 Okay, hello everyone. It's really a big pleasure to see so many people after so long. So it's a pleasure also to introduce our speaker, Daniel Madulu Shabrak. He's our visitor from Tartania. He will be around two months. So if you find his research interesting for you, don't hesitate to talk with him. He has a very nice story, so especially for the student, because he was a step student. He did all his career in Tartania, but he came to STP first time as a step student. And he joined here for several months, but he was so interested in physics that he did the PhD courses of CISA and the courses here in the diploma. So he was extremely motivated to do this. And he took a lot of profit from the courses he does now. He's a professor in Tartania, and he's teaching a lot of new students in his country, and he's got some motivation for him. So he will talk about natural products and new discoveries. Yeah, thanks for the nice introduction. My talk will be about computational methods that we use in the discovery of practical natural products. I mentioned Tanzania, and I'm with St. John's University of Tanzania. And now I'm visiting ICTP for some months. So welcome to Tanzania, my friends. St. John's University of Tanzania is in Yodoma, the capital of the city. And from Yodoma to St. John's Trieste is as far as this education. I'm St. John's, so I work in the Department of Chemistry. So I'm myself from a trained chemist, actually an experimental chemist. And then I moved from being an experimentalist to the rest of computer scientists. So I moved from doing chemistry to confession by physics. I moved from being an experimentalist to confession. And then I took this step. And also the support from the office of the former hospital director, Fernando. So it was a very nice experience actually for me. So back home to Tanzania, I said, oh, this is, I am ready to go bring food back. So I established the training by the schools in here. So since 2018 today. Now I have been a mission to establish an African center by a team. We hope it will work very, very soon. And the school has been very successful bringing many African scientists, all these participants coming from different countries in Africa. And the good thing that some of them are doing PhD, for example, is that it is here in Iran. You met the horizon in the school. So this is the success of it. Now let's go back to our business and I'll be sharing with you the overview of natural products research in Tanzania. And how we can use professional methods to understand the original and the actual how they work. So research is a natural product in Tanzania. That is about to the early 1970s where Professor Hassanar was a professor in the Department of Heritage at the University of Islam. So actually Hassanar is the father of Hassanar. So Hassanar is junior, Hassanar is senior. And then was Professor Sankunya. So we lost him three months ago. So the guy that established the natural product research and the motivation was the exploration of this nice small electric and he did not know how to name it. So he named it Barak, Barak means Baraka. He said because it has two hydroxyl groups on the other hand, so let's talk about this abrasive. So we have abrasive to this research. So natural products actually of different classes. And how do we obtain from the plant? As the plant strive, you know, with biotic and you know, with all the stresses, then they make of course food for them to survive. As they make food from different sources, from the inorganic, from monoclonal, from water, there are different pathways that they got through. So the first pathway we need to first the metabolite that the vitamin D, et cetera, et cetera, that are very essential for the plant. And again, as they strive this pathway, one of the questions that they give another program. The second, we follow them, a secondary metabolite. So we are primary metabolite, secondary metabolite. So the secondary metabolite are of actually no use to the plant. So actually they give them out as physical or, you know, as the product of the vitamin D. And then for last we use them. They're very important for us. So and then we can find them from many plants of different classes like caricalloidate, apennate, and many for the pet type. And actually this, of course we use them in our daily life because the others are from different sources. We can obtain them from marine organisms and from our, we call it in the Swahili in Darasi. And then we use this natural product for many, many years that we use in the, as a source of medicine. And I will focus also my discussion with this natural product and I've shown very good promising as a neuroprotective agent and treatment of warrenia and patents on disease and also in many other diseases. So some natural products now have been approved by the FDA for treatment of many diseases. For example, as an energetic particular to vital and the many and that they're from marine organisms and the other from the plants. Now in Tanzania, natural product research, so we have two major, two groups. One group is focused on the isolation and the characterization of this natural product. And this man, Professor Nyandoro from the University of Tehran so he was also my MSC, a scientist and advisor and now we work together with him. And this Professor Esta, she's a student of Professor Sanan, senior. She was trained in natural product and now she do formulation of this natural product. So how do we obtain this natural product in this laboratory? Normally chemists will go to the field, we collect the plant, we'll do the solution, put in the solvent, evaporate the solvent, get the crude extract, the fractionation and then we use spectroscopic technique to get some signals then we use these signals like in NMR, the signal corresponds to carbon, it corresponds to this atom. And then we join together, we get the monitoring and also with the other IT NMR. So an example of this natural product, very interesting, is for example this alkaloid. So most interesting look is the change just in this group here. So chemists are already interested with smaller changes in the functional group. So the changes in the opening of this to form this results to a dramatic effect in the parent activity. So I'm interested in how this is happening. So computational approach