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Synthetic Methods of Organometallic and Inorganic Chemistry focuses on the needs of the laboratory researcher. The series contains reliable experimental procedures organised according to the periodic table. The series addresses the needs of students and experienced researchers alike. It contains:. This series is designed as a bench top tool and contains detailed and reliable experimental procedures.
This work is extremely comprehensive and brings an expert opinion to your work top. It aims to address the needs of the laboratory researcher, presenting accurate information on important starting compounds, which are organized according to the periodic table. It provides extensive cross referencing and the physical properties of the materials discussed. DE EN Thieme. Medizinische Woche: Der Mensch hinter den Daten Altogether , different fragment types have been obtained by fragmenting the PubChem structures.
Only a relatively small number of fragments, namely 0. The most common fragments are shown in Figure 2. The most common fragments present in the million PubChem molecules. The "A" represents any non-hydrogen atom, "dashed" double bond indicates an aromatic bond and the yellow circle marks the central atom of the fragment. The frequency distribution for the whole fragment set is shown in Figure 3.
As a result the frequent fragments have positive scores and less frequent fragments have negative scores.
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The whole approach is based on a simple assumption that the fragment frequency is related to their synthetic accessibility — substructures which are easy to prepare are present in molecules quite often, those which are difficult to synthesize or are unstable are rare. The complexityScore is simply a number that characterizes the presence of complex structural features in the molecules.
It is calculated as a combination of ringComplexityScore, stereoComplexityScore, macrocyclePenalty and the sizePenalty. This is close to the way in which chemists assess molecular complexity themselves.
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The ringComplexityScore characterizes complexity of ring systems in molecules which is probably the most important factor influencing molecular complexity based on detection of spiro rings and ring fusions. The stereoComplexity penalizes molecules with many potential stereo centers. These factors are calculated as:. After subtracting the complexity penalty from the fragment score, the result normally in the range -4 worst to 2. In the rest of this publication we will use the term SAscore for this value.
We did not make any attempts to find optimal weights of complexity and fragment contributions as was done for example in the study [ 9 ] where these weights have been optimized to fit the ranks assigned by chemists. Our dataset 40 molecules was relatively small and any optimization of parameters would probably lead to overfitting. The complexity parameters act in this sense rather as "indicator variables" increasing the SAscore for molecules containing synthetically problematic features. Distribution of SAscore for natural products, bioactive molecules and molecules from catalogues.
To illustrate the performance of the new SAscore, its distribution for , synthetic molecules from catalogues of commercial compound providers not used in the training process , , bioactive molecules randomly selected from the WDI [ 14 ] and MDDR [ 15 ] databases and , natural products from the Dictionary of Natural Products [ 16 ] is shown in Figure 4. The graph is consistent with the common presumption that natural products are much more difficult to synthesize than "standard" organic molecules. Bioactive molecules have their SAscore somewhere in the middle between these two sets.
This graph should provide some feeling about the meaning of the score and how it is distributed in different molecular data sets. To make the SAscore as broadly available as possible at Novartis, we implemented the algorithm in the Pipeline Pilot environment [ 13 ]. PipelinePilot protocols are used routinely at Novartis to support various drug discovery activities.
The heart of the calculation protocol is the "SAscore Calculator" component, which is a custom component written in PERL, where the actual calculation of the score described in the previous section is implemented. The speed of the protocol is sufficient to process large datasets; SAscore for , molecules may be calculated in about 3 minutes. The implementation using other cheminformatics toolkits, however, should be straightforward. Access to simple molecular characteristics such as molecule size, number of stereocenters, presence of macrocycles etc.
Also the generation of atom-centered fragments should not be complicated. Actually the initial prototype implementation of this algorithm has been done by using the Molinspiration molecular processing engine [ 18 ] using the HOSE type fragments [ 19 ] implemented there, and the results were practically identical to those of PipelinePilot implementation.
As mentioned in the introduction, it is not easy to validate the performance of the synthetic accessibility score, because there are no experimental measures or objective molecular characteristics we can compare it to. In order to validate our algorithm we decided therefore to compare the SAscore with an "ease of synthesis" ranking assigned by synthetic chemists. For this purpose 40 molecules were selected randomly from the PubChem database in such a way that the whole range from small to large molecules was covered.
Stereochemistry information was discarded, because it is not used directly in the calculation protocol. The number of possible stereoisomers, however, is used in the generation of the complexity part of the score, so the stereocomplexity is captured in this way. Nine Novartis chemists, with long experience in various medicinal chemistry projects, were asked to rank these molecules.
To make the ranking process easy, a simple web interface was prepared where all 40 molecules were displayed along with a menu which allowed selection of a score between 1 and The "chemist score" we use in the rest of this article is simply an average of 9 scores entered by chemists Figure 5. We want to stress here that this validation experiment was performed only after the development and implementation of the SAscore protocol had been completely finished, and therefore the results could not be used in any way to "tune" the calculation algorithm.
