Part 2: What disease should I …. ? Data mining Zika-virus information

Continuing from our introduction in part 1 where we established the “what” – i.e. Zika Virus –  we will address now the “how”.
(disclaimer for the nit-picky ones: this is a casually written blog, not a full-fledged scientific article)

First – which target do we need to dig into? There is of course the virus itself. This is somewhat “easily” done by creating a vaccine against it. As far as we can tell, efforts for this are ongoing, though require still many years to deliver any positive or negative clinical outcome. It won’t be the focus of this little report – we are small-molecule people after all!

Thus, the alternative is to look into the biological pathways affected within the host by the Zika-Virus. There are currently a multitude of targets that are of interest. Generally speaking, all drugs that are developed affect a target within such a pathway (or several) – if such pathway(s) are known. In the case of Zika, several dozens of targets are under investigation which seem to show some impact on inhibiting/stopping the disease. Some targets are more promising than others.

After some digging around on the Web and reported literature, there are two sources we will be using:

ZikaVR: http://bioinfo.imtech.res.in/manojk/zikavr/ a large database dedicated to this particular virus. It has been described in detail in this research article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5025660/ (you can access it for free).

This database has done a lot of the leg-work in collecting relevant information at this single resource. E.g., results of the Zika genome compared to known viruses which allows for quick identification of known drugs. These known drugs affect certain targets, therefore allowing the assumption, that the Zika virus affects said targets in the host as well. Of course, this is still to be proven in animal models/clinical trials. But, it reduces the number of possible targets to allow for a more focused plan of attack until more details are known (e.g. new targets may emerge from the genome comparisons or certain known drug targets being less/more attractive for whatever reason, etc).

There are numerous ways and strategies to use as starting point to find new leads. Strategies can be at times a question of personal vs company taste, experience and even “philosophy”. The overall (certainly non-exhaustive) gist in our case is a structural analysis with associated data.

The second resource we will be using is a recent article by Guo-Li Ming and coworkers who describe in more details some of the most advanced candidate molecules and discuss possible combination therapies: http://www.nature.com/nm/journal/v22/n10/full/nm.4184.html

Actually, there are two more general resources which we use:

Pubchem (https://pubchem.ncbi.nlm.nih.gov/) and DrugBank (https://www.drugbank.ca/) – public accessible databases with a wealth of information. They can be searched via a web interface, downloaded via FTP or accessed via their APIs. The latter is the way we access especially Pubchem, with the help of Knime . If you are wondering at this point about Knime – please see some of the previous blog entries on this site.


Analysis Part 1

To identify known drugs (pre-clinical-, clinical-, as well as marketed ones) and (related) drug targets. From a small molecule perspective, the most interesting section of ZikaVR is the “Drug Target” section, as of October 2017, containing 464 targets: http://bioinfo.imtech.res.in/manojk/zikavr/drug_target1.php, the list of compounds is over 500 structures long! For easier and faster analysis, we exported this list containing Drugbank IDs and used Knime to continue working with these. We need to find the structures, check for duplicates, and do a clustering – if at all possible.

With the help of Knime, we end up with a list of only 14 compounds! (We will give details on the Knime workflows in a subsequent part of this blog series).

And looking more closely at the targets of these, we see that all of them belong to three main biological classes:

We will at this point not discuss a potential bias stemming from compounds entered into the ZikaVR database – we refer to the database itself on details on curation.

The next step in part one is to analyze the structures and check for any similarities. Fingerprint & graph-based clustering (in simple terms: strip all attached groups from a molecule to keep the core, then replace all heteroatoms with Carbon and finally make all bonds to single bonds; again, see upcoming part 3) and end up with only three distinct cores, whereas six of the DrugDB compounds share one common core (subgraph), all corresponding to KIF11 inhibitors:

Analysis Part 2

Looking into the above mentioned article by Ming et al. we payed particular attention to the PHA-690509 compound. It is a CDK (see e.g. this WIKI entry) type of inhibitor known to have antiviral activity. The authors disclose further structurally unrelated CDKi compounds which inhibit ZIKA replication as well.

In the supplementary material one finds a list of 27 CDKi compounds which we used as basis for further analysis. If we do a subgraph analysis of these compounds we find three such graphs for all of these (with some overlap):

So far…..