is we are now coming in to help understand how this is little working and what is happening. So I will give an example to this one in the next two slides. Yes. Thank you very much. Okay. This is a, okay. This molecule, the alkaloid, as the labestide. So they showed the ability to kill the lab as to what the concentration is effective. So this is the rate of dose of killing the perfect population of the tested organism. So this one is able to kill at 0.0025 milligram per milliliter. So this is a milligram per milliliter. So there are more rest, rest, rest, the more effective, effective, effective. There is a very small opportunity to give a larger effect. That's what the meaning is. So the other type of natural product that I will use also in my talk is this from the plant. Because when I was doing the masters there we actually did many of them and we tested them as cancer, and now we are working with them as a neuro-protective agent from two perspectives so using the computation of logic and using the experimental asset. Now while traditionally the process of discovering a drug is very possible, very challenging, it takes a long time. 15 years on average, if you go to the pharmacy and you ask for a panel, your mind is taking more than 15 years to be a fluke. And it's not a guarantee that the molecule will be accessed. So some drug was there in the fluke, they evaporate from the application just in one year, two years because of the availability of systems and all the things. So why this? Because all of these things are discovered or they are studied during the progression of the process. It is very easy to skip things and when you go through them you find all these problems. So it's a bad time to pharmacy. Now the advent of computer hardware algorithm has been helping a lot. Now to study all these in advance. So let me use this slide here. So normally now in order to shorten the process we will begin with computational things by informatics understanding all the drugging properties, the pharmacopinetics, how it is distributed into the body, how it is absorbed, what is the toxicity, before going to clinical trial. And then we can study all these, then we go to clinical trial. Now at least we have private information on the toxicity, on the therapy and this can quickly shorten the time and speed up the process. So computational methods of private drug design actually are of two categories. They are based on a region-based design so that you have one range of natural products and you have a set of other known models to treat, for example a cancer. So you have your data set, your database this micro product, you don't know how to evaluate it. You train based on the model that you have known. So if the model is known to treat cancer they have maybe 3 or 8 and they have 3 reasons that you train, you need to have these properties to provide to do this. So this is based on this machine learning approach. I will talk about it later. And the structure based drug design is much machine learning that you approach a disease when you treat it. We just look for a target. As it is a protein, mostly it is a protein. So we look for a model that we go to bind to the protein and we stop that thing by preparing for process that is happening. So that could be my prognosis that someone say, oh now I'm feeling well. The third one is the number you don't know the disease, you don't know the region. So the first one is useful. So we can do quantitative structure and relationship and efficient neural network decision tree. But all of these are based on machine learning algorithms. So how do we do you have a database of natural products and you have now camera informatics they can predict all these things. So you can look into the physical chemical property membrane intermediate system between people and the human side. Then you can get your favorable natural product. Then you can say, okay let us establish a protein-region interaction. Then you can go to other technique that I'm going to explain the molecular dynamics. Which now is based on a structure. So in molecular dynamics the system involves this time with the useful impression of motion that you have the force is equal to the product of mass in this type of relation. And then the force is is equal to the negative potential. And then we're going to have now the potential when you get at the first field this color now you are assisting the body angle, the stationary angle and all those how they are going to interact. And now the we're going to do the breakage of the bone. So molecular dynamics So how do we copy those other techniques we can now combine the quantum mechanics because molecular dynamics is based on molecular mechanics. So now we can come up with quantum mechanics and molecular mechanics to understand the point of interest. For example in the end of catalysis this is the most powerful tool to understand the way of that descent of production. So right assisting for example is treated in molecular mechanics and just the point of interest is treated with quantum mechanics. So in the system now as a implicit so there is a constraint of time-scale the apparatus at the lens. So as the system actually the apparatus also to some extent to play. So getting information for example for protein folding system like this one requires the right time-step because the integration times that we use in molecular mechanics to one step the order of intersection and then the folding of this guy will take in the order of microsecond. So you need millions of integration time-step to go and get this information. So this is why molecular dynamics to some extent is very expensive. Yes. Any of us would be there. Ah ok. I think the apparatus can be usually different thing. Ah ok. So this is a free energy around the step. The confirmation of this the folding step so what is the probable step of this protein. So you see this is a a global minimum of the system so before reaching to this step it goes through differences. So here it is like this, here it is this and it goes here crossing many other barriers before it reaches here. Yep. Thanks. Ok. In my group we are using molecular dynamics and how we do we do a very mysterious molecular dynamics process and we start with a structure for protein from our experimentaries especially biochemists