Monash University Faculty of Pharmacy and Pharmaceutical Sciences, Australia, licenses Cresset’s computational tools

Cambridge, UK – 25th November 2014 – Cresset, innovative provider of computational chemistry software and services, is pleased to announce that Monash University Faculty of Pharmacy and Pharmaceutical Sciences, Australia’s leading pharmacy and pharmaceutical science educators and researchers, has licensed Cresset’s Forge and Spark software. These applications provide extensive capabilities in SAR analysis, ligand-based molecular design and bioisosteric replacement.

“Cresset’s software is used in the R&D departments of the world’s leading pharmaceutical companies. It is great to know that our students will be using the very same cutting edge tools for their molecular design and optimization work,” says Dr David Manallack, Senior Lecturer at Monash University. “Cresset’s software will enhance our teaching methods, helping us to prepare our students for frontline roles in drug discovery and development.”

“Cresset strongly supports academic research. We are delighted that Australia’s Monash University will be using Forge and Spark in their teaching programs,” says Dr David Bardsley, Cresset’s Commercial Director. “These world class tools will ensure an excellent grounding in modern drug discovery techniques for the next generation of Australia’s scientists.”

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Proximagen licenses Cresset’s computational tools for scaffold identification and bioisostere replacement

Cambridge, UK – 18th November 2014 – Cresset, innovative provider of computational chemistry software and services, is pleased to announce that Proximagen, a company focused on the development and commercialization of novel therapeutics for diseases of the central nervous system (CNS), has licensed Cresset’s Forge and Spark software. These applications provide extensive capabilities in SAR analysis, ligand-based molecular design and bioisosteric replacement.

“Proximagen has successfully used Cresset’s computational chemistry tools in the past to generate pharmacophores from known ligands and to identify novel chemical series by scaffold hopping,” says Dr Ed Savory, Deputy Head of Chemistry at Proximagen. “We will be using Spark to identify new drug scaffolds and to make bioisosteric core replacements. Both Spark and Forge are now important and valuable components of our drug discovery program.”

“We are delighted that Proximagen have chosen to further the collaboration with Cresset,” says Dr David Bardsley, Cresset’s Commercial Director. “Cresset’s continual striving for outstanding science delivered in usable software is translating into valuable results for our customers.”

Proximagen_Cresset

Web clip: Using radial plots to visualize multiple parameters simultaneously

Optimizing the physical properties of molecules is a key goal in most drug discovery or agrochemical research projects. Molecules must be active but also need to reach the site of action, at the correct concentration and for long enough (but not too long) to be useful. In a spreadsheet these properties are listed as numerical values in columns and can be difficult to compare across molecules in a quick way. By plotting the numerical information in a radial plot it is possible to quickly visualize the data and in particular how the data fits the project requirements rapidly. Moreover comparisons of the radial plots of multiple molecules makes it possible to quickly identify trends, sort on overall fit to the project profile or find outliers enabling the team to focus on the results that meet the project goals rather than those which solve the immediate problem.

Radial plots can be used in all our desktop applications (Torch, Forge, Spark). See this in action in the web clip below and contact us to find out more.

International Symposium on Compound Design Technologies: Guiding Better Decisions in Drug Discovery, Shanghai, China

Cambridge, UK – 16th October 2014 – Cresset and Optibrium, providers of chemistry software and services, today announced the ‘International Symposium on Compound Design Technologies: Guiding Better Decisions in Drug Discovery’. The symposium will be held on Friday 21st November 2014 at the British Centre in Shanghai, China.

Of particular interest to chemistry and ADMET/DMPK researchers working in the pharmaceutical, biotechnology and academic organizations engaged in drug discovery, attendees will hear about technologies that guide them to successful small molecule and biotherapeutic drug discovery, intuitive molecular design and 3D-QSAR interpretation.

Chaired by Dr. Zhengtian Yu (Novartis), the program will feature the following presentations as well as software demonstrations by Cresset and Optibrium:

  • Integrated predictive ADMET/DMPK tools for optimizing exposure and safety in drug discovery and development, Dr. Jianling Wang, Executive Director, DMPK, Wuxi AppTec
  • Finding and applying multi-parameter rules to guide successful drug discovery, Mr. Ed Champness, Chief Scientific Officer, Optibrium
  • New methodology to design anti-T2D targeted library, Dr. Jun Xu, Professor, Sun Yat-sen University
  • Structural-based drug discovery in Shanghai Hengrui, Mr. Qiyue Hu, Associate Director, Shanghai Hengrui R&D Center
  • Matched molecular pairs and activity cliffs: the next dimension, Dr. Robert Scoffin, Chief Executive Officer, Cresset
    Practice of SBDD: The discovery of SB939, an anticancer agent in Phase II, Dr. Niefang Yu, Professor, School of Pharmaceutical Science of Central South University

Registration is free, online.