We have one structural “coincidence” based on KIF 11 (plus two other target classes which we will not use here) and ten structurally unrelated CDKi compounds (different CDK targets). Thus we intended the following:

  • Search for bio-actives based on the subgraphs shown above
  • Search for KIF11 inhibitors (target/structure related)
  • Search for CDK inhibitors (target/structure related)

We searched through Pubchem either by target-name or via the subgraphs (substructure type of search) – also, we used an activity cut-off in the case of CDKi’s of pI50 > 6 (i.e. sub-micromolar activity).  For target names we also had to do a detour to check the Assay IDs (AIDs) based on related structure IDs (CIDs) – if there is a better way that you happen to know of, please contact us, we would be delighted to hear how.

In the case of CDKi’s we found 257 structurally unrelated CDKi’s based on the subgraph search, such as

In total we found over 1450 CDKi’s (hitting the whole range of CDK1-CDK20).

In the case of KIF11 it wasn’t equally straightforward. One of the reasons is that for KIF11 the alternative name KIF1 is used… Absolutely not confusing, right? So initially we get 0 hits when we do targetname/assay related searches. With the graph based search though, we do find over 1100 analogues. If we filter by fingerprint similarity (Tanimoto >0.7, or even 0.8) we end up with a bit over 730 compounds, whereas 18 of those are reported as KIF1 compounds (meaning KIF11).


Summary

We were able to systematically identify compounds that are not necessarily structurally related (which is good to since it removes some of the starting bias), many of them are active on the same targets. There is assay data available as well, thanks to Pubchem, something we haven’t really used so far other than a generic potency cut-off in one case.

For a simple exercise as we have done here, we would claim it is acceptable if one neglects some details, e.g. the at times varying curation quality of Pubchem. You might need to drill down further into things like target names and make sure you search through all alternative names, just in case. Sometimes structures aren’t 100% correct, etc. etc.  Don’t get us wrong, in general curation is very good, but you cannot trust it blindly since “the devil at times lies in the details”.

And now what?

Now? Only now one would really get started! We have some starting points and would want to make of them. For this, there are “gazillion” things to do further with these findings. Property analysis, pharmacophore modelling, 3d-shape-modelling, etc. etc. etc. Perhaps we will revisit this in the future.

Comparison with commercial products

On a final note, if you have the possibility to search through commercial databases, you can do basically the same thing. It might even be a good idea to combine these two types of searches. A quick test with e.g. Reaxys revealed many more CDKi structures, but less KIF11 inhibitors. In the end, you have roughly an equal amount of data and would probably end up with the same conclusion in terms of what you want to focus on. For someone doing Science@home though, Pubchem and other public databases are the go-to-place.

Stay tuned for some practical examples from the above mentioned Knime work-flows in Part 3.

PS: Part of this blog will be (have been) presented at the “ICIC 2017“, the International Conference on Trends for Scientific Information Professionals, Heidelberg, October 23-24, by Dr. Fernando Huerta from InOutScience .

Part 1: What disease should I research @home? Zika-Virus as example

To dabble with basic Science@home is fun – though probably only up until the question arises “what to actually research about”?

This leads to the question on how to find or decide on any disease to start with. Since this is a rather entangled question (or rather, the answers can be), I will offer three of the simplest answers:

  • Choose whichever disease you are curious about (or have a relation to)
  • Pick a particular target that you heard (know) of and are interested in
  • Take from current news a “hot-topic” disease/target

This might sound somewhat naïve, but can be rather relevant and is used by many researchers within pharmaceutical development at least as part of the starting point. As example, my friend Fernando from InOutScience and I have been considering the Zika-Virus ourselves (hence, I will use “us/we” for the remainder of this blog series). Myself, I stumbled upon this due to the news last year (and a family interest, if you will).

What is Zika?

Zika hit the world news last year after an outbreak of epidemic proportions in South America. That the world took notice at all was (as usual?) down to economics. The 2016 Summer Olympics in Brazil and the spread to southern parts of USA.  It then though nearly equally declined by the end of the year as fast as it appeared earlier. The reasons for this still seems to be unclear for epidemiologists.

mosquitop

The virus itself is a mosquito-borne virus transmitted by Aedes mosquitoes. It leads to harmless symptoms, the most common ones’ headache, muscle and joint pain, mild fever, rash, and inflammation of the underside of the eyelid.
But: What brought this virus into the limelight is the fact that when transferred to pregnant women the fetus is at risk for birth defects!