are very good so they go to an organism and they move the protein they put it in the data bin and they help us to do this. We give you an example. So we take the protein we do molecular dynamics to get a sense of because the molecular process is different the way I take it here the way I take the protein I bought is I guess dancing. So we do molecular dynamics for the protein to people dancing so we do clustering to have different structures of the protein and then we can do some database of natural products to any individual of this and then we get the algorithm. And this is about molecular dynamics another thing we call the form of before in form of before you have a database like this one here with known for retroactivity so you pray now you see still that this to have activity you need to have this future. If you don't have this future you don't qualify and then you screen your database based on this form of future so maybe you have this group you need to have this group and then you can screen and then you can measure how we compute the form of kinetics the form of kinetics is how the how the body dies to the drug so once you can check and see how this will give the toxicity or not and then we can compute the distribution so the best performing molecular we do molecular dynamics and we can score those molecular dynamics result with the endpoint free energy like MMPBCA and union structure or by thermodynamic integration and also we can do the enhanced sampling approach for example by metadynamics simulation and then to get the binding and unbinding the kinetics of the system now because now I'm working with experimental group so with an experimental group what do we do they will extract the natural product they test with the animal they go to the test tube and then they give out the compound and then we do this simulation and then we compare the result so this is what we are doing now so one example the two molecules I showed before this one they have showed a good cycle of activity now the problem is we want to solve a maladyan object a maladyan is a big problem so a colleague in Italy isolated this protein from this mosquito and say now we have the protein what is your natural product let's see the one thousand this much product and what is the experiment so we give the document experiment so the difference between this one and this one actually does the structure here so the binding and again the document show this is less this is even more stronger and the difference is brought here so some of this experiment are not yet completed so we said we have a little amount of this so we are synthesizing again and then we give the experimentaries to do and another group now testing the behavior of the mosquito how is it behaving so sometimes back we used this process in process of discovering the HF49 HF49 is a molecular type alone responsible for maintaining collecting all the protein that are responsible for cancer so the idea if we are able to stop this we can create a range of protein cancer at once so we said okay let us clean many many many drugs from natural product database and let us test and see if this can be a good distribution so we were lucky so we cleaned this one and we found this one is called pitavastatin is more showing more affinity to the protein than the other molecule luckily after we have an other experiment where he said oh pitavastatin is still showing here a good inhibition with a very small effective concentration at the field so we said okay so our method is to be like with the experimental people what they found so natural product so talk here and use to example of two natural products and his brother as well as Astro so the problem with and and that is and the aggregation protein and the role of the natural product is to prevent this misfolding of the meloid fibers and also the aggregation so by so doing we we are using now this natural product so how do we use it we take this natural product so it is in use to an animal it induces stress reactive oxygen species which again this double the mitochondria and then that is some so we can induce now the disease to the rat so fear and then we extract the natural product after we extract we induce it again to the rat and then we check the behavior so the experimentalist so once we induce this guy then we check the pathological in the brain what is happening so this is before you say it's clean and then after inducing this not an end we induce effect to the protein to the signal then the brain like this and then when we induce now our natural product so this is for example from green tea so take a lot of green tea is very useful because we have this guy here so yes then we check a different concentration then when we induce it we can see now this effect so now this experiment so as near to the end but we have so many of these kinds of where we are working on it and it's giving the good insight now something happened because of the COVID and we have to work with the community and to tell what is happening so the structure of COVID is offering us three treatment options we target to the immunity of the body and we have many prominent natural product is it to make everyday we not to make it, we use it or we prevent the virus from entering the human cell or we act to the virus itself yes yes the yellow one yes that's one yes at the latest slide it's special for Kukumi I will explain in detail so when this happened so we say now we a friend at the rest of the time decided natural product and he tested them against many other virus before including the ambient fruit and so we say that has created now a database of natural products all of them but without viral properties the gander of the class so we created the database and because biochemistry by then had isolated the proteins from the virus and then we screened it against the viral proteins that were available by then and then we ended up getting four promising natural products so we say these are not appropriate drugs it's very expensive going back to the forest and we need immediate response so a computational approach to one of the things we use is a similarity stage and then it's opposing so you choose for a similar model so you go to the approved drug you look for a very similar model and then you test it for the project so once