The meeting is held in association with CloudScientific, distributor for both Cresset and Optibrium products.

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Rapidly explore available chemistry space in Spark V10.2

Cambridge, UK – 14th November 2013 – Cresset, innovative provider of chemistry software and services, announces the release of Spark V10.2. This release enhances Spark’s capabilities in the lead optimization phase of a drug discovery program by recommending new molecules to make from the immediately accessible chemistry space.

Spark is a powerful tool for generating novel and diverse structures for your project. It searches chemical databases for biologically equivalent replacements for key moieties in your molecule. It has been successfully employed in scaffold hopping and the search for bioisosteres, providing novel, usable solutions for projects that have ADME, toxicological or intellectual property issues.

New in Spark V10.2 are databases of fragments derived from chemical reagents and building blocks. These enable Spark to scan the immediately available chemical space for the best possible move. The databases come from the processing of sets of commercially-available reagents with simple, chemically intuitive rules for generation of R groups. Using these rules on the reagents that they have access to gives the synthetic chemist the ability to search across of all the possible molecules that they could make today. Spark presents the results in a simple table with the physicochemical properties of the molecules summarized in a new radial plot.

“Spark has always generated novel and diverse structures in new areas of chemical space. With these new databases, medicinal and computational chemistry researchers can customise the search space to focus on results that they know they can synthesise with reagents they already have in their labs,” explains Dr Tim Cheeseright, Director of Products at Cresset.

Dr Cheeseright adds, “Intuitive visual tools make an incredible difference to interpreting and unlocking the value of our scientific results. The novel radial plot feature makes it easy and intuitive to rank Spark’s suggestions on multiple physicochemical properties.”

The new radial plots summarize the properties of Spark result molecules in an instantly readable and interpretable way. They enable the rapid visual profiling of new bioisosteres against physicochemical properties and are totally customizable and sortable. The user can create a corporate or project based profile that can be used in every Spark experiment, reducing the time taken to choose the best possible synthetic direction for a drug discovery project.

Deciphering complex aromatic SAR

The substitution of aromatic groups provides a unique tool to moderate the potency and physicochemical properties of drug like molecules. However, the huge variety of substitutions that are possible can give rise to SAR that is almost impossible to understand, with small changes resulting in large shifts in potency. In these circumstances the understanding of the causes of the observed activity cliff is critical to progressing the project aims. This is an area where we at Cresset have always felt that using molecular interaction fields gives you a head start as you can model the electrostatic and shape properties of the molecule accurately. The release of the Activity Miner module for Forge and Torch significantly improves this process by detecting automatically activity cliffs in the SAR. Below we present a case study on a small set of changes around a set of reported DPP-IV inhibitors and show how the Activity Miner interface helps find the root causes of the changes in activity.

A set of DPP-IV inhibitors related to the ligands from PDB codes 2QOE and 2P8S were extracted from bindingdb together with IC50 values for enzyme inhibition. Using Forge, PDB 2QOE was downloaded and split into reference ligand and protein. The ligand from PDB code 2P8S was downloaded as a fixed conformation and aligned to the 2QOE reference using the default ‘normal’ settings then added as an additional reference molecule. The remaining 31 compounds in the dataset were aligned using the ‘Substructure’ method to these references with the maximum score against any reference being used to choose the alignment. The resulting alignments are shown below.

dpp-iv_aligned_structures-300x136
The aligned dataset was transferred to the Activity Miner module to study the SAR around the terminal phenyl substituent. Using the activity view focused on the most active compound (shown below) highlights that the SAR around this substituent is complicated with many small changes resulting in significantly worse IC50 values. The activity view presents a central (focus) molecule, with the most similar molecules to the focus compound displayed in a wheel around it. The size of the segment represents the distance between the two molecules and the segment is colored by the disparity between the pair. Highly colored segments represent changes that result in disproportionately high changes in activity (colored red is worse activity, green is better).