The latter is the reason for efforts on trying to find treatments (otherwise, basic flue treatment seems to do the trick).

You can find nicely summarized facts at the World Health Organziation (WHO) webpage on Zika.

Unfortunately, as with any neglected disease (tropical ones fall most often into this category), there is no money to make in finding new medications (research & development costs versus what you can make from it….). Therefore it falls on some smaller companies as well as academic groups as major player researching these, as is the case with Zika.
You yourself can participate indirectly if you like via the WorldCommnunityGrid distributed computation project – see my blog entry here to see how. 

If you want to know more about Zika, please check out these links:

Now that we have a disease to research on – how to continue? Part 2 now available, please click here.

PS: Part of this blog series will be presented at the “ICIC 2017“, the International Conference on Trends for Scientific Information Professionals, Heidelberg, October 23-24. The presentation will be made by Fernando Huerta from InOutScience .

MolPress – Open source chemistry plugin for WordPress

So much to discover and to do – yet so little time.
Here e.g. is such a nifty thing that I will have to try out at some point, time or not since it fits the @home perspective perfectly:

MolPress is an open source chemistry plugin for WordPress.

One of my new colleagues, Alex Clark, who has been a bit longer than me in the blogosphere, is putting work into this. No need for me to reiterate what he can describe best himself – check out the Molpress page, or his blog:

Cheminformatics2.0 – MolPress

Now – to just find the time to integrate this 😀

 

Science@home for everyone – the quick and simple(st) way

Do you want to do contribute to research but don’t have the time/nerve/know-how for any kind of deeper involvement? Of course you want to 😀 !
And yes, it is possible! The answer is – distributed or volunteer computing!

This is not a new phenomena, it has been around for quite a long time now. One of the more know projects most likely is SETI@home, where you help analyze radio signals from space in the search for extra-terrestrial life.
Today, the field of distributed computing encompasses all kinds of research areas, including drug discovery. One of many summaries on this subject can be found on this blog by the OpenScientist and of course Wikipedia, on Volunteer Computing.

Thus, by allowing your computer to calculate on behalf of whatever research in question, you indirectly contribute to that project – without lifting a finger. The only thing you need to do is install a program, register yourself as user (for some you can even just run anonymously) with a tiny caveat that you also “contribute” with electricity. But hey – it’s for science, right? In addition, some projects include a fancy looking screen saver!
Don’t want to have your computer on all the time? Don’t want to be bothered while you are using your own machine? No problem, nearly all allow AFAIK several ways to restrict the client with regards to CPU/GPU usage or the time it may run or not.

Can’t decide what to contribute to? Want to contribute to multiple projects but not have multiple clients installed/have to keep track off? Then I can recommend the World Community Grid which supports out-of-the-box 7 different projects. And if I am not mistaken, with a wee bit of manual work, you can make the client run other projects via this client. And if you prefer doing something like this while playing a video-game, even that is possible, for example in EVE Online or FoldIt (these though require a bit more “work” requiring active inputs/analysis by the user and thus go beyond the idea of “simplistic” distributed computing).

Myself, I am supporting the OpenZika project, due to some personal interest in this field. Come join me and many, many others!

Click here to get started!
(Note: this includes my referral ID – don’t worry, there is no money involved, it simply gives out “badges” for “recruitment”. Use the above World Community Grid link instead if you don’t like this idea).

Awesome animation video on cancer and target Fractalkine

The enclosed video is a rather cool and awesomely made animation video of the biology of the Fractalkine receptor in context of cancer.

The reason I post this here are two-fold:

a) it’s awesome, we need more of those types of videos! Have I mentioned that it’s awesome?

b) I used to be involved in that target – and that particular compound during my time at AstraZeneca. Not so much as many of my former colleagues, but still, it does make one proud to see all that hard work come to fruition. Especially in today’s day and age of companies (smaller ones even more so) being careful about what goes into clinic or not and no longer “pump out” volumes of potentially questionable thing as it might have happened in the past.

Good luck to Kancera and hopefully it will benefit the (future) patients!

If you want to read about Fractalkine (Chemokine), you can find more information on e.g. Wikipedia: en.wikipedia.org/wiki/CX3CL1

Docking & virtual screening @home – preview

Way too many things have been happening lately, I didn’t have the time as I’d like to write new entries, one of them is the start of a new Job within the next few days 😀 [That’s a valid excuse, isn’t it?]