we looked for the similarity we got many drugs from Dr. Benkin and then we did the screening and we got these three molecules we did again the molecular dynamics in all those computations then we ended up with these three approved drugs so we said and of course in both the so we said now what are the sources of this they are all natural products so if we wanted to give immediate solutions to the community what are the sources of this where and we found so then we say ok let's go to the sources that's wonderful ah ok so we use it to mean to mean also coefficient so like how using this the molecular so we set the criteria so there is a formula cause I'm not writing here that we use that we say ok 64 more products are similar there's a 90% to this one with these criteria so one of the criteria we use is coming out of criteria yes yes based on the chemical on this and then it will screen everything similar to this one this one is the index in the experimental structure just based on the chemical problem depends on it you see a mental structure yes yes so you are going to quantify the distance between the structure yes yes you look at small distance yes ok how do you find TANIMONTO yes TANIMONTO index ok ok so we said natural source of this so we found one of the very good natural sources is the Cyprus but unfortunately we discovered it as a waste so it is found at the pier and not to the world this is the white guy that is a good source of husbandry so back home now we said what should we do then we found this paper from Italian people so they found now the computational studies so I guess that is the third thing a favonoid abandoned in Cyprus because there are many other people they found it and then they extracted now this one and producing many many many many grand dosage per day the good thing now this guy has chance is in the clinical trial for COVID treatment for us we say ok take the formulation the combination of this bring it together you will find the molecular within there you will save the pappas and there is going to have two pappas so if you contracted the vital acid some molecule stop replication other will prevent from getting inside from entering but there is one disadvantage that we need to take a quick question our body is very sensitive it is a very good chemistry teacher if the sense is even very small changing the molecule take example of this one this and this thing by just looking you say they are same the molecule is the same but the chemistry is not the same this is pointing that way this is pointing that way of course they are like mirror image they are mirror image but they are not somebody possible so you cannot take this and they post it the body is a good chemistry teacher chemistry teacher and they test this smeris in the remone this smeris in the orange so they are two different things this one if we just say this you will get something similar this is 100% but actually this is a thing this is very not in danger and you can look at the effect of this one because it was presented to pregnant mother and that resulted to give better of chance with many bodies effect because when it was the minister was converted to this and people were not able to what is happening you take this it is like this and the body goes like this and then this looking this way is a problem and the body does not take problems so last one about the protein you don't know the starting point you go blind so how do you go blind I take one example of cassava cassava test beta sometimes cassava beta test 3 what it causes is this guy cyanide so in cassava there is this molecule called the renamarin and there is the enzymes called the renamalase that do the hydrolysis to remove the cyanide that test beta so we wanted to see the interaction between the renamarin and the renamalase so but we don't have the starting point so we started doing homology modeling so this is the powerful concentration on our product design you don't have any use so we did the model from a light template and then we obtained our protein for the cassava then we did the stability of the molecular dynamics and then we established the interaction by the pre-energy method so we are still working on this with the other interaction piece but the drug design is not to honor docking protein ligand time there are many interesting things for example drug dissolution aggregation polymorphism crystal structure these are very interesting things I will share with you how to assemble the aggregation process of a drug and then I will finish with non-technology so let's take an example I have three natural products so choose one to experience we have capsicin from pepper pepper that gives a strong sensation is this guy here when you have pepper and the bunny's attention is this guy which gives the bunny's sensation but it's very good in rheumatism matrix the challenge is to absorb in water so we try not to understand at least to get some insight at the optimistic level what is the confirmation of the different software how does it behave so now we are starting the group where we started in Tanzania so at least we started working on this with the student and it's good thing that we calibrated also the reservation free energy and ok let me talk about this guy you ask that was cool for me same problem with capsicin but now this guy has many many many applications that have capsicin now our interest is it can exist in this confirmation it can go also in this but this is in a higher percentage than this now because it is for a short period of time I mentioned that it is in a crystalline form and also in a amorphous form so we are looking now what is the behavior of water near a crystalline and how this crystalline changes the properties of water at this interface and then what is more stable than the other so here this is more stable and this is meridian and probably later with the etiquette I will look at maybe at the mini passage of water so the most interesting thing so you wanted to ask something about cocoon now you can ask here or we go on I was asking more about experiment how can you connect this to any experiment level experiment level experiment so there are many ways we can connect first for the bioassay and second just investigating the cocoon itself looking for example on the confirmation of stability