DPP-IV_phenyl_activityview-300x168
It is interesting to contrast the activity view above with a classic SAR table:

row Phenyl substitution Activity (pIC50) row Phenyl substitution Activity (pIC50)
1 2,4,5-triF 8.2 6 3,4-diCl 5.8
2 2-Cl-4,5-diF 7.1 7 3-F 6.9
3 3,4-diF 6.9 8 2,4,5-triF 6.1
4 2,4,6-triF 7.1 9 4-F 6.6
5 2,5-diF 7.6

Clearly the SAR around the phenyl substituent is critical to activity but it is very difficult to decipher. However, with the combination of Activity Miner, field differences and the protein crystal structure we can get some pretty good hypotheses. (Note that all pictures below show field differences not absolute fields – regions where one molecule is more positive (red) or negative (blue) than the other.

1. The 2- substituent should have a negative field

The change of F to Cl in the 2- position (compare row 1 to row 2) is a slight increase in size but also introduces a small positive field at the end of the chlorine atom. It is interesting to note that the phenyl ring is slightly less electron poor when changing to chlorine (Cl is a better pi-donator than F). Taken together with the change of 2-F to 2-H (row 1 to row 3) there is a strong suggestion that this substituent should present a negative “end”. This is consistent with the protein crystal structure which indicates interactions with an arginine and the NH2 of an asparagine side chains.

Comparing row 1 to row 2 (top) and row 1 to row 3 (bottom) shows the less active molecules (right) are more positive at the end of the ortho substituent
Comparing row 1 to row 2 (top) and row 1 to row 3 (bottom) shows the less active molecules (right) are more positive at the end of the ortho substituent.

2. The 4 position prefers negativity at the end

Removing the 4-F from row 1 gives row 5. Moving the fluorine atom in this position round the ring one position gives row 8. In both cases the activity is reduced by the change. The smaller change in activity when going from F→H suggests that introducing a negative region in the 3 position is additionally unfavorable. Neither of these hypotheses are obvious from the protein crystal structure where both the 3 and 4 positions interact with a number of residues of various types.

Comparing row 1 to row 5 (top) and row 1 to row 8 (bottom) shows the less active molecules (right) are more positive at the end of the para substituent

Comparing row 1 to row 5 (top) and row 1 to row 8 (bottom) shows the less active molecules (right) are more positive at the end of the para substituent.

3. The 5 position must be negative at the end

All the changes that remove the negativity from the end of the 5 position result in significant drops in activity whilst those that retain the negativity, even in the absence of other favorable interactions retain some activity. For example row 4 has both the 2 and 4 fluoro atoms but is only pIC50 7.1. The reason for this becomes evident on examination of the protein crystal structure. This atom points directly at the edges of the indole from tryptophan-659 and the phenyl of tyrosine-670 (numbers from PDB 2QOE).

Comparing row 1 to row 4 (top) shows the less active molecules (right) are more positive at the end of the 5-substituent. Bottom shows the interaction of this substituent with the protein
Comparing row 1 to row 4 (top) shows the less active molecules (right) are more positive at the end of the 5-substituent. Bottom shows the interaction of this substituent with the protein.

4. The electron density of the phenyl substituent is important

This hypothesis is harder to establish as it comes from many observations. The most obvious is the change from row 3 to row 6 where there is a drop in activity from pIC50 6.9 to 5.8. Clearly this could be due to the increased size of the chlorine atoms but equally likely is the change in the electronic properties of the phenyl ring where highly electron poor rings have higher activity. This change is also observed where any of the fluorines of row 1 are deleted or where any atom is switched from fluorine to chlorine. Again the protein crystal structure helps to validate this hypothesis as the catalytic serine together with a couple of tyrosine residues point their respective alcohol oxygen atoms at the face of this ring.

Comparing row 3 to row 6 (top) shows the less active molecules (right) are more electron rich. Bottom shows the interaction of this phenyl ring with alcohols from the protein.

Conclusion

Many of our hypotheses could have been guessed at from studying the crystal structure of the 2,3,5-tri-fluorophenyl analogue in detail. However, the use of the field difference mode in Activity Miner brings the interactions into sharp focus and helps us rationalize the observations that we have. Subtle effects such as the difference between electron-rich aromatic and electron-poor aromatic rings are clearly visualized, explaining difficult and complex SAR in a way that is easy to interpret.