Anyway, a bit more complex and especially CPU/GPU heavy task is docking and receptor modelling. It depends though on what you think you want to do –

Do you just want to dock the occasional molecule(s), maybe make a nice picture, then you should be fine with a low-spec configuration as described in my post Part 1 of Drug Research @home . If you intend to do high throughput virtual screening of tens or even hundreds of thousands of compounds, you either have to have a lot of patience (in the range of days to weeks) or a lot of money for a cluster [I am not going into the possibility of using cloud-services (yet), though that would probably be an option as well].

The system I will describe is AutoDock, resp. Vina, the simplest and most “open-sourced” docking software, and combine it with other free tools for visualization, respectively preparation.

As time/computational reference: Docking a single molecule with Vina on an average modern i7 system takes ca 20-30 seconds. That’s ok for several hundreds at once. While I previously had access to Xeon based Linux Cluster, I screened 80k compounds on 12 CPUs in 10 or so days…. (well, it was a queue system shared with other users, though the way the system was set up it was more or less constantly calculation,).

Now, using Vina isn’t new and there are descriptions out there, but few deal (if at all) with automation. Furthermore, you have to pick bits and pieces from different places and combine them, which isn’t as obvious as one might think if you aren’t an expert (well, at least I don’t consider myself one in this particular field).

Until soon!

 

Abuse of open access tools and data?

As in a previous blog of mine described, it is rather simple to set up virtual compound design from the comfort of your home. Tools and data are easily accessible and hardware is cheap. Add to that a bit more hardware, maybe even a (garage) laboratory – it makes you wonder “What If”?

Is it possible that open access data is abused for criminal purposes, in particular recreational drugs? I recon it it would make sense (unfortunately) and I am sure there are more articles to be found other than the one I stumbled upon recently, dating back to 2013, by the Guardian. Though they don’t give any source or example for their (probably legitimate, imho) claim of what/were “clandestine” labs are.

Synthesis of known (recreational) drugs have been accessible since the days of Usenet newsgroups (seen them myself back in the days) and probably even BBSs. And then there is of course PhiKal, perhaps one of the main sources for Usenet/BBS in those days, before internet became bigger and easier accessible. With that know-how also follows a list of how to replace certain ingredients with household items/chemicals as replacement of otherwise only laboratory accessible items. It is so simple nowadays, a simple Google search will yield e.g. the recipe for crystal meth based on household chemicals; “Breaking Bad” in real life.

Combine the urge do to something like this with knowledge on pharmaceutical design and open access…..

Though as long as as so called designer drugs seem to be based on arbitrary testing of only slightly modified existing compounds – one of many examples fitting that picture seems to be acrylfentanyl – it doesn’t look like  open access is the culprit (yet).  It’s more the usual greed and stupidity with as fast, simple and cheap turn-over as possible – health and safety concerns have never been on the agenda. The only optimization probably is accessibility of starting materials. If there is anything valid to the above mentioned article, then of course the synthesis can go beyond your local garage and is done by “professionals” with expert equipment and chemicals. But hey, maybe I am naive and there are pro-labs doing all the typical design and test cycles as a pharmaceutical company would do…. Not that that is a good justification for illegal drugs.

It’s a rather scary thought – I am not sure what, if anything at all, can be done about this.

Perhaps the law-makers should start banning substances based on their pharmaceutical action, or generic structure (Markush like?), rather than one-by-one. I believe a similar problem exists in the area of sports & doping, were new “undetectable compounds” turn up faster than anyone has time to analyze and make new laws prohibiting previously identified ones.

I (obviously) can only recommend against any type of creating existing or new drugs – not only from a substance abuse of legal issue, but also from a plain health perspective – putting untested “shit” into your body will lead to – shitty results, plain and simple.  And if you are not a chemist doing “shit” in your garage, well, count on “shit” happening.