aggregation processes we can people think experimenters can do I don't know how they do but I think they can do and try to establish which is more stable than the other so if you know experimenters have done experiment so I know experimenters they have done understanding the crystal structure of this and the changes whether it's crystalline or it's amorphous so this is what I know we started working on the crystal structure from the experiment of people yes, yes now last but one drug assemblies let's take this in a cross-amide experiment way this chemistry is very interesting so look at this changing at this this hydro angle these are two different so when it is changing when it is like this this is a bit of foam and then once it flip up here it gives the alpha foam and experimenters have given two answers other experimental groups say this is the most stable and this is the least stable other experimental groups say we don't see the difference and the other say most of this is to be stable so we want to say okay what is the real reality of what is happening so we we did the molecular dynamics simulation in water because it's for a server so we want to understand the behavior of this in water so we have okay let me go here so we have different system so we have 4 or 6 and the 150 a monomode of necrosis in water so we say this through how they aggregate it by the for a server so interesting of the aggregate forming a chair sitting in the office and forming something like this but what is right that stability is the anti-parallel interaction spread by high cation interaction and then we say what should be done so we measure the solubility for each interaction system and the interaction energy for aggregation and for the solubility to the solubility so we call this the vice versa when the solubility energy is too smaller than the aggravation energy it's very very strong like for this one the interaction energy it's very stable and then the solubility was very very low and it goes into that way so we said okay this poor solubility is because of this interaction this is the dominant in the what should we do to improve the solubility and the co-crystallization chemists normally they interested in functionalization but that has the risk of reducing the increases of 50 oxygen energy open the question for for anyone interested is this natural product we don't know how it works but it has the antimatter around the world because of its property wow let me finish so with the with the group in the university this month we have established a plan to look into the solubility and the free energy and the progression mechanism of different solvent hopefully come up with something and the last thing I talk about addressing the solubility problem of natural product by nanotechnology and my battery will die now soon I use this example here that more likely when we need to give strong biological activity need to cross the membrane and then to have to be absorbed and this some more likely like lethician they like the membrane and they can act as good radicalia so we investigated the ability so this is the lethician and this is necrosomite that I'm talking about and it goes on the free pin like at the end of the way so we say to capture now more detailed information let's start with a very small system and let's see how it self-assembles and how does it load the molecule and how does it load it and deliver so before it started loading this model self-assemble this one into a bivariate and then it started crafting the system like here now it has assembled like the head detail and the hydrophobic data here this necrosomite the hydrophobic layer of it then is looking interacting with the hydrophobic cycle of it and the hydrophobic is in dynamic detail and then it said ok this is then it do a meister for a very long very long time so this will be a question how do we communicate the science to the community and finally I acknowledge a lot of people working together supporting together by group and many many people in ICTP and of course for me to do this science there thanks to Ari and to everyone to and many many people that have been working with them grazie mille santa sana this is the end of my talk very good so how is your relationship with the industry so I'm curious about what I think it was amazing yes so the relationship with the pharmaceutical industry yes so if any yes well back home to Tanzania you know we have one problem the academician the very big gap between the academician and the pharmaceutical industry so we are trying to bridge so that we can work with them directly so we have a relationship but not good so we struggle to make close the relationship so are they interested I think that they might because they have this very structure or maybe natural stuff or not they might be interested in not coming in contact with them many the problem was more the contact than the fact that yes trying to make efforts to contact them but we have not succeeded maybe what we do just with our lab we cooperate on these things yeah thanks a lot of course that is the thing maybe we are asking here how should we go on the science to the 90s scientific community there is a question there was a raise on ah so I maybe I should not share yes what is happening I think the picture is here I don't know sorry I don't know what is happening I don't know sorry what is happening maybe I should not okay now it should work yes there is a question in the I don't see any questions yes there was a raise on ah okay so let's see any questions from online questions there was a raise on ah any questions Daniel yeah thank you for the talk actually ah it's quite interesting you could present so many results from different angles you know to put all this together within a short time you present your talk present to your talk is quite impressive now you are in one side showing structure based drug design methods including molecular dynamics and all of that docking and all of that then on the other side you are showing legal based approaches like like tannin motor similarity like pharmacophobic methods and so on and you are trying to fuse all of that together and I think that there's one thing we should not forget that similarity methods themselves are not sufficient because two similar molecules with very