Bucket List 2019-05-15T14:25:40+00:00

Accelerating the life sciences ecosystem

MedChemica Bucket List

Accelerating the life sciences ecosystem

The MedChemica Bucket List.

The MedChemica Bucket List

BucketListPapers 11/100 : Heroic tabulation of LogP data enables first calculations

In compound design, and particularly drug design, the concept of lipophilicity is key. A measured partition co-efficient between water and octanol serves as a predictor of further properties and drug developability. Leo, Hansch and Elkins set out to compile multiple measurements from the literature to form the basis of further understanding of molecules interacting with biological systems. Without this “ClogP”, a computer calculated value we take for granted so much, would not exist. A job very well done.

“Partition Coefficients and their Uses”: Leo, Hansch and Elkins:

Chem Rev. 1971;71(6):525–616

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BucketListPapers 10/100 : Just how do you store molecules in a computer efficiently?

At the heart of handling chemical information within computers are methods to store complex structures accurately, uniquely and in a searchable manner. This early paper from Morgan describes one of the techniques that allowed the registration of compounds to CAS. Computers clearly deal with numbers quickly, so the further encoding of a structure into a bit number allows very fast comparison. The algorithm described within became the basis of the very widely used Morgan fingerprints and now is at the heart developments in convolutional neural networks.

The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service. H. L. Morgan

J. Chem. Doc.1965,5,2,107-113

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BucketListPapers 9/100 : Novartis and NextMove: Big Data having patents for breakfast

Five years after Roughley and Jordan’s seminal approach looking at medicinal chemists preferred reactions by hand, the folks at NextMove and Novartis used automated natural language processing to analyse >200,000 patents and extracted over 1.1 million unique reactions.  Using the Roughley and Jordan reaction typing they then classified the reactions.  With this much larger data set they could analyse the evolution of reaction types, for example with carbon-carbon bond formations they see the switching from phosphorus ylid to palladium catalysed cross couplings as the Suzuki and Negishi reactions have been applied in drug hunting research. Still however alkylation and acylation of heteroatoms remains a key process. They also analysed the properties of the products of reactions where unsurprisingly compounds grow in size and rigidity over the 40 year period reviewed.

This scale of work would never have been possible without automation and now it’s hard to see why anyone would ever go back.

“Big Data from Pharmaceutical Patents: A Computational Analysis of Medicinal Chemists’ Bread and Butter” by Schneider, Lowe, Sayle, Tarselli & Landrum

J. Med. Chem.(2016), 59, 9 ,4385-4402

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BucketListPapers 8/100 : Roughley and Jordan – The MedChem Toolbox – What’s in yours?

The authors surveyed the publications in three medicinal chemistry journals in 2008 covering 139 papers describing the synthesis of 3566 compounds and employing 7315 different reactions. They categorised the reactions that had been used and identified surprising trends such as the frequency of C-C bond forming reactions being about 10 %.  They highlight the 10 most frequently employed reaction types (Table below) and that an average medicinal chemistry synthesis used 4.8 steps per compound (Graph).  They finish with some challenges to chemists working in industry and academia.

For what happens when AI comes to reaction analysis see our next post…..

“The medicinal chemist’s toolbox: an analysis of reactions used in the pursuit of drug candidates” by Roughley and Jordan.

J. Med. Chem.(2011),54,10, 3451-3479

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BucketListPapers 7/100 : Hagmann – Fluorine in MedChem

This survey of the impact of fluorine on medicinal chemistry highlights that fluorinated molecules have accounted for about 5-15 % of approved drugs over the course of decades.  Fluorine has often replaced hydrogen or oxygen in earlier lead compounds and retained effectiveness.  The ability of fluorine to make interactions with proteins and to affect pKas is discussed and some of the methods for introducing fluorine into a lead molecule are highlighted.  Some case studies of drugs that benefit from a fluorine (either by improved pharmacokinetics or potency) are showcased and a final section suggests that introducing fluorine could reduce metabolism sufficiently to make drugs that are excreted intact into the environment; our own findings suggest that this latter effect is not really to be expected – fluorination increases metabolism as often as it decreases it when a comprehensive survey is made using matched molecular pairs: https://pubs.acs.org/doi/10.1021/jm0605233

“The many roles for fluorine in medicinal chemistry” by Hagmann.

J. Med. Chem.(2008)51, 15, 4359-4369

DOI: 10.1021/jm800219f

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BucketListPapers 6/100 : Standard Precision Glide (SP) – a step change in docking technology.

The paper presented an advance in docking technology that has been at the heart of Schrödinger’s software suite since. Standard Precision Glide (SP) is introduced in detail and includes terms for hydrophobic interactions, hydrogen bonds of charged and neutral types, metal interactions, a rotational bond penalty (for conformational entropy), coulombic and van der Waals terms.  In addition a solvation term considers water molecule presence. This glidescore is used to predict binding free energy and a combination of glidescore with molecular mechanics interaction energy and ligand strain energy to select docked poses. It is curious that it was necessary to scale the van der Waals’ radii (effectively shrink the atoms) to fit some known actives into their cognate binding sites. The effectiveness of the approach for correctly positioning ligands in the crystal structure they came from was compared to that with GOLD and FlexX. The test set used included those used for these other approaches and involved 282 complexes taken from crystal structures. To avoid bias from the ligand geometry, these were all created from scratch from 2D structures.

“Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy”  by Friesner, Banks, Murphy, Halgren, Klicic, Mainz, Repasky, Knoll, Shelley, Perry, Shaw, Francis and Shenken.

