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Chemo-informatics - Science topic

Chemo-informatics is the use of computer and informational techniques, applied to a range of problems in the field of chemistry.
Questions related to Chemo-informatics
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Hi all,
I'm a master student working with computational molecular fragmentation and docking.
I did a virtual screening using autodock vina of some thousends of molecules, and I'm wondering if there's a solution to fragment the molecules based on the interactions observed on the docking poses. In the end I want obtain the smiles of the fragment, the AA the fragment is interacting and the type of interaction.
Is there any methods to do this?
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I handle large amounts of data on chemical substances. There is unique information for each chemical, but there is repetitive information that is required to be prepared and processed.
I do not have much knowledge of coding, automated functions in Excel, or even AI tools for these matters. I only know one Macro feature in Excel but that is all I know. The pace and amount of data is just increasing over the months. So, I wonder where to start leveraging existing tools that I can use for such tasks.
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Hi Stuti Ahuja thanks for your answer!
Hi Alisa Gorislav ! Thanks for your answer and resources. I will check them out.
Best,
John
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Which package do you use in R for general chemoinformatics, like handling molecular formulas, computing weights and mass to charge ratios, maybe even handling adducts etc.? Ideally something well maintained and with little dependencies.
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The depedency to java is quite annoying but clearly better than the rest.
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Hi all,
I'm a master student working with computational molecular fragmentation and docking.
I did a virtual screening using autodock vina of some thousends of molecules, and I'm wondering if there's a solution to fragment the molecules based on the interactions observed on the docking poses. In the end I want obtain the smiles of the fragment, the AA the fragment is interacting and the type of interaction.
Is there any methods to do this?
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hi Gautam Bhardwaj, thanks for yout answer. I used qsar models too for the prediction of activity, using fingerprints and molecular descriptors. But the idea here is really to have the fragments, the AA they are interacting and the type of interaction. The methods of fragmentation I know are only smarts-based or rules-based, and I'm looking for a structure-based methods
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Its my very first attempt when I am learning chemoinformatics while I am working on gromacs there is a warning sign if anybody can provide me with guidance will be great.
The warning states this : WARNING: Empty diagonal for a 3-dimensional periodic box
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What happens if you add a -d 1.0 and -bt cubic to the command?
aka
gmx editconf -f AZD0156.pdb -d 1.0 -bt cubic -o AZ0156.gro
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Hello all
When I was filtering a chemical database for virtual screening using open babel software, I got this error message few times "Fail to set stereochemistry as unable to find available bond".
Herein, I have two questions:
1. Is it a limitation of open babel or a problem with the database preparation work? Any suggestions to solve this issue?
2. If this issue happens to a molecule that already passed the filter criterion (being < 400 Da), does the molecule got written to the output file?
Thnx
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During drawing the chemical structure(s), add stereochemistry properties and 3D input with H-bond. Additionally, validate the structure error.
If you face repeated problems, then take help 1. PyMOL, 2. BIOVIA Discovery Studio
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I want to understand what is the significance of fukui functions and in what way it helps in predicting the vibrational structure of a molecule. kindly help me in this regard.
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Dear all RG colleagues,
UCA-FUKUI software is a good supporting software. Nice to share an outstanding video about the combination of UCA-FUKUI, Gaussian, and Gaussview to calculate Fukui functions as follow:
Have a good day!!!@@@
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Hi everyone! I'm trying to work on the acquisition of the Raman Spectra of a leaf section using Confocal Raman Spectroscopy. The samples to be used are pure, dried, and powdered leaf samples. I am going to use a 785 nm laser source.
However, the only thing I get was a spectra with no peaks or it is strongly masked by fluorescence. Do you have any tricks/sample preparations to avoid the fluorescence because I'm afraid that it covers the raman signal or enhance the Raman Signal because the compounds might have a relatively weak Raman Signal compare to the background signal and the fluorescence? Are there any sample preparations that can be done without the use of water or an immersion objective like the use of solid matrices which can be mixed with the sample? Thank you. 
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All of the suggesttions can be not feasible for John. I guess Raman microscope is what he has had and he needs to obtain Raman peaks out of the strong fluorescence background.
John, may I ask what is the model of your equipment? In general, you could try different configuration to enhace Raman peaks and reducing fluorescence background: objective lens, generally the larger magnifier the better; the shorter exposure time with larger acquisition numbers; adjusting sampling focal point, the best Raman response obained not always from the focused sample; sampling on larger particles/or particulates, the larger particles the smaller surface area and weaker fluorescence.
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we didn't find any specific TLR7 specific antagonist . maximum are dual antagonist of TLR7 and TLR9 antagonist. Is there any specificity on the protein active site? what was the reason for that?
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We are also having the same question: is there a commercial available antagonist specific for human TLR7?
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I am interested in software, databases and web servers in the field of chemoinformatics or pharmacoinformatics . We are trying to collect and maintain these resources at our web site http://crdd.osdd.net/ . Your information will be useful for us as well as for scientific community as we in process of developing repository of freeware in above fields.
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You can check Alvascience solutions for chemoinformatics and QSAR (https://bit.ly/36rAMnH).
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Recently, Researcher using Bioinformatics and Chemoinformatics for Drug development for covid-19, what are the limitations of in computational analysis vs. experimental?
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Hi Dr Abu Turab Naqvi, this is important to mention that Covid-19 viral strains are highly mutable. The in silico methods used for drug discovery makes predictions only. Hence, the viral strain in question should be well characterized primarily. The structural proteins and non-structural proteins are to be identified as drug targets, then the simulation studies can be made. This is further to mention that instead of going for antiviral drug targets, it is necessary to go for the identification of epitopes or stable antigens, which may be attenuated for preparation of active vaccines or processing of preformed antibodies as passive vaccines. Plasma therapy is based upon the application of plasma containing immunoglobulins which are available in recovered patients infected with patients. These immunoglobulins are the expression of class-switched genes in response to the evasion of a specific Covid 19 strain. The Ag-Ab simulation studies may support the in vitro studies.
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Basically I want to know what Bragg reflexes I can access with my goniometer.