high tannin motor similarity may have complete two different modes of action so activity and mode of action so two molecules may have the same activity but different modes of action so I would have been really impressed if you could take one case scenario and show clearly how you use molecular dynamics use molecular docking for macrophore and similarity search methods and how your results are converging together because when there are many there are many many examples at the end of the day I was a bit confused so more or less if there was you could take one case like one case study one or two case studies show how these approaches come together and how actually it will make a lot of sense because there are a lot of results you have in there and we have seen all the various methods how you use them but you use them in different case scenarios so at the end of the day I was a bit caught up by this yeah thanks a lot thanks a very wonderful comment and of course I tried to highlight when I was talking about Taridomite and the Remonene it's not always the case thanks a lot Fideli yes it's a wonderful comment thanks a lot for all the questions I think we have to close because the battery of Daniel is ending she's bringing him here she's bringing the battery then there's the time for another question yes please I did search you said that there's different criteria do you have to tailor the criteria for the specific problem that you're working on or is there like some sort of criteria that they find works the best for all small problems and all so yes you need to tell the criteria that for example this is my model okay it's shown some problems but I want to find a more effective so I want to say chemistry is a similar model working with a similar mechanism although sometimes the proportionality should be there so I said now that this model and this future search for model with similar features to this one this is similar maybe to 90% then I will get more and then I need to start our demo so how much do you have to change the criteria though is it always the same criteria so we have different methods each method of different criteria I think the approach is really interesting because in Guatemala we also have lots of different plants that have medical properties that are known by local so I really thank you and like on one part you mentioned that one of the processes is identifying the parts of the molecules through the extraction and that do you actually have nuclear magnetic resonance in Tanzania or do you have the test done in other places in Tanzania we don't have the nuclear magnetic resonance but we work with a group in Sweden at Uppsala University it's where we normally send our samples for characterization so normally we go to Uppsala University or in Switzerland also we have one that is proof of NMR but I'm not sure how we collaborate in Uppsala in Sweden so in your model you use maybe similarity with other pharmaceutical that you know that they are active at least this is for them but are you considering the wisdom of the community for example do you use some plan for treating a headache or something maybe including this knowledge into your model in order to maybe study the compounds that are in these plans to actually that is a very foundational product we start from Black so normally we go to the community so they will say ok we use this plant to treat this disease so we will take if they say this is a leaf so we take the part of the leaves now we go to the lab we do all this process until we get the individual more and then we go and test the individual more so the whole database is based on the things that you know from the community so the good example in not 30 years about the first enlightenment of the prosthetic gland is the problem now in Tanzania and everywhere so my parents had the same problem we remember sometimes that but now they say ok we go to the we take this seed of this fruit we grind it and then we drink then we eat it so we are interested ok let's take it and let's take it to the lab so we are still doing that experiment what is exactly the ingredient that works there some of them of course they work synergistically so we try also to combine a fractionation different fractionation in two or in one then we look at the outcome yes thanks but ultimately you get one molecule but this plant has many molecules so how do you reach to this one aha how do you reach to this one so actually yes so when we take this one for example the leaves ok so we do what we call a collaboration either you grind but you do shed drying you soak into the sorbet so the rule of sorbet too so we have different sorbet polarity non-polar and polar sorbet and then you we evaporate the sorbet we remain with the fruit extract so we can just use the fruit extract to test what is happening because it's the fruit extract actually what is they use in the local community and then we want to separate an individual bit so we take the same composition so we go to different sorbet and so we fractionate with the polarity so the other molecule will come fast the molecule will come very slowly so we take this coming fast and we use let's say you give it to check what is the possible and then we can go now from here to purify and then we use this gigantic spectroscopic tourist that they will give signals but for essential all it is to go up to here so we extract the oil and then you go to this gas chromatograph because they are easy to evaporate and they have a lot of database of this molecule so once it ionizes it gives the signal and it captures the names and it gives this is the name of this molecule this is the name of the correspondence so you have directly the names there but in the end you isolate more than one molecule but you want to see one of them or one so at the end if there are 10 molecules here for example this is an example here for example this molecule they are more than 2 here but of course they are same this is just changing the conformation here it was a very problem in the installation so we are selecting all of them but at different stages you take one but the other thing you say but now I completely there is no anything coming out thanks a lot you say again bye bye thanks a lot so bye thanks for the colleagues online and this is the end of the talk goodbye