Our hypotheses can now be used in the design of new ligands with better IP or physicochemical properties with each design being validated against the regions of positive or negative field that we conclude to be important. Equally we could look for new ideas for this section of the molecule by using Spark together with the new reagent databases to suggest compounds (that we could make today!) that would retain the activity we have in this series while driving us into new regions of chemical space.

Is Chocolate Druggable? Using Theobromine for Desktop Virtual Screening

Dr Rae Lawrence, Cresset’s Technical Sales Director for North America, is a self confessed chocoholic.  For this blog she has spent some quality time with her favorite food, investigating the chemistry of the compounds that produce the delicious taste, desirable effects, and even the addictiveness claimed by some.  With Valentine’s Day just behind us and Easter around the corner, there’s no better time to think about this wonderful topic!

Dark chocolate has long been reputed to be a healthy dietary addition.  It is claimed to be a mood enhancer, cough suppressant, and a key nutritional supplement for cardiovascular health.

There are a few hundred pharmacologically active compounds in chocolate, including, but not limited to:

  • Theobromine and caffeine (CNS stimulants);
  • Salsolinol  – dopaminergically active, and possibly responsible for chocoholism1;
  • Anandamide  – an endogenous cannabinoid neurotransmitter;
  • Phenylethylamine – the alleged ‘love chemical’, another endogenous neutrotransmitter.

Even within this short list of components, it’s easy to see that given their CNS activity, chocolate is indeed mood-altering.

For this discussion, I will focus on theobromine, which is the component responsible for many of chocolate’s pharmacological effects and its bitter taste.  Theobromine is part of the methylxanthine class of compounds and has a similar structure to caffeine.  Pharmacologically, theobromine is a known antagonist of both Adenosine A1 and A2a receptors, as well as an inhibitor of cAMP-specific-3’,5’-cyclic phosphodiesterase 4B (PDE4B).2

This month I will use the virtual screening capabilities of forgeV10 to compare the biological properties of theobromine to a database of known pharmaceutically active compounds. For more information or a demonstration of how we compare molecules in field space, please contact sales@cresset-group.com or read our 2006 paper, which summarizes the technology and algorithms3.

Scouring the Literature and Google

When I started researching the pharmacology of chocolate, I discovered that theobromine appears in nefarious message boards4 suggesting that it can be used for a legal high when taken as an extract.  Reading a little further down the board, one user noted that theobromine itself didn’t really give the desired opiate numbing effect (considering its similarity to caffeine, no surprise there).  However, when they took kratom5 with it, the effects of kratom were more pronounced.

My interest was piqued by this, so I googled ‘theobromine opiate’ and while digging through the results, found a couple of peer-reviewed articles where it was reported that a shot of adenosine during anesthesia and surgery reduces the amount of opioid pain-killers required for post-operative recovery6.  Pharmacologically, theobromine binds to the A1 and A2a receptors, and thus, it is plausible that theobromine may have a similar, albeit, less pronounced effect – and given the anecdotal evidence provided by law-bending netizens and their search for inexpensive, legal highs, we might be on to something!

Theobromine and Adenosine, Adenine and N9-methyladeninea

Figure 1: Theobromine and Adenosine, Adenine and a N9-methyladenine.  Blue field points are negative, red field points are positive, yellow field points denote shape, and orange field points denote hydrophobicity.

Notice the lower similarity score when comparing adenosine to theobromine.  The decreased similarity is a result of the molecules’ different sizes.  When we look at the similarity scores for Adenine and the modified N9-methyladenine, the scores suggest higher similarity for the fragments.  This suggests that theobromine is likely accessing the same part of the Adenosine Receptors’ binding site as the purine piece of the natural ligand, adenosine.

Theobromine’s Anti-Tussive Properties

In a recent publication from National Heart and Lung Institute (London)7, it was demonstrated that theobromine has significant anti-tussive (cough suppressing) properties via inhibiting action potentials in the vagus nerve.  Theobromine’s activity was compared against codeine, which is the gold standard in cough suppression, and was found to be as good as codeine, and without the adverse side effects or addiction risk.  BC1036 (theobromine) is currently being developed by SEEK and Pernix Therapeutics and is has just begun Phase III trials.

Theobromine with codeine

Figure 2: Theobromine and codeine compared in field point space.  The low similarity score leads us to hypothesize that these molecules are likely to be binding to different receptors or alternate areas of the same receptor responsible for relaxation of the vagus nerve.