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I'm trying to generate CGR and in order to do so I want to use the Marvin Sketch auto mapper function on a bunch of reactions, stored as reaction smarts. But in order to use the auto mapper function from Marvin sketch I have to pass the reactions as rxn files or as a RDF file.
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Alright, thank you guys!
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I am performing a molecular dynamics simulation from CRBN protein (PDB ID: 4tz4), which contain a ZN binding site composed of four cysteines. The main goal is understanding the chemical and biological changes in the Zn binding site from this protein when Cys391-to-Arg (C391R) substitution occur. For this, we performed the geometry optimization of the complex of four cysteines and Zn using DFT calculations on Gaussian 16 suite. Then, we put the coordinates obtained through DFT on the PDB format, and we will perform molecular dynamics simulation to obtain results in the perspective of the whole protein.
Now, I am using Gromacs to perform the MD simulation, but there is an error on my simulation during the NVT stabilization. This is the error message:
"Back Off! I just backed up step0c.pdb to ./#step0c.pdb.1# Wrote pdb files with previous and current coordinates ^Mstep 0 WARNING: Listed nonbonded interaction between particles 4204 and 4221 at distance 4.402 which is larger than the table limit 2.029 nm.
This is likely either a 1,4 interaction, or a listed interaction inside a smaller molecule you are decoupling during a free energy calculation. Since interactions at distances beyond the table cannot be computed, they are skipped until they are inside the table limit again. You will only see this message once, even if it occurs for several interactions.
IMPORTANT: This should not happen in a stable simulation, so there is probably something wrong with your system. Only change the table-extension distance in the mdp file if you are really sure that is the reason."
I am thinking about the reason for this error, and this could be because the force field which I am using to perform the simulation (amber03), but I am not sure about that. It is proper to mention that the atoms in which there is the error are the C of the residues (Lys324 and Gly325) which are between two of the Cys that form the Zn binding site.
Please, can anyone help me with this issue? Thank you very much
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Sebastian Ayala-Ruano I was checking the system and the C391R substitution looks pretty tough for the system considering that C391 is one of the "internal" cysteines... seems like a "loss of function mutation" and in my opinion with the mutagenesis module in pymol you could shown that there is no way to have an Arg residue in that position without having steric clashes...
hope this helps!
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I am deciding whether to buy a license for Dragon (https://chm.kode-solutions.net/products_dragon.php), but I wanted to check if there are any free alternatives that compute most of these features.  I have used Mordred (https://github.com/mordred-descriptor/mordred) and PaDEL, but these only provide a small subset of those available in Dragon, and haven't been as valuable for predictive modeling.  Is there anything free (and preferably open source) that provides anything close to the coverage of Dragon?  
#chemoinformatics #computational #chemistry #qsar
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RDKit is a free cheminformatics tool. You can easily play with molecules and calculate 2D and 3D descriptors in Python.
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I have to calculate the molecular descriptor using freeware. Which software is best for calculating the molecular descriptor? Or does anyone have a license for any software(paid)? Please help me out.
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For those who have some basic experience of Python or are willing to acquire basic Python notions:
RDkit is a quick and free way to get a bunch of simple descriptors, which range from 1D to 3D. Also, note that if your molecular names are not completely niche, you can easily convert them into SMILES using the cirpy library. I had a good experience with this library: from a bunch of about 4000 molecules, it could convert name to smiles automatically for about 3800 of them and I had just 200 to convert manually. Once you have SMILES of your molecules, descriptors can be easily calculated with RDkit.
Check the list of descriptors, 3D descriptors and fingerprints here:
Note that fragment descriptors, for group contributions, can also be computed once suitably defined.
I recommend using the last version of Python as installed with Anaconda. This way, you will have the least possible hassle with updating libraries. Personally I simply use notepad++ as the text editor and I execute my script using the windows command line. This way, the entire workflow is free.
As an example, here is a simple script I've done that converts a bunch of names (1 by line) given in names.txt file into their SMILES and prints them on the command line output.
#!/usr/bin/python
# -*- coding: Latin-1 -*-
# ----- Get SMILES from list of names -----
#
#
# (c) 2019, University of Nanjing
#
# A: Theophile Gaudin
#
import cirpy
with open("names.txt") as file:
data = file.readlines()
for molecule in data:
try:
name = molecule.rstrip("\n\r")
SMILES = cirpy.resolve(name, 'smiles')
formula = cirpy.resolve(SMILES, 'formula')
Mw = cirpy.resolve(SMILES, 'Mw')
print(name, ";", SMILES, ";", formula, ";", Mw)
except:
print("none")
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While going through various articles, I found the common practice of selecting a particular chain over others if a protein is in multimeric form. In this regard, I have the following questions
1) Is there any rule regarding which chain should be kept and which one should be deleted.
2) Since we mimic biological molecule in docking study to find the binding mode then how far it is valid to select only particular chain of the protein. Moreover, what if we took the whole protein pdb for docking study?
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First and foremost, one is supposed to check the physiological state of the protein prior to docking/MDS. Subsequently, one can decide which chain to keep and which to discard. There is no harm in keeping the entire protein chains while performing docking/MDS, except the computational cost. One more thing, better mimicking of the biological molecules can be done by performing MDS.
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Hi there ! I'm loking for a tools that is able calculate a similarity score for each term of 2 list name and give me the top 10 similarity score he found. For exemple List 1 : (-)-epigallocatechin (+)-catechin (pyro)catechol sulfate 3',4'-Dimethoxyphenylacetic acid List 2 : 3',3'-Dimethoxy-phenylacetic acid catechin (epi)gallocatechin catechol sulfate Results (-)-epigallocatechin VS (epi)gallocatechin - Score = 0.9 (very similar) (-)-epigallocatechin VS (+)-catechin - Score = 0.5 (+)-catechin VS catechin - Score = 0.9 etc etc ... Thanks a lot for your great help.
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In order to determine the similarity of arbitrary terms, I would transform them into their sets (alternatively Bag-Of-Words BoW) and would use Jaccard similarity on the two sets (resp. an weighted derivation for BoW).
