A Personal View from Peter Hunt, Optibrium Ltd
When thinking of computational chemistry tools for mobile devices I ask the questions “why?” and “who should be interested”? This may sound somewhat negative but before one can suggest that something is worthwhile perhaps one should think about the uses and for whom, after all we have survived as chemists, for several centuries, with just a pen and a piece of paper. There was a review on the same topic by Antony Williams on the RSC website back in 2010 (http://www.rsc.org/chemistry world/Issues/2010/May/MobileChemistryChemistryHandsFace.asp) and the list of mobile resources at the Click2Drug site from the Swiss Institute of Bioinformatics (http://www.click2drug.org/directory_Mobile.html) and these are both excellent sources for what is available. The majority of apps on mobile devices, on either Apple or Android platforms, are directed towards being educational tools and here one can see that the purpose and audience are clear. The ability to find information, compound or protein look-ups, target information, reagent ordering etc are useful facilities whether on a mobile device or not. Examples are the PDB look-up apps like http://www.rcsb.org/pdb/static.do?p=mobile/RCSBapp.html , PubChem Mobile for database searching on Android https://play.google.com/store/apps/details?id=com.bim.pubchem&hl=en_GB or ChemSpider from the RSC on both iOS and Android platforms http://www.chemspider.com/about.aspx , SPRESImobile for reaction searching on iOS https://itunes.apple.com/us/app/spresimobile-by-infochem/id505308290?mt=8 , Mobile reagents universal (MORE) on both platforms http://www.mobilereagents.com/overview.html.
For other purposes the arguments for computational chemistry on mobile devices are more vague, for example I do not see computational chemists using their iPhone to run high level ab initio calculations on a typical drug like molecule even if someone does “have an app for that” (eg iSpartan on iOS https://itunes.apple.com/us/app/ispartan/id534726646?mt=8 or Atomdroid for Android http://pubs.acs.org/doi/abs/10.1021/ci2004219, https://play.google.com/store/apps/details?id=org.atomdroid&hl=en_GB ).
Obviously the capabilities of the device have to be taken into account and for most uses the important factor is communication from the mobile device to a compute server (eg Mobile HyperChem level1 http://www.hyper.com/Default.aspx?tabid=521) although even this restriction is becoming less relevant as the memory capacity increases and more data is stored and searched on the device itself; effectively turning your mobile device into a mobile library (eg ChEMBL structure searching – https://itunes.apple.com/gb/app/elementaldb/id627422287).
Retina display not withstanding most graphical output is best viewed on the large monitors with 3D capabilities and glasses to match, but the screen resolutions of tablets are improving in leaps and bounds so this restriction will soon be a non-issue. So what should we be realistically doing on a mobile device? The visualisers and chemical sketchers are plentiful and have their place to help view active sites and note down new ideas. However there needs to be a certain level of interaction with the app (such as property calculation or model prediction as found in Asteris from Optibrium/ICD or Elemental from Dotmatics) that takes it above the capability of pretty picture generator or the pencil and paper as mentioned earlier. Although not strictly computational chemistry there are more eNotebook apps or web accessible versions of lab notebooks being created. The ability of a medicinal chemist to add information to a notebook entry, or design and order reagents for a new rapid analogue library whilst still being in the lab is one that would increase their productivity. The only issue then becomes the ability of the mobile device to withstand the rigours of a chemistry lab environment. An iOS iPad app directed specifically towards medicinal chemists is being developed by the RSC and is available in its early form from the Royal Society of Chemistry website (RSC.org). This will be interesting to follow to see how it progresses.
