
Aromatic
2.7755575615628914e-16 -1.942890293094024e-16 -0.2252839133689341

RDKit has many useful features for chemoinformatics!

Is VR useful for drug discovery?

I read the article about the VR application in drug discovery. I felt it is very interesting approach because it allows chemists to see molecules directly.
https://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00544

Also there are many tools and services to use same approach. Now chemists can dive in to a protein pocket and look deeply around the site.
https://www.rdmag.com/article/2018/07/virtual-reality-could-be-used-drug-discovery-tool

Some articles said that VR makes drug design process more intuitively. Hmm… is it true? Of course the approach provides new opportunity to view the binding site or any other thing. It will be exciting and will be easy to understand 3D structure. Just like “Don’t think feel”…. It is opposite to AI( Machine Learning ) driven drug discovery. I think most of drug design process is not intuitive. And current VR system lacks the sense of touch the feel. User can not feel the repulsion or attraction between ligand – protein interaction directly.
If VR drug design works very well, it indicates there are many elements which are not still defined as descriptors or energy I think. (It just my opinion…)

From AI side, I found very interesting article about making “SAKE” with AI.
http://www.itmedia.co.jp/enterprise/articles/1803/05/news016.html
The article describes a Toji’s challenge to making “SAKE” with AI. Toji is the chief brewer at a sake brewery.
They collected many data from their brewing process and trained AI with the data.

These technologies are progressing rapidly, so I might be necessary to change my opinion soon.
VR and AI is very attractive and interesting area for me. I would like to follow the fashion.

Do we need measure metabolic stability in chiral form?

Recently the importance of rsp3 ratio is increasing because of accessing designed space, improving physchem properties such as solubility etc.
However, accessibility of chiral compound is difficult due to synthetic accessibility or lack to chiral separation conditions.
As you know, sometime biological activity is quite different between enantiomers. It is as same as ADMET properties. So, how about importance of the chirality in metabolic stability?
Today I found short letter from Merck’s researchers.
“Interpretation of in vitro metabolic stability studies for racemic mixtures”
https://pubs.acs.org/doi/10.1021/acsmedchemlett.8b00259
They analyzed in house DMPK data and conduct simulation. Finally they concluded that the risk of misinforming project teams through generation of metabolic stability data on racemic mixtures is low.

In the Figure 4 of the article shows that frequency of compounds which shows 10 times difference of metabolic stability between R and S enantiomers!

For QSAR modeler it is worth to know the data. BTW, for project member small difference of molecular properties are very important even if the difference is small (2 times). I think drug designer is required balance, to see things from a wide point of view and to see things from a specific view. I need improve myself more and more…

Label Free ubiquitin assay system

Here is a new report about ubiquitin pathway system.
https://doi.org/10.1016/j.chembiol.2018.06.004
Recently ubiquitin system is becoming attractive drug targets. Known assay systems such as ELISA and SDSPAGE has limitation for through put and FRET is depended on the fluorescent.
The author developed and reported new HTS system to overcome the issue.

They use MALDI-TOF (rapifleX) with N15-labeled ubiquitin for the assay. N15-labeled ubiquitin is used for internal standard.
rapifileX
https://www.bruker.com/products/mass-spectrometry-and-separations/maldi-toftof/rapiflex-maldi-tissuetyper/overview.html
This assay system detects consumption of mono-ubuquitin directly.

To detect the covalent binder that binds to cys in ubiquitins, they recommends to use (tris(2-carbox- yethyl)phosphine instead of DTT or beta-mercaptoethanole( BME) for conducting the enzymatic reaction. And they conduct the assay with high ATP concentration to reduce the likelihood of identifying ATP analogs as inhibitors.
Well-designed system.

They performed HTS assay with 1430 FDA approved compounds as library and MDM2, ITCH and HOIP as E3 ligase.

Finally they got some hit compounds.
resveratrol showed week UBE1/UBE2L3/HOIP pathway inhibition activity. Hmm…..
bendamustine shows high potency.
https://en.wikipedia.org/wiki/Bendamustine
It seems irreversible binder I think.

Label free high though put assay is useful to reduce the risk of false positive and analyze ligand-protein interaction directly.
Technology and science is moving so fast….