However, better suited would be to transform them into a set of their syllables, that would respect the domain language better. But I don't know whether that can be done for chemical compounds or molekules easily.
Hope that helps for first attempt.
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I have a large set of molecules and I'd like to assess its diversity in terms of molecular structure, assuming I'm designing a screening library. My question is not what tools to use, but rather how should I interpret the results?
Let's say I choose clustering. How many clusters indicate a diverse library?
"Modern Approaches in Drug Discovery" suggests comparing Morgan fingerprints against Tanimoto distance matrix with multidimensional scaling applied, among others. But what will it get me?
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Here is one way to assess the diversity of a library:
1. Find out an approximate similarity threshold for the metric/descriptors used in the diversity analysis. For example, for the popular MACCS keys, the Tanimoto threshold is ~0.75; for ECFP4 (circular fingerprints implemented in Pipeline Pilot), the Tanimoto threshold is ~0.5.
2. Calculate all pairwise similarities for your library compounds.
3. Plot and analyze the probability density function for these similarity values. An ideally diverse library would not feature any pairwise similarity higher than the threshold (that is, every molecule is dissimilar from each other). For a fully redundant library, all pairwise distances would be higher than the threshold (that is, every molecule is similar to each other).
4. It is you call than to decide which kind of library do you need? That is, how much of redundancy would you accept? Keep in mind that an ideally diverse library is not well suited for screening. Ideally, every compound would be a member of a small family (cluster) of 10-20 compounds. If more than one family members show some activity in a screen, this may be considered as an indirect confirmation that these are true actives. Also, activity values for the family members can be used for a preliminary structure-activity relationships (SAR) analysis.
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Dear Colleague,
Greetings from Department of Computer Science, Central University of Kerala
Our Sincere apologies if you receive multiple copies of this email from different sources.
It is a great pleasure to inform you that Department of Computer Science, Central University of Kerala is organizing an International Conference Intelligent Computing and Signal Processing (IntelliSig2019) during March 8-9, 2019. Many eminent speakers in the area of computer science are likely to join the conference. We expect your valuable presence and participation in the conference.
Papers are invited on the following topics and other closely related areas.
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Fuzzy/Neural systems, Deep learning, IOT
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All the best wishes for the program
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I would like to decide whether a given internal coordinate (angle or dihedral angle) is totally symmetric, i.e., E(y1,y2,...,yi+delta,y[3N-5/6])=E(y1,y2,...,yi-delta,y[3N-5/6]) for any delta, where E is the total electronic energy, y1,y2,...,y[3N-5/6] are the internal coordinates, and yi is the selected coordinate. Is there any chance to prove that yi is totally symmetric by using only the molecular structure?
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Thank you again. My molecules are weakly bound van der Waals dimers (A.B) and, of course, I try to study their coordinates in those regions where the intermolecular interactions can be enforced.
Let pA and pB are two arbitrary planes of monomers A and B, respectively. If I understand correcly what you wrote, the rotation around an arbitrary axis R is symmetric only in the case that (a) R is contained both by pA and pB, and (b) pA and pB totally overlap. Then, for a given R, I have to find a plane of A, pA, that contains R, and search for a plane of B, pB, that coincides with pA. Is it a good "algorithm" to inspect whether R is symmetric?
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We are working on computational aspect of target screening for effective diagnostic. We are combining systems pharmacogenomics, chemoinformatics, systems biology, graph theory, structural biology aspects majorly. More focused towards interdisciplinary approaches. If you are interested to be a part of project and contribute to scientific community through our approach, you are most welcome.
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I would like to contribute and expecting an outsanding result for a well chosen target.
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I am training machine learning models to predict the binding affinity of small molecules against a protein receptor. During the training set preparation I remove very small and very big moelcules, as they are more likely to binding non-specifically to the receptor and abolish its funtion. Currently, I use z-score thresholds (-2.5 for the very small and 2.5 for the very big molecules). However, the z-score depends each time on the distribution of MW in the available data and hence I believe that actual MW lower and upper thresholds would be more accurate. According to your experience, what should be these MW thresholds?
PS: please don't point me to Lipinski's rule of 5 (180 to 500) or some related rule. When training a machine learning model on known data you have to be more "generous" otherwise you will discard lots of precious experimental data.
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True enough, but you're reducing your training diversity by limiting molecular weight as well. Besides, at four-sigma, assuming well distributed data, you're getting rid of less than 1% of the samples. I'd expect more like 5% for real data, but still - if your dataset diversity hinges on that small number of samples, something is probably very wrong somewhere.
Still - you appear to want somebody to give you arbitrary molecular weight values, but aren't happy with the Lipinski set. Literally nobody can do better than that, ESPECIALLY without knowing your binding site. If you don't like just eliminating the molecules most unlike the others, either for all properties or for weight alone, the best I can suggest is to do an alpha-sphere search in the binding site, figure the amount of, say, diamond that would fit into that volume, and use that as, say, 80% of your maximum mass. Then figure that anything that doesn't weigh at least, oh, 20% of that mass, won't make enough contacts to matter. You'll probably have to fiddle with that more than a little bit, but it would at least give you an algorithmic approach to your cutoffs, rather than arbitrary numbers.
As for smiles string lengths - I'd just do the mW calculation properly. It's a simple calculation, quick, and avoids the problem of strings with very little markup 'weighing' the same as strings with lots of rings, branch points, etc.
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Hi there,
I have about ~1 million compounds to cluster. While calculating the fingerprints were relatively fast using Canvas on a desktop computer, I found clustering (by K-Means) very slow. For example, clustering them into 128 clusters took ~10 hrs, and I wonder if this would increase exponentially as I increase the number of clusters.
So are there alternative/faster methods to cluster a library of compounds into arbitrary number of clusters?
Thanks!
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You can right python scripts to do the clustering. There are many options as it is a common task. I would try scikit-learn packages first, and then the pyspark if necessary.
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Today, there is a huge body of scientific data that can probably solve all the problems of mankind. However, even a group of scientists can not physically analyze this Big Data. In addition, people are subjective in judgment and often do not notice the negative consequences of some innovation...