If I were to be able to request an app for creation then I can foresee an extension to the above for brainstorm or project meetings. Everyone would have their favourite mobile device and in rooms equipped with a computer projector the app would allow linking of these devices to the projected room computer acting as a hub. The linking should enable ideas for new molecules to be swapped between each device and the hub, gathering these new molecule ideas from within the room enabling better capturing of idea flow and progression of designs. Property calculations could be done from any of the mobile devices before submission of the ideas to the hub for comments by the other members of the meeting. If other information is needed to evaluate an idea (like a docking score perhaps or novelty estimate compared to the corporate collection or patent literature) then this could be submitted from the hub or set up for later submission. This scheduling activity then leads on to project management tasks such as allocating those ideas to those in the room for synthesis, or if the compounds already exist, submission of those compounds to the relevant assay submission tool, gathering data from in-house databases and interacting with visualisation tools to help find the SAR trends within.
The above dream enhances the utility of a mobile app from being a very personal, individual, aid into a collaboration and project management tool and also allows meetings to be set up so everyone can contribute in his or her own way.
Senior Software Developer, Chemical Computing Group, Cambridge, UK
Computational Chemist (18 months contract), Agrochemicals company based in Bracknell, UK
Computational Chemist (Faculty Position)
The Department of Chemistry & Chemical Biology at IUPUI (http://chem.iupui.edu/) invites applications for a tenure-track faculty position in computational chemistry at the interface with biology, medicine, or drug discovery to begin August 1, 2015. The position will be at the rank of Assistant or Associate Professor, with tenure status to be determined based on prior experience and qualifications. The successful applicant will be expected to teach undergraduate physical chemistry and to develop a graduate course in computational methods in medicinal chemistry and/or biological simulations. We are particularly interested in applicants with research interests that complement ongoing efforts in the IUPUI School of Science and, on campus, within the IU School of Medicine. Applicants must hold a Ph.D. in Chemistry or a related discipline along with relevant post-doctoral experience. Candidates must demonstrate the ability to initiate and sustain an externally funded program of research, and be able to teach effectively at the undergraduate and graduate levels. Applicants at the Associate Professor level must have a record of research excellence, a history of external funding, and evidence of successful student mentoring.
Applicants should submit a CV, research plans, a statement of teaching philosophy, and the names and contact information for three references as a single, merged pdf file to firstname.lastname@example.org.
Alternatively, candidates can submit their materials in hard copy to:
Computational Search Committee
Department of Chemistry & Chemical Biology
Indiana University-Purdue University Indianapolis
402 North Blackford Street
Indianapolis, IN 46202-3274
Review of applications will begin late October 2013 and will continue until the position is filled.
The following meetings may be of interest to our readers:
Dotmatics European User Group Meeting, 15-16th September 2014, Down Hall, Herts
5th RSC/SCI Symposium on GPCRs in Medicinal Chemistry, 15th-17th September 2014, Basel, Switzerland
SMR: Personalised Medicine – Are we there yet?, 2nd October 2014, London
SCI Designing Safer Medicines: What Works and What Doesn’t, 13th October 2014, London
SCI Highlights in Medicinal Chemistry, 14th October 2014, London
SCI Introduction to Epigenetic Drug Discovery, 22nd October 2014, SGC Oxford
Cambridge Chemoinformatics Network Meeting, 26th November 2014, EBI Hinxton
MGMS Young Modellers Forum, 28th November 2014, London
Full text of Rod’s bi-monthly newsletters, which include med chem literature reviews in addition to the more general papers included here, can be found on his website at http://rodporterconsultancy.com/newsletter/.
A useful review of the various aromatic descriptors that have emerged over the past few years has just published from a couple of the primary exponents of the negative role of too many aromatic rings has on developability.Interesting observations of marketed versus patented compounds included the fact that only 14% of marketed drugs compared with 63% of patented compounds had more than two aromatic rings. Furthermore Ar- sp3 (the number of aromatic atoms – the number of sp3 atoms in a molecule) stays fairly constant over time with marketed drugs – varying with the ionisation state. Namely acidics had a (high) value of 4.05, bases 0.73, neutal molecules -0.22 and zwitterions -1.48. Possibly this reflects issues around solubiity for neutral and zwitterionic compounds. Amongst the other developability characteristics of a compound solubility does seem to be influenced significantly by aromaticity (above and beyond any change in lipophilicity there may be) – cf a following discussion on predicting solubility.A useful addition to the supplementary material is a .csv spreadsheet which can be used to calculate a range AROM, Fsp3, Ar-sp3, Ar/HA, and PFI from any SMILES string molecule input if JChem for Excel is installed. Apart from this aid, this is a useful review of the various ways that have so far emerged for assessing impact of aromaticity on drug design and the developability of compounds and as such is well worth a read. Bottom line really seems to be keep control of aromatic rings and don’t use an aromatic when an sp3 motif will do – the odd chiral centre is no bad thing by all accounts in helping survive development.