DOI: 10.1021/jm0306430

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BucketListPapers 5/100 : Lipinski – At number 5 The Rule of 5.

What else could be at number 5? In this article, the authors consider the properties that influence the solubility and permeability of drugs. This was partly a response to the introduction of high-throughput screening to drug discovery which yielded compounds that had never been tested in cells or in vivo and consequently had properties that need not be consistent with good solubility and permeability.  They showed that there had been an increase in higher molecular weight compounds and an increase in logP of compounds in the previous decade. The properties of a set of over 2000 drugs were considered (with a selection criteria for clinical exposure likely to favour orally bioavailable drugs).  Of these, about 10% was found to have a clogP above 5, about 11% a molecular weight above 500; such compounds are likely to have poor solubility.  A crude estimate of the number of hydrogen bond donors (number of OH + number of NH) and even cruder estimate of the number of acceptors (number of O + number of N) were also examined; only 8% of the drugs have donor count above 5 and only 12 % an acceptor count above 10.  These latter groups likely suffer from poor permeability. The period since this paper has seen a host of attempts to derive “rules” for other types of compounds (fragments, leads etc) although more nuanced views have also emerged.  Personally, we think that the prompt to consider the importance of solubility and permeability was a good one but the introduction of the idea of “rules” about what a drug looks like was a misstep.

“Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings” by Lipinski, Lombardo, Dominy and Feeney.


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BucketListPapers 4/100 : Optimising pKa – The kernel of an idea about molecular interactions

The sulphonamides exert their bacteriostatic effect by competing with p-aminobenzoic acid.  To gain a deeper understanding of the structure-activity relationship, the activity and pKa of 50 sulfonamides were measured.  This required measurements that were as free from confounding influences as possible – an approach that prefigured much of the work undertaken in target-based drug discovery using all the latest tools of molecular biology since the 1990s. It was found (see graph) that the sulphonamides show a maximum in activity at pKas around 6. This is explained by a requirement for the sulphonamide to be in its ionised form (as might be expected for competition with p-aminobenzoic acid) but that the anionic form must not see the charge so stabilised by delocalisation that the SO2 does not carry a maximal negative charge. These are the kernel of key ideas about understanding and optimising molecular interactions with a binding site that underpin much modern drug design.

“Studies in Chemotherapy. VII. A Theory of the Relation of Structure to Activity of Sulfanilamide Type Compounds” by Bell and Roblin  J. Am. Chem. Soc.(1942), 64, , 2905-2917

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BucketListPapers 3/100 : Hansch – Bringing Molecular Descriptors together

Various scientists had been gathering experimental data from which to extract descriptors that could be used to rationalise electronic (sigma values), size (molar refraction, MR and molecular weight, MW) and hydrophobic (pi values) effects caused by substituents, particularly on aromatic rings.  Hansch and co-workers aimed to bring all of these descriptors together in one place as a resource for those aiming to explore quantitative structure-activity relationships; an example of 4 groups that span the different types (polar vs hydrophobic, electron-donating vs withdrawing) is shown in the table.  Hansch et al.’s compilation of values remains a good starting point to understand the effects that various substituent types can exert, although Hansch and Leo went on to compile an even more comprehensive set: https://pubs.acs.org/doi/abs/10.1021/jm950902o. Some of our own work in matched molecular pairs has seen us effectively computing analogous values for the effect of aromatic substituents on solubility, plasma protein binding and other properties:  https://pubs.acs.org/doi/10.1021/jm0605233


Group Pi Sigma(meta) Sigma(para) MR MW
Me +0.56 -0.07 -0.17 5.65 15.0
F +0.14 +0.34 +0.06 0.92 19.0
NH2 -1.23 -0.16 -0.66 5.42 16.0
NO2 -0.28 +0.71 +0.78 7.36 46


“Aromatic Substituent Constants for Structure-Activity Correlations” by Hansch, Leo, Unger, Kim, Nikaitani and Lien.

DOI: 10.1021/jm00269a003


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BucketListPapers 2/100 – Free / Wilson asks “have we made the best combination of substituents yet?”

In this paper Free and Wilson analyse the variation in biological activity caused by substituents at various positions in a series of molecules.  They apply a simple least squares fitting to attribute a contribution that each substituent is making to the observed activity. This analysis of variance has become a fundamental tool for medicinal chemistry.  It is assumed that the SAR is additive although the possibility of additive and non-additive groups is discussed. The method should always be considered whenever the question is asked “have we made the best combination of substituents yet?” and as such should be used in almost every drug discovery project.  Recent developments building on the Free and Wilson approach have explored how many compounds must be included in the data set to ensure satisfactory values for the contributions of each group, for example:

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“A Mathematical Contribution to Structure-Activity Studies” by Free and Wilson. DOI:10.1021/jm00334a001

BucketListPapers 1/100 : The First Hint that Lipophilicity might be important….

Our first BucketListPaper is from 1899 and introduced the idea that the activity of anesthetics might correlate with  lipophilicity (as measured by partition into oil from water).  This first hint that lipophilicity might be a key property, as found by Overton and Meyer, kick-started the field of QSAR. The papers formed the basis of a theory of anesthesia, which still intrigues and stimulates new scientific studies.

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  1. Overton E. Studien über die Narkose Zugleich ein Beitrag zur Allgemeinen Pharmakologie. Jena, Germany: Verlag von Gustav Fisher, 1901. Meyer H. Zur Theorie der Alkoholnarkose. Arch Exp Pathol Pharmakol 1899;42: 109-18
  2. Graph abstracted by Campagna et al.: https://www.nejm.org/doi/full/10.1056/NEJMra021261)