Drug research at home – (how) is that possible? – Part 2

Continuing on after part 1:

What to do with the tools

I’m assuming that you have (some) knowledge on how to search, what to look out for, a  workflow on the different steps required to do the job. It’s otherwise a topic on it’s own for another time. Not that it hasn’t been described before, alas, no, see just one example here:

Nicola, G., Berthold, M. R., Hedrick, M. P., & Gilson, M. K. (2015). Connecting proteins with drug-like compounds: Open source drug discovery workflows with BindingDB and KNIME. Database: The Journal of Biological Databases and Curation, 2015, bav087. https://doi.org/10.1093/database/bav087

Actual Compounds

So you identified something and want to test your hypothesis beyond in-silico. Well, that is a bit tougher – you can’t really handle and test compounds at home. Theoretically though,  you could have someone else do this part for you (order commercial compounds, synthesize something new, test in a biological assay). That is (unfortunately) not for free.

Though to obtain compounds ,if you are (or have connections to) academia or a (smaller) company, there are some interesting initiatives are available, such as within Malaria research by http://www.mmv.org/research-development/open-source-research/open-access-malaria-box, though now more broadly for pathogens at http://www.pathogenbox.org/. Then there are possibilities as described in the next section.

Once you think you have something

Actual testing aside (it never hurts), what can you do with those cool results? Well, there are a number of things – the simplest one would be: write a blog! More involved and scientifically more appropriate – at the same time more difficult – write a publication in a scientific journal or present at a scientific meeting. You could even try and patent your findings, if you have the finances. It all depends on the impact you want to have.

To go beyond a publication, if you want to be part of/follow your findings, you can contact some of the initiatives by pharmaceutical companies who are open to collaboration on new findings. For example,  Johnsson&Johnsson [jnjinnovation.com/partner-with-us], or AstraZeneca [openinnovation.astrazeneca.com], or the Medicines for Malaria Venture [www.mmv.org/partnering/our-partner-network] and many more. You can also find incubators within academia, but then you would require some contact to a research group within. The list of incubators/companies & universities is nowadays quite big and could be a topic for a separate blog entry.

If you are really in it for the money though, I think you will be disappointed. Doing drug research from home is more like a hobby just fun, in the best case though for the greater good. Having said that, should you really find something interesting and you contact any of the above mentioned initiatives, intellectual property and reimbursements will most likely be on the table at some point.

Now, start researching!

Drug research at home – (how) is that possible? – Part 1

In the current day and age of open access information, combined with cheap computing power, it is rather simple to do (some) drug research from the comfort of your home, be it as private person for fun or out of interest, or as a small (start-up) company. Actually, big pharma companies use some of the same resources combined with their own in-house data and programs as well – so why shouldn’t you?  

Where is this data? What kind of data?

There are a number of public- so called open access – databases available these days, curated over many years by high profile institutes, as e.g. the National Institute of Health, NIH for Pubchem.  Many more institutions and specific initiatives have evolved over many years, some appearing literally right now, depending on the field and data. Databases on chemical compounds, small molecules, have been around the longest, afik, with structure, properties, literature references and biological data associated.

Listing all of them would require an entire Wikipedia page (or more), and that work has already been done – you can find a substantial list here for example http://oad.simmons.edu/oadwiki/Data_repositories, though in terms of life science, on this NIH site, you can really knock yourself out: https://www.ncbi.nlm.nih.gov/guide/all/#databases_. The scientific literature has regularly some article on databases and software, as well as many blogs do, but that is outside of this scope.

More focused for our purpose of drug research, you have sites such as PubChem, BindingDBZinc, or e.g. GuideToPharmacology. I’d say with these you can get pretty far.  Curated from literature and also patents, these databases connect structures to biology, i.e. mechanism of action, structure of the target, how much is know about it (or not).  All sites and db-s are arranged differently, some you can search on the web, via an API, some by browsing, or a combination thereof. Then, there are also the semi-public databases, such as CDD-Vault – you can register and search within the public databases (all via the web, independent of your machine power), though you cannot download or batch process on the free account. It might still be worth a look at times considering you find data which is not in literature/patent based curated databases.

What will you need?

A certain understanding of the drug discovery process, chemistry and some degree of biology. If not yourself, then a good friend who might have that knowledge and can support you (though this seems like a unlikely scenario?). Some IT-skills certainly don’t hurt. Below I will focus on data-mining as the core task of the home research, methods such as docking or quantum mechanic calculations I will leave out for now.