Finding binding sites
An interesting attempt to try to unravel binding sites which caught my eye 1 although perhaps I am not best placed to say if this adds real benefit but it brings to mind some of the recent work I have been reading for personal benefit relating to some epigenetic targets where “gold standard” crystallographic data is failing to identify intact binding sites due to the crystallisation of protein constructs rather than whole proteins. Which of course then brings to mind the dangers of trying to assess druggability of a binding site based solely on published (often incomplete) protein structures or indeed protein complexes. Perhaps looking more holistically so called poorly druggable sites are more druggable than initially thought if whole protein or whole protein complexes were considered. This was I believe some of the point behind the work Cellzome was involved with in assessing target protein in a whole cell environment rather that as expressed proteins – HDAC as a target springs to mind where selectivity of compounds in isolated enzyme systems was very different to that in the whole cell due to the impact of other proteins on the target enzyme.
While predicting/calculating any property is tricky predicting solubility has proved particularly problematic over time. It has been argued that the problem is really due to the relatively poor quality of the data sued to generate models. However a new report suggests that the real problem is that the most appropriate properties to include in models have not yet been identified. The approach used was to compare models built with a collation of literature solubility data versus a set of compounds for which solubility as carefully measured to a high degree of confidence. The conclusion was that no matter which set of data was used the models generated were equally unreliable – back to the drawing board for the modellers!.
Perhaps there is a bit of a tendency to think of proteins as generally reasonably well ordered perhaps driven by our looking at elegant crystal structures – forgetting the omitted loops and other sections too mobile to give useful co-ordinates. A full issue of Chemical reviews 1 has been dedicated to Intrinsically disordered proteins with sections introducing the field and systems of classification. There are also, amongst others, sections on looking at free energy landscapes using NMR, multisteric regulation by structural disorder in modular signalling proteins 2, short linear motifs as diverse protein interaction modules involved in cell regulation 3, looking at conditionally and transiently disordered proteins and a new ‘omic – pathological unfoldomics of uncontrolled chaos. Perhaps this really highlights the dangers of over interpreting crystallographic data certainly during target evaluation and at least in some cases when using structure based lead optimisation.
P-gp substrates and translation to man
In an extension of earlier work in the mouse 1 the Terasaki group have reported 2 on reconstruction of distribution of P-gp substrates in the cynomolgus monkey brain to help increase confidence in translation of CNS drugs into man. “this study experimentally demonstrated that the Kp brain and Kp,uu,brain values of P-gp substrates and non-substrate can be reconstructed by integrating in-vitro P-gp transport activity, P-gp protein expression levels, and the unbound fractions in plasma and brain based on BBB PPx. These results also demonstrate that in-vivo P-gp transport function at the BBB can be reconstructed based on in-vitro P-gp transport activity and P-gp protein expression levels. The key points is that expression (and activity) levels of P-gp are much closer (ratio <1.5) between cynomolgus monkey and human relative to rodent/human and that Fuplasma varies substantially across species. The team also use pH adjusted Fubrain 3 to try and more accurately reflect the in vivo situation. This gives an in vitro method for helping to give an improved estimate of effective exposure in man particularly when working with compounds with evidence for efflux liabilityFu brain was broadly similar for mouse and monkey general differing by only two fold although indinaivr did show a three fold variation which is consistent with earlier data showing a broad conservation of Fubrain while Fu plasma is more variable across species.