Desktop Virtual Screening

With the plethora of information about the pharmacology of chocolate, I thought it would be interesting to run a desktop virtual screen of theobromine against the Drug Bank’s databases8 of approved drugs using forgeV10.  Since theobromine is a fairly rigid flat molecule, we don’t need to be concerned with elucidating a binding mode.

Examination of the PDB structure9 3RFM, in which caffeine is bound in the A2a receptor, showed the binding conformation of caffeine to be flat and rigid, comparable to the theobromine conformation used as a starting point reference for the experiments outlined in this article.

Before conducting any experiments, the Drug Bank databases were visually inspected and curated (i.e., removal of counter ions, waters, etc.), leaving only the drug structure.  The structures within the approved database ranged from quite small to quite large and floppy, so in hindsight, I should have run only their small molecule database – it would have been far less stress on my poor laptop.

When aligning molecules under Normal settings within forgeV10 (or torchV10), it is important to note that the molecules are not aligned simply on structure, but on their field point patterns – it’s well reported that even when the 2D structural similarity is low, it’s possible that two compounds can have similar “personalities” (i.e., shape, hydrophobicity, and charge distribution)3.

Results

In the approved drugs database, the top hits were as expected – theobromine (0.998) and caffeine (0.942).

Theobromine with caffeine

Figure 3: Comparing Theobromine and Caffeine.

The next best scoring results were a series of PDE inhibitors that result in vasodilation and bronchodilation, and are used in treatment for asthma and COPD patients.  These are as expected, as the xanthine moiety is conserved through this set of compounds.

Theobromine and PDE inhibitors

Figure 4:  Comparing Theobromine to PDE inhibitors used for vasodilation and bronchiodilation for the treatment of asthma and COPD.

A surprising set of hits were lower scoring (0.729-0.799), but don’t share the xanthine scaffold.

Lower scoring comparisons with theobromine

Figure 5: Lower scoring comparisons for Theobromine.  The similarity scores aren’t fantastic, but they are still worth a look.

Azetazolamide is carbonic anhydrase inhibitor used as an anti-convulsant and to relieve intracranial hypertension; Pyridoxal and Pyridoxine are forms of Vitamin B6; Menadione is Vitamin K3;  Metharbital is an anti-convulsant barbiturate with similar properties to phenobarbital; Stavudine is a nucleoside analog reverse transcriptase inhibitor (NARTI) with some activity against HIV; and Oxitriptan is a chemical precursor to serontonin and melatonin, and is sold as a nutritional supplement for anti-depression and insomnia.

It is noteworthy that if we look at the rotamers of the hydroxyl groups of pyridoxal and pyridoxine, the alignment score decreases.

Could chocolate be a vitamin/anti-depressant truth-serum that helps with high blood pressure and HIV?  Seems like a snake-oil claim, like those single cures sold in the late 19th century that simultaneously cured diarrhea and constipation!

These lower scoring results are on the borderline of what I would consider worth a peek, but at the same time, it’s interesting to note that there are some similarities to theobromine – perhaps a re-profiling study is in order? (See Rob Scoffin’s recent posts for more information about teaching old drugs new tricks).

Summary

In cases presented above and examining the hits from the desktop virtual screens, it’s clear that the pharmacological activity of theobromine is both exceptional and diverse.  Being a small molecule, it’s no surprise that this fragment can access binding sites in a variety of targets to achieve medicinal effects!

To answer the question of whether chocolate is druggable, given the success of the theobromine anti-tussive drug that is currently in Phase III trials, I’d have to say “yes”.  That being said, I don’t think we have enough evidence here to support confiscating Cadbury Easter eggs from your children next month with the explanation that they contain “drugs”…

Until next month,

Rae

References and Citations

  1. J. Ethnopharmacol., 2000, 73, 1530159; Physiol. Behav., 2011, 104 (5), 816-822.
  2. http://www.rcsb.org/pdb/ligand/ligandsummary.do?hetId=37T
  3. J. Chem. Inf. Model., 2006, 46(2), 665-676.
  4. www.legalhighsforum.com/archive/index.php/t-2758.html
  5. Kratom is a traditional Thai medicine used to treat chronic pain and opioid dependence. It is not currently regulated in the USA or Europe.
  6. Eur. J. Pharmacology, 1998, 347 (1), 1-11.; Anesthesia & Analgesia, 2007, 105 (2), 489-494.; Korean J. Pain, 2011, 24 (1), 7-12.
  7. The FASEB Journal, 2005, 19 (3), 231-233.
  8. DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS. Nucleic Acids Res. 2011 Jan;39(Database issue):D1035-41. 
PMID: 21059682;  DrugBank: a knowledgebase for drugs, drug actions and drug targets. Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M. Nucleic Acids Res. 2008 Jan;36(Database issue):D901-6. 
PMID: 18048412;  DrugBank: a comprehensive resource for in silico drug discovery and exploration. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J. Nucleic Acids Res. 2006 Jan 1;34(Database issue):D668-72. 
PMID: 16381955.
  9. http://www.rcsb.org/pdb/explore/explore.do?structureId=3rfm