So scientists inevitably set local goals. Can the Artificial Intelligence setting "absolute" goals for the scientific community?
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This is a very difficult question to answer. First at all we must define "the research" and second what do we mean by "Artificial Intelligence". AI is not a science in the traditional sense. It is a collection of approaches modeled inside a field of one science (mathematics, physics, biology .....social sciences......) or inside a fiels of two or several sciences (examples : genetic algorithms, convolutional neural network). These approaches raise problems and try to solve them. Once a model proposed, it is transformed in an algorithm and this algorithm is injected on the machine. If the machine can take in charge a number of tasks more and more "sophisticated" that human brain can solve, it become an artificial intelligence system.
So, it's not only the problem of data, it is the problem of the model, the approach, the algorithm and its running on the machine.
I think that is the epistemological path for an AI system to be an AI system.
As for the research, it is a research on all vertex of above path. The difference is that there are different types pf research. The research in mathematics, are different from the research in computer science and eve in the different sub-field of computer science.
This is my position.
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My aim is to cluster a set of molecules coming from a virtual screening (in the order of thousands of compounds). I want to cluster them by chemical similarity using hierarchical clustering. The distance metric used is the Tanimoto index. Ideally, I want to identify a limited number of clusters (10-30) that can be conveniently represented by the molecule corresponding to the centroid of each cluster.
My problem is that the results of hierarchical clustering seem to be strongly dependent on the linkage algorithm chosen. The optimal number of clusters predicted (in terms of Kelley criterion) seem to vary a lot according to different algorithms tested. Eventually, I am afraid that the results of my clustering will depend too strongly on the linkage algorithm chosen.
Is there a way to perform clustering without this kind of bias? Can consensus clustering help removing this problem? If so, is there any software that performs consensus clustering on molecular libraries?
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Besides clustering algorithm, the molecular fingerprint used to calculate the similarity is another (maybe more) critical factor to affect your result. There are many different types of molecular fingerprints available, each of them focuses on different aspect of the compounds. One of the commonly used fingerprint is MACCS (http://www.dalkescientific.com/writings/NBN/fingerprints.html). Openbabel can deal with path-based fingerprint FP2 and structure-based fingerprints FP3 and FP4 as well. (https://openbabel.org/docs/dev/Fingerprints/intro.html) With the same clustering algorithm, I suggest to try different types of molecular fingerprints, or to define your own fingerprint to calculate the similarity.
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how Can I do williams plot for showing applicability domain for QSAR model?
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Dear Akabli Taoufik ,
You can use The Applicability Domain toolbox for MATLAB (free) by '' Milano Chemometrics and QSAR Research Group''; C:\Users\INTEL\Desktop\AD toolbox (Sahigara et al., 2012)d20\help\index.htm''.
and this is a direct link for download it: ''http://michem.disat.unimib.it/chm/download/addownload.php''
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I synthesized some novel heterocycles which shows a particular in-vitro bioactivity. After this, I decided to go for its docking study. Synthesized heterocycles are in the racemic mixture; also the in-vitro bioactivity is assessed by using a racemic mixture. My question is, for docking purpose is it necessary to use each and every individual from the racemic mixture one by one or the individual single isomer structure (among its racemic mixture) after energy minimization is sufficient? Out of this which method is correct?
(For energy minimization I used ChemDraw program)
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You would need to perform docking studies with the separate isomers.
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I have one drug candidate in diastereomeric mixture. Is it possible to identify the number of isomers in diastereomeric mixture by using the second derivative calculations and Gaussian curve fitting analysis of DTG (Differential Thermogravimetric analysis) curve by using OriginPro 8 software?
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Dear Sameer....Unfortunately, the TG is not the choice for such identification of isomers. We have studied a large number of organic compounds (preferably solid) by TG and all isomers show the same behavior. Isomers are almost identical in the thermal decomposition profiles. However, if the isomers differ with their melting or even boiling points, there may be some degree of identification and discrimination between them by the DSC technique following their melting or boiling peaks.
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        MINEQL is a powerful and easy-to-use chemical equilibrium modeling system that can be used to perform calculations on low temperature (0-50oC), low to moderate ionic strength (<0.5 M) aqueous systems.
        MINEQL is a data driven program, so you do NO programming. In the simplest scenario, you create systems by selecting chemical components from a menu, scan the thermodynamic database and run the calculation.
        However, MINEQL also provides tools to allow you to take control of your reaction data, create a personal thermodynamic database, perform synthetic titrations and automatically process mutiple samples (such as field data).
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What exactly you want. may be look at thishttp://www.mineql.com/
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Dear Friends,
The high quality electronic version of a very nice book namely "Exploring Chemistry with Electronic Structure Methods, Third Edition" is extremely requested. Can anyone help me to reach to this book in a free of charge manner?
Thanks for your help.
Saeed
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"to reach to this book in a free of charge manner" = "to steal it"
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I am trying to create the topology files for my input protein which contains zinc and calcium ions. The OPLS forcefield in gromacs does not contain the parameters for these metals and I could not find it in the forcefield files as well. I have made a search on internet but I could not find a solution. Any ideas ?
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Ca2+ exists in the GROMACS OPLS-AA force field files (both ions.itp and aminoacids.rtp) so it should work out of the box. A quick Google search turned up several papers purporting to parametrize Zn2+ and other metals in a manner compatible with OPLS-AA. Don't use automated servers; they won't work. Take from the literature or derive them yourself.
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What are the steps to do MM potential energy surface scan for an organic molecule using Gromacs?
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Thank you for the reply. I'll try that method.
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i have got the values of all the parameters A, B, Bo, S, E, L and V of a organic compound using an online portal. I also got to know what each parameter means. but i am not able to interpret my result. For eg. B of my compound is 0.88...so what does it mean? Is there some acceptable range of the parameters for good drug molecules?
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Hi Ishani,
You need to do multiple regression analysis,then compere the coefficient for each parameter with it's standered deviation if SD is smaller than the coefficient then you will say that parameter is significant affecting parameter but if SD is larger than coefficient then you will say that parameter is not significant parameter . then look at the coefficient sign if it is positive you will say there is direct proportion and if it is negative you will say inverse proportion,then you can divide coefficient by each other and from ratio you say the importance of that parameter over other, and so go on.  Good Luck
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I want to calculate the frontier electron density (FED) for each atom in the molecule, so I need to extract the Homo and Lumo values for each atom not for the whole system. can anyone guide me how to extract these data from the output file?