Hardware
  • A(ny) computer – Windows, Linux, Mac – doesn’t matter.
    In my experience though when it comes to chemistry, the Windows platform still offers a broader range of both commercial and freeware programs .
  • How powerful?
    Simply put, also doesn’t matter. Sure, the more power, the smoother your experience, though for mining purpose I would go for more memory before processing power. An Intel i3 with (minimum) 16GB of RAM can get you pretty far with little money. Only for large data sets and more complicated calculations I feel this being a bit of a bottleneck. If you have an i7 or Xeon available, good for you!
    What about graphic cards? That actually doesn’t matter for data-mining and simple visualizations. Once you want to do some visual 3d-docking though, that’s another story.
  • An alternative, or even complimentary solution is a (powerful) workstation, placed “anywhere”, which could e.g. be shared with someone else sharing investment costs and then access it via any (simple) PC/Laptop via remote access, e.g. TeamViewer. Cloud computing@home so to say.
  • Reasonably fast internet connection  – for mining those web-services.
Software
  • Knime (available on all platforms) allowing for flexible, visual and fast development of search and analysis workflows.  Combined with some know-how on Java or XML and you have quite a powerful package. To start your journey, you can use some of the readily available (example) workflows before getting into details.
  • A chemical drawing program – there are a rather larger number out there, it is difficult to really make a good suggestion. Knime itself comes with a “myriad” of plugins for structural input and output, thus you actually don’t really need a separate program. Myself, I do have the free Marvin package by Chemaxon installed.
  • DataWarrior – a great package for visually guided “manual” mining, sort of “Spotfire light”, if you will.
  • Excel – or similar, can be used as light weight DataWarrior alternative, but also useful for sharing or storage (as would be Word or Powerpoint (and alternatives).
  • Scripting languages – R or Python – are not necessary, though they can make a good complement, depending on your requirements.
  • Java – also not necessary, but since Knime is built on Java, it sometimes can help for certain work-arounds.
  • XML, HTML, REST – some basics might be helpful when accessing certain services via network API.

What if you don’t know Java and such? Don’t fret, initially, I for example didn’t either. If you are though a person who is more of a “learning by doing”, then the knowledge will come automatically. Obviously, you can learn these in courses as well.

Continued in part 2.

 

SpotRM+ – potential reactive metabolite formations – batch analysis in Knime

Modelling and prediction of toxicity of drug compounds has been, is, and will be be a continuous area of interest. I won’t go into the detailed literature of this, here, I want to focus on SpotRM+’s contribution to that field:
This methodology focuses on reactive metabolite formation and avoidance as a means to reduce structure based toxicity issues. In addition, it is a computationally cheap method since it is solely based on SMARTS, carefully hand-curated ones at that. In addition to identifying certain structural features, SpotRM+ delivers one to three page monographs on the marketed (or withdrawn) reference compounds including mechanistic summaries. So it is more about learning than pure black box filtration.

SpotRM+ requires Bioclipse, a platform which has chemical data-mining in its focus. There is a disadvantage to this package – you can only run and analyze one compound at a time, batch mode isn’t possible.
According to the company Awametox AB, the batch mode analysis is a feature requested by a number of customers, e.g. for design/synthesis prioritization. And yes, it is possible – IF you use script based or workflow based tools with one of the simpler ones being Knime. For this, you require access to the SpotRM+ database itself and the standard chemistry mining nodes in Knime.
[note that SpotRM+ is a commercial package, though there is a free demo available; both are based on Bioclipse. For the mining suggested here you need the database itself which can be purchased separately]

One of the drawbacks of the database and the SpotRM+ system with regards to batch analysis is that it isn’t really designed for batch analysis. The readout usually consists of a traffic light colouring system of reference compounds and links to their analysis monographs. Thus, for batch mode to work, you need to ask what you desire of it -e.g.

  • Is a single “red” or “green” reference hit sufficient?
  • Do you want to summarize all the reference hits?
  • Combine with other data for further calculations?

In principle, anything goes, that’s the beauty of the flexibility of a package such as Knime. But, would that be sufficient for you to make a decision? I can imagine that a batch based “high quality” decision should be possible, if you combine the output with, e.g., a model based on measured ADMET data (and/or reactive metabolite data).
Independent of the latter, a basic workflow could look simply like this:


You can find more info and access to mentioned programs here:
SpotRM+:   www.awametox.com (bioclipse included; recently updated to V1.2!)
Bioclipse:   www.bioclipse.net (mainly for info, not required to download separately)
Knime:   www.knime.org