Additional Notes

Safety First – Theobromine is extremely toxic to dogs (LD50 – 300 mg/kg). Please be careful to keep your chocolate treats (especially dark chocolate) out of reach of pets.  Chocolate + Dog = Expensive visit to the vet

Adenosine A1 Antagonism

The Adenosine A1 receptor is a GCPR receptor for which adenosine is the endogenous ligand, and has been found to be involved in sleep promotion by inhibiting cholinergic receptors in the basal forebrain, and also present in the vascular system’s smooth muscle to regulate myocardial oxygen consumption and blood flow through the heart muscle.

Adenosine A2a Antagonism

The Adenosine A2a receptor is a GCPR receptor (PDB:3EML) with adenosine as the endogenous ligand.  A2a regulates myocardial oxygen consumption by vasodilating coronary arties, which may result in hypotension (decreased blood pressure).  In the brain, it regulates the release of neurotransmitters glutamate and dopamine, and has been indicated as a potential target for treating Parkinson’s Disease, addiction, and mood disorders.

Great Communication Makes a Great Consulting Project

In this series of blogs, Dr Martin Slater, Director of Consulting Services at Cresset, talks about what makes a great consulting project.

In the best discovery pipelines, computational chemists work closely with medicinal chemists to help direct and refine lead generation and optimization.  Making this work in an outsourcing model is a continual challenge for all parties.

It’s fair to say that good communication can make or break almost any scientific project, but it is particularly important when working in a consulting model.  We inevitably work in different locations to our clients, often across time zones, and of course we come across many different corporate cultures.

How closely we work with our clients depends absolutely on the individual client.  A few customers prefer a hands-off approach; they give us the input and we give them the results.  Provided the project has been well defined and expectations set accordingly, that can work well.  But the real strength comes from a more collaborative model.  Ideally, there will be a constant stream of information between the consultants and the client.

For example, a few years ago Cresset developed an extremely close working relationship with a US biotech firm.  We would speak on the phone several times a week to discuss the results we were getting.  Our initial collaboration was a hit finding project.  They developed the hits as we reported them.  We became an integral part of their team, giving their chemists our view of each compound from a computational point of view and discussing different ways to optimize them.

Success led to further projects and more extensive collaborations.  In the end, they bought our software and built their own in-house computational chemistry team.  Our work with them resulted in a patent, and eventually they were acquired by a major US corporation.

I mentioned already how important it is to define a project clearly and to set realistic expectations of success.  Making sure everyone understands the deliverables of a project, the methods we plan to use to get there, and the time frame makes communication during the project much easier.  Getting these process-oriented aspects defined clearly also means that administrative issues do not get in the way of the science.

Finally, it is important that we let our clients know from the start how likely we are to succeed in a project.  At the end of the day, we can define deliverables, but we can never guarantee what the results will be.  After all, we are engaged in scientific research for our clients.  But, I am pleased to say that we have many successes, and many satisfied customers who have come back to us again and again.

In my final post on consulting I’ll discuss some of the business models we use for consulting projects.

All posts in this series:


Dr Martin Slater,
Director of Consulting Services

 

Switching Series

When researchers find their promising lead series facing IP issues, they are faced with a tough decision. Do they change series—possibly to a candidate that doesn’t show the same level of activity against the target—or do they spend a great deal of time and money screening the totality of the corporate database for a new prospect? In this situation, scaffold hopping and virtual screening are effective and efficient ways to identify a new series to move the project forward.

Read the full article by Martin Slater and Katriona Scoffin in Drug Discovery and Development Magazine.

Forge: Independent Review by Chris Swain

Chris Swain (Macs in Chemistry) has independently reviewed this latest offering from Cresset. Whilst a new product, those familiar with FieldAlign and FieldTemplater will recognise much of the functionality. Forge

Read the full review here…