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Mrs/Miss Ahmadi,
The HOMO/LUMO is computed per atom.  
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I'm a structural biologist and because of that I have not much experience with software that is able to model inorganic compounds like polyoxometalate clusters. I have an old version of Diamond but I'm wondering if there are more intuitive software packages available. 
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Can you clarify your needs? e.g.: Is the POM in a salt or in solution, or in vacuum? Are biomolecules around? What properties are you interested in? Polyoxometalates are typically very heavy and can be challenging to model, even by DFT, if you go for "ab initio" modelling. But since you are in structural biology perhaps you rather work with some kind of force fields? Giving more details about the question would help people reply with more precise answers too.
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Hi, I’m a PhD, who is beginning to learn about molecular modeling. I work with enzymes and for prepare it, I use the application PDB2PQR of Chimera and this software offer 6 forcefields:
- PARSE (default) - PARameters for Solvation Energy
- AMBER - AMBER ff99.
- SWANSON - AMBER ff99 charges with optimized radii.
- CHARMM - CHARMM27.
- PEOEPB - a version of Gasteiger-Marsili Partial Equalization of Orbital Electronegativities, optimized for Poisson-Boltzmann calculations.
- TYL06 - a Poisson-Boltzmann-optimized force field.
I read some about them and I think that PARSE could be a good option but I don’t know if another forcefield could be better than it. I would be very grateful if anyone could help me with this question. Thank you all for your anticipated response.
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Use the default AMBER force field unless you have reasons to use another force field.
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My molecule is too big for ATP ou PRODRG server. I wanna to prepare files for DM at GROMACS but a can't create this topology file for my enzyme.
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I'm looking for a tool that can give how many methyl groups, hydroxyl groups etc. are in a compound from it's SMILES, SDF or MOL2 format.  
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If you are familiar with Python, you might want to try SMILES/SMARTS pattern matching in RDKit:
[in]>>from rdkit import Chem
[in]>>mol = Chem.MolFromSmiles('COC(=O)CNC(=O)CCCO')
[in]>>functional_group = Chem.MolFromSmarts('C=O')
[in]>>matches = mol.GetSubstructMatches(functional_group)
[in]>>len(matches)
[out]>>2
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Hello, I need to predict the mass spectrum of a molecule to compare it with the mass spectrum obtained experimentally, if anyone knows a program or web server that gives me this option I would appreciate it a lot
Thanks
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I have done virtual screening of a database containing around 3.5 K ligands. Best five ligands were sorted out by considering the binding affinity with the protein. All five showed  binding affinity around -14, but after optimization in PM6 or DFT in Gaussian 09, it decreases to around -8. 
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Waht did you optimize with PM6 or DFT? Ligand + part of the protein? Ligand only? How did you choose the part that was optmized?
How did you calculate affinity after PM6 or DFT optimziation? Using the same force field protocol, but jut taking newly optmized geometries? Using binding energy from PM6 or DFT?
By the way, most binding affinity methods that use force field approaches have the expected accuracy of a few (3-5 kcal/mol).
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Is there a way to choose the site where we want to place the grid box using the command line?
When creating the grid parameter file in ADT, in the graphical interface, you go to Grid ---> Grid Box --> Center. Then you have to chose to center the grid box in either: 1) Pick an atom 2) Center on ligand 3) Center on macromolecule 4) On a named atom. Therefore, it is possible to chose to center the grid box in the ligand.
My question is how can you do it (chose the position of the grid box) with the command line? Is there a script that I could use? There is no option in the grid parameter file. I have more than 10000 compounds and it would take me a really big amount of time to do it graphically. Then, I would like to automatate the process. Is there a way that I can do it?
I am going to do rescoring which means that the grid will not be in the same place of the ligand automatically.
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Dear collegues,
I have received an answer from Proffessor Sanner (Michel Sanner), who works at the Scripps Research Institute. 
I hope it can  also be useful for you all:
"I have been  working in a version of AutoDock that supports making receptor side  chain flexible and as part of these software developments I wrote the  AGFR software program.
This command line program allows the compute grids (using AutoGrid4) and supports a range of options for automatically positioning of docking
boxes, including using: a known ligand, or a set of residues in the
receptor, a binding pocket identified by our pocket finding method
AutoSite, etc...
The software is not 100% ready for release yet but if you want to give
it a shot you can download the code from
please look at the Use cases on that web page for examples on how to use  the software. (Be aware though that currently the graphical user  interface AGFRgui is broken) but the command like version should be working.
The .trg file contains is a zip file that contains all the maps generated by AutoGrid4 and that can be used for AutoDock4.
So you have to unzip the .trg file and give AutoDock4 the the .map files located in the folder created by unzip.
> unzip myfile.trg
Archive: myfile.trg
inflating myfile/rigidReceptor.C.map
inflating .. "
Thank you all for your help! I really appreciate it!
Huge hugs 
Stellamaris
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The electronic state of the initial guess is 1-A.
Requested convergence on RMS density matrix=1.00D-08 within 128 cycles.
Requested convergence on MAX density matrix=1.00D-06.
Requested convergence on energy=1.00D-06.
No special actions if energy rises.
Integral accuracy reduced to 1.0D-05 until final iterations.
Problem detected with inexpensive integrals.
Switching to full accuracy and repeating last cycle.
Spurious integrated density or basis function:
NE= 228 NElCor= 0 El error=5.13D-01 rel=1.49D-03 Tolerance=1.00D-03
Shell 101 absolute error=2.76D-04 Tolerance=1.20D-02
Shell 71 signed error=1.90D-04 Tolerance=1.00D-01
Inaccurate quadrature in CalDSu.
Error termination via Lnk1e in /opt/apps/gaussian09/g09l/g09/l502.exe at Wed Oct 12 18:51:23 2016.
Job cpu time: 0 days 5 hours 33 minutes 22.6 seconds.
File lengths (MBytes): RWF= 447 Int= 0 D2E= 0 Chk= 10 Scr= 1
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The problem in your calculation is in "Inaccurate quadrature in CalDSu" in Gausian 09. To solve this problem add the statement (integral=NoXCTest) to the input command line. This statement stops any checking to the accuracy of quadrature calculations.
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Ninhydrin reagent solution for amino group detection.
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Dissolve 0.2g of ninhydrin in 100ml ethanol. Mix thoroughly.
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I am beginner for gromacs simulation package, I used it for organic compounds. But unable to understand how to deal with inorganic compounds and what about the force field and solvent we can use.
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the best thing you can do: 1. paper analysis. 2. did anybody perform MD with inorganic compound (that you mean) and which force field is used for those simulations.
under basis of analysis hope you will be able to decide what to do. if you have further questions just let me know. cheers
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In pubchem, i found the pKa of DMF is -0.3. However it is a strong base, which means its pKa value should be high (>16).
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Hi
Yes it is correct. DMF is a derivative of formamide, the amide of formic acid.
Amides (known as acide amines) are not basic as amines,  DMF is not a strong base, the pH of a 0.5 M DMF solution is 6.7
Best regards,
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Working on my PhD im using a catalysis that offers the creation of alkylamines with full stereoselective controle. To put this to use im looking for bioactive structure that might be accesible through the synthesis. SciFinder, Reaxys and so on gave either huge or none results, while the Merck index doesnt offer full potential as free user. Im looking for platforms or tools that might help.
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Try a ligand search in ChEMBL (https://www.ebi.ac.uk/chembl/). You can search it by substructure.
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I've been using Glide for docking. I got some data for docking score and Gscore, but i dont really understand what they mean....and so I cannot choose the best ligand.
Could anyone help me please?
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You will not know until the selected virtual hits are experimentally tested.
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i successfully done the protein ligand complex simulation on NAMD with Generalized born implicit solvent model on 20ps timescales. and it is smoothly done.
but after loading the .DCD and .PSF file on VMD nothing is shown on screen. 
can any one tell me where is the problem?
i upload the dcd and psf files heres.
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I think it could happen because when you open the 'Molecular viewer browser' window for uploading the trajectory you might have chosen in textbox next to 'Load Files for:' as 'New Molecule', instead you should select '1.psf' (which must have been uploaded first), then in the textbox next to 'Filename' select the trajectory.
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Hi!
I was wondering if you can suggest program/script for linux or not, that can calculate the interactions between Ligand and protein from a pdb file. When I'm saying interactions i mean, h-bonds, aromatic interactions, hydrophobic bonds etc.
I found IChem on the internet, but it seems that it doesn't work properly. I couldn't even run it.
Thanks
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The Protein-Ligand-Interaction Profiler https://projects.biotec.tu-dresden.de/plip-web/plip/ adds some details that are not covered by HBplus. Besides the Eeb interface, a command-line driven version is available for download.
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Actually, I am submitting the job into the Polyrate 8.0 software for my reactants and products to calculate the rate coefficients for a particular reaction.
But, in the output, I am getting some non-zero imaginary frequencies. In principle, for reactants and products, there should not be any negative frequency(NImag=0). I have made sure that my inputs are correct including the Z-matrix too for the reactants and products.
I am unable to understand as to why this is happening. Can somebody please help me with this?
I am also attaching the i/p files for your reference.
The files "r1.dat" and "r1.71" are the i/p files for Polyrate. And, the file "esp.fu82" is the Gaussian o/p file from the Polyrate software.
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I have done this also.
But still I am stuck with those negative frequencies. Can you please help me out with this problem?
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recently,I met the difficulty on material studio.
The two pdb files has similar contains,but display differcently.
the files has been attached.
Why the structure display pink bonds and atoms in material studio?
and how to adjust the file of hem.pdb ?
we should pay attention of what is the key point of pdb file when we constructing it.
Thank you very much in advance.
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what's meaning about the pink colour in software material studios ? thank you, sir!
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I have done molecular docking on water solvated model of hERG open and close model and from final poses build GRIND model and prediction power is comparatively very low from the model I have obtained from vacuum docking poses 
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Compare many crystallographically solved hERG structures. If you find any water molecules as crystallographically stable (appeared in all the structure's binding cavity), then those have certain impact in binding. Include such water molecules in docking.
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It was always a challenging problem to describe the electrostatics of proteins. Over last few years there has been a significant effort, and considerable progress was seen in this area. Most of those efforts are concentrated on applying continuum electrostatics, such as Poisson−Boltzmann (PB) or molecular dynamics simulations approaches. Though the accuracy level of those prediction methods are encouraging but most methods require a significant computational effort with impractical calculation times. In this light, I'm interested if any one could explain me regarding a relatively fast calculating methods with good accuracy. Thanks in advance for your time and consideration.
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Many folks are working on that, but implicit or explicit,
BUT AFIAK with current offerings:
if you want improved accuracy and high speed together you either need an impressive and well connected HPC (for larger system semi-dependent systems like macromolecules)
or GPU  powers for non-macromolecule or highly dependent systems/methods.
and the movement seems to be towards impressive GGPU local boxes
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I have carried out MD simulations about membrane channel. In the process of MD simulations, the upper leaflet and the lower leaflet were separated as shown in figure 1. I tried to center the two leaflet together and used PBC wrap command in VMD "pbc wrap -centersel "resname PAP" -center com -compound res -all", PAP is the name of the fixed channel in the membrane. The two leaflets were together as shown in figure 2. When I evaluated the thickness of the membrane, something weird happened. The thickness of the membrane experiences great fluctuations from the time the two leaflets was taken apart. I guess there is something wrong in the treatment of PBC but I don't know how to tackle this problem.  
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You're welcome!  If only all problems were so easily fixed.  :)
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In machine learning approaches many researchers are taking different  reference values to classify molecules as actives and inactives?
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It all depends on context, i.e., which drug-discovery stage you are at. A drug candidate is expected to have low nanomolar on-target IC50. Hence, in lead optimization, ~100 nM would be considered as a right threshold, while in hit identification this would rather be 1 or even 10 uM (micromole/liter). 
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Let's say we want to generate conformers of ligands prior to docking them rigidly.
Is there a formula, like a function of the number of rotatable bonds, to decide how many conformers (maximum) to generate for a given molecule ?
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I've been following the discussion generated by this question. My interest is in which conformations a molecule will adopt in its crystal structure and we performed a study a couple of years ago that might have some relevant results for the docking problem. We only looked at a fairly small set of molecules, but collected some statistics on the energy of crystalline conformations relative to the gas phase global minimum. We found that, in the absence of possible intramolecular dydrogen bonds, drug-like molecules crystallise with conformers up to 25 kJ/mol (6 kcal/mol) above the global mimimum. In case it's of interest, the paper is here:
“Which conformations make stable crystal structures? Mapping crystalline molecular geometries to the conformational energy landscape” Chemical Science, 5, 3173-3182 (2014). http://pubs.rsc.org/en/content/articlelanding/2014/sc/c4sc01132e
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I write in executable line AOMix : "molecule.out fragments.txt FO"
but after run, I can't find EDA in output.
I think it doesn't diagnose  the fragments. 
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thanks for your answer dear Cramer
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i made a thickness of transmembrane of 100 * 100 on vmd. but can we reduce the thickness of the transmembrane on vmd ?
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As stated already, the thickness of a membrane is a given property of a particular lipid. You can't change it. The only way to get a thicker or thinner membrane would be to use lipids of different chain lengths that would correspond to whatever you're trying to do.
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In order to digitize smells, one would need a device that can recognize individual smells as binary inputs/outputs and then form the molecules required to create the smell. I would love to hear some ideas on this.
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The only device that can recognise individual smells is a perceiving human subjects n a brain. Smells are not properties of molecules, or even properties of bindings of molecules to receptors. They arise somewhere inside the brain after a complex comparison/collation process has occurred between signals arriving from multiple receptors. The spectrum of smells is determined by the evolution of a nervous system designed to distinguish useful and noxious substances irrespective of their close similarity or difference in chemical structure. Because there is considerable genetic variation between individuals in receptors (and a systematic one between males and females) there is no fact of the matter about what smell any particular molecule has - each of us defines it as the smell it has for us.
So the only device is the one that has been used over the centuries. Chemists worked out that rotten eggs give off hydrogen sulphide using the strategy of random trial and selection rather than instruction that biological systems tend to use. Chemists played with minerals and found certain procedures smelled to their inner human subjects like rotten eggs. A thousand other procedures did not. They discovered the chemical formula of H2S. 
But no sensing device can mimic this. You could transfect excitable cells with olfactory receptors and see them respond but there would be no smell involved. You could use the system to recognise molecules in terms of their ability to bind receptors but would that be of any interest?
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I know the definition (Maximum E-state of an atom in the molecule), but I don't understand how we get a number.
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I've seen various electrotopogical indices but not this one. Thank you anyway.
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I have a problem calculate 3d descriptors using Chemopy(as Python modele).
Usually this function working normally, but sometimes occur same error...(Input file is same!!)
I added the image file that captured error messege.
l wonder why ouccur this problem ..... 
plz reply to me...
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Could it be that there is no old file 'temp.arc' when the error message is raised ? At least not in your 'cwd' (Current Working Directory). Either the file is missing or you started your application in the wrong directory.
If you have to cope with a missing 'old file': check for the existence of the old file prior issuing the os.rename() and skip this action if there is no file.
If the missing 'old file' is a 'human error': check for the existence of the old file and abort further execution if it does not exist.
Wish you success.
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I am new to REMD simulations. I have performed REMD simulation on a short peptide in AMBER12. I need to calculate the acceptance ratios for all the replicas. Can anybody explain how and where to get this information in amber output files?
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Let's assume that you've kept all the replica swapping history. ( # of swap ) / ( # of swap trials ) is the ratio. 
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Hello
I want to model interaction between C60 and few some chemical compounds in a box with water. Can anybody suggest me any software tool (except gromacs) for such kind of work ?
I would be very grateful for that.
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Dear Vasyl
Good results for the interaction of compounds can be obtained using software NAMD code59 with CHARMM27 all-atom force field modified for your compounds (see.: Armen H. Poghosyan, Hrant H. Gharabekyan, Aram A. Shahinyan "Molecular Dynamics Simulation of DMPC / DPPC Mixed Bilayer ", International Journal of Modern Physics C Vol. 18, No. 1 (2007) 73-89;  Armen H. Poghosyan, Levon H. Arsenyan, Hrant H. Gharabekyan, Joachim Koetz, Aram A. Shahinyan" Molecular Dynamics Study of Poly (diallyldimethylammonium chloride) (PDADMAC) / Sodium Dodecyl Sulfate (SDS) / Decanol / Water Systems " J. Phys. Chem. B 2009, 113, 1303-1310,  Armen H. Poghosyan, Grigor A. Yeghiazaryan, Hrant H. Gharabekyan , Aram A. Shahinyan "The GROMACS and NAMD Software Packages Comparison" COMMUNICATIONS IN COMPUTATIONAL PHYSICS, Vol. 1 (2006), No. 4, pp. 736-743).
A.Shahinyan
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Derivative of  Oxazine?
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Basically it is a phenoxazine derivative. The correct name of the structure is "mono perchlorate salt of 3,7-bis(N,N-diethylamino)phenoxazine".
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I'm looking for a database that contains generic synthetic reaction. Most databases I found only hold reactions for specific molecules. I'm interested in a somewhat more generic representation, or a hierarchical classification of reactions in a synthetic context (i.e. not enzymatic reactions).
Since I would like to process these reactions in an chemoinformatics context, the database should offer a machine readable download, e.g. RXN or Reaction- SMILES/-SMARTS/SMIRKS, with atom mapping.
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Thank you for all your the answers, especially for the one about the Greg Landrum Paper (http://pubs.acs.org/doi/abs/10.1021/ci5006614). The introduction briefly describes probably all resources (commercial and public) that are available, including the ones mentioned above. The best resource I found for my "problem" is also the one they used in the paper:
I should have known about the paper, since I've known the first author for quite a while. :)
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Dear all:
I think from NBO calculation after running in Gaussian we can obtain Mullikan electrical charge for each atoms ,then we can compare this charge in TS with reactant ,but I don't know How we can calculate CT for all transition state structure?
Thanks in advance
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Hello Mam,
It may be done by simple visualization on charges of reactants atoms in Transition state. If you have two reactants in your reaction then find out the total charges of both reactants in T.S. seperately. You will see one has reactant has +ve and another one has same but -ve charge. species having +ve charge is donor and another one is acceptor.
Have a nice journey...........
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Is there any way to find the IC50 values if we do not have the experimentally performed data?
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Coarse estimations yet extremely useful visualization is now possible with SeeSAR from us. 7days license included, so just download and get a go. You would need a 3D receptor structure though: www.biosolveit.de/SeeSAR   please give us feedback!  best wishes  marcus
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Does anyone know how to consider the intra-molecular basis set superposition error of conformers for a molecule if I use Gaussian 09? 
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Sorry, I am interested about the method to consider the intramolecular BSSE. I have also used the CP method to consider the intermolecular interactions. However, thank you.
Can an OH radical form a strong hydrogen bond? A theoretical comparison with H2O
CH Lai, PT Chou
Journal of computational chemistry 28 (8), 1357-1363
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 I am trying to get the Raman spectra of solutions with very low concentrations of amino acids 1mM-6 mM. However, I can not see their peaks because they are masked by the fluorescence of the solutions, which also prevents me from using long exposure times.  I have no idea what is the source of this fluorescence because my buffer is 25 mM  in Tris-HCl and 150 mM in NaCl (pH=7.2).  Also, I always wash my cuvettes with soap and nitric acid before using them. Does anyone have any tips on how to decrease the fluorescence? By the way, I am using a 514 nm laser source.
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There are some methods to observe Raman bands of fluorescent materials:
1) using exciting laser lines with long wavelengths (in the spectral region of the red or infrared radiation) one can prevent electronic absorption and thus fluorescence; however, the intensity of Raman scattering results lower than that obtained with excitation laser lines with shorter wavelengths (for example, in the blue or green light region);
2) in the region of anti-Stokes scattering, fluorescence does not occur and therefore it is possible to observe Raman bands (especially those with minor Raman shifts), although of much lower intensities than those of the corresponding Raman bands in the region of Stokes scattering;
3) by strong interaction with Ag or Au nanoparticles it is possible to observe Raman bands by means of the SERS (surface-enhanced Raman scattering) effect, with marked fluorescence quenching.
However, failing to provide different exciting laser lines nor appropriate SERS substrates, it is possible to mitigate the fluorescence emission, favoring non-radiative decay from the excited electronic state of the sample molecules, for heat exchange with surrounding molecules, for example by finely mixing fluorescent solid samples with KBr powder.
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I get convergence problems when I use the following input:
$contrl scftyp=rhf dfttyp=b3lyp tddft=excite
maxit=200 runtyp=energy icharg=-1 mult=1 $end
$tddft nstate=3 mult=1 iroot=1 tammd=.false. $end
$SYSTEM MWORDS=2400 MEMDDI=1600 $END
$SCF diis=.f. soscf=.f. NPREO(1)=0,-1,1,9999 $end
$FMOPRP NCVSCF(1)=2 mconv(4)=65 MAXIT=1000 IREST=2 $end
$guess guess=huckel $end
$basis gbasis=N31 ngauss=6 $end
$data cobalt compound
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If it is the SCF that does not converge, I would take the MOs at the end of this calculation, and use them to read in as initial MOs and run the calculation again.  Can't remember exact keywords (been some years since I ran GAMESS), but this is what I would try first.  I often had MOs that would get "stuck" before converging, and reading them in as initial MOs in a subsequent run would allow them to converge correctly.  If that does not work, then I agree with Venelin:  use your graphical display program (like MacMolPlt) to make sure all atoms look bonded reasonably.  You might have left an atom off or something. 
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I have a crystal structure of azopyridine molecule linked to another (different) molecule by hydrogen bond. It is well known that azopyridine molecule shows trans to cis isomerization when exposed to UV (365 nm). Crystalline sample of this hydrogen bonded system becomes amorphous when it is exposed to UV (365 nm). We predict that the intermolecular hydrogen bond is breaking upon UV exposure. Could anybody explain how this effect can be reliably simulated in gaussian09?
Also, some of such photo-isomerization processes are reversible. How can one predict this reversibility computationally?
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Try to calculate the single-point energy of the two hydrogen-bonded species separately, then together linked with hydrogen bond(s) apllying BSSE correction.
From these calculations you'll get energy of the hydrogen bond(s) with the following formula: Ehbond = Ea +Eb - Eab(BSSE corrected) 
The energy of the UV photon what you use is 78.3 kcal/mol at 365 nm so if the calculated hydrogen bond(s) is weaker than this, it may cleave during irradiation, however it heavily depends on the molecular structure, but I hope this will help.
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The AlogP parameterisation that I'd like an implementation of is defined in this J. Phys. Chem. paper from 1998: http://pubs.acs.org/doi/pdf/10.1021/jp980230o
Thanks in advance!
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Hey Christopher, if you are looking for an open source for AlogP, you can use padel descriptors calculator from NUS. Its pretty good as it also do the job for standardization as a preprocessing.
If you like to use coding, you can use R or python.
Rcdk, Rcpi, rcpp just to name a few for r packages.
For python, rdkit is so hot right now.
Good luck mate.
Christopher do you use R?. If you do...here it is for descriptors extraction.
biocLite('Rcpi')
library(Rcpi)
RI.smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')
x.mol = readMolFromSmi(RI.smi, type = 'mol')
x = suppressWarnings(extractDrugALOGP(x.mol))
write.csv(x, file = "ChristopherALogPDescriptors.csv")
Or if you like GUI where you just use the mouse to click. you can use padel descriptors. Its cool and its free.