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Average of chemist ranks for 40 test molecules blue compared with the computed SAscore red. Error bars on blue points indicate standard error of mean of estimations by 9 chemists. The agreement among chemists in their rankings is quite good, the r 2 ranges between 0. For a few molecules, however, scores by some chemists differ by 6 or more ranks, and for 7 molecules out of 40 the standard deviation is above 2. Average standard deviation for all 40 molecules is 1. The chemists seem to agree on scores for very simple and very complex molecules better than for structures in the middle region as mentioned already in [ 9 ].
Our results are consistent with the outcome of previous studies, indicating that in order to use ranking by chemists as a reference, one has to use the average of several estimations that smoothes somehow the high individual variation. The correlation between calculated SAscore and the average of chemist ranks is shown in Figure 6.
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The chemist score correlates also highly with molecule size r 2 for correlation with the number of atoms is 0. This correlation, however, is somehow artificial, caused mostly by the fact that our set contains several relatively small molecules, and on the other side also rather large molecules, about which, as already discussed, chemists agree very well on. We are, of course, aware of the fact that our set containing only 40 molecules is not large enough to draw too general conclusions from the results.
The sole purpose of this exercise was to check whether the calculated SAscore correlates with assessment of ease of synthesis by chemists. The data presented here clearly indicates good support for validity of SAscore with both its components molecular complexity and fragment score being important for its good performance.
Particularly large differences between chemists and computers could be seen for molecules A and B, both shown in Figure 5. A is a highly symmetrical molecule, which makes synthesis easier, but this factor is not considered when computing the SAscore. We plan therefore to introduce recognition of molecule symmetry in the next version of our SAscore. Another example where the chemist score and SAscore differ significantly is structure B. In this case chemists overrate the complexity of synthesis. On a first look, the molecule with a central scaffold consisting of 4 fused aliphatic rings indeed looks large and complex.
When checking PubChem, however, more than 39, molecules with this particular central scaffold can be found. This system may be actually easily synthesized by a sequence of Diels-Alder reactions from simple starting materials [ 20 ]. This example nicely illustrates how the fragment score can recognize easy to make substructures even without the necessity to rely on the reaction databases.
In order to get a better understanding of the fragment contributions in the SAscore method, it is helpful to study the most common fragments depicted in Figure 2. They can be grouped into three general groups. The first group consists of frequent side chains. Fragments 5 methyl , 19 methoxy , 20 hydroxy , 21 fluoro , 23 ethyl and 27 chloro belong into this category. Fragments 18 and 22 encode a methyl group in a specific environment: attached to an aromatic ring and to an aliphatic carbon. Fragment 26 describes a 6-membered aromatic ring with maximally one substituent and maximally one heteroatom, which must be identical with the substitution site.
With the exception of the relatively rare pyridinium group, the simple phenyl group shows this pattern, and therefore 26 can be also counted as a typical side chain fragment. These side chain fragments are also among the most frequent substituents identified in [ 21 ].
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It is worth noting that many simple, mono-substituted 5-ring hetero-aromatics often used as side chains, such as thiophene, furane, or pyrrole, share fragment 3 regardless of whether substitution is in position 2 or 3. These side-chains are typically available for all types of building blocks and, with the exception of the hydroxyl group, do not generally interfere with most chemical linkage reactions used in parallel synthesis. Another group of fragments is directly related to these typical linkage reactions. Each molecule synthesized with one of the linkage reactions listed in Table 1 contains at least two of the most frequent fragments as shown in Figure 2.
The comparison of the most common fragments with the RECAP bond cleavage types Figure 2 in [ 22 ] shows that of the 11 RECAP cleavage types only "olefin", "quarternary nitrogen" and "aromatic nitrogen — aliphatic carbon" are not represented by one of the fragments shown in Figure 2. When comparing the cleavage types with the most common fragments, it is noteworthy that the cleaved bond is not always included in the fragments listed in Figure 2. Fragments containing these bonds are often characteristic for one linkage reaction related to one cleavage type, whereas the most common fragments are those which cover more than a single linkage reaction; for example fragment 8 representing carbonyl groups in general or the even more generic fragment 2 describing a carbon atom with one double and two single bonds to non-H atoms.
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A third group of the most common fragments generally represent frequent structural features. Fragments 1, 3, 7, 9, 13 highlight the prevalence of aromatic rings in the space of easily accessible chemistry. Fragment 14 represents any aromatic nitrogen. Usage of piperazine as a linker is represented beside the fragments listed in Table 1 and also by presence of fragment A novel methodology to calculate synthetic accessibility score of drug-like molecules has been developed.
The method is based on the combination of molecule complexity and fragment contributions obtained by analyzing structures of a million already synthesized chemicals, and in this way captures also historical synthetic knowledge. The method provides good reliability and is sufficiently fast to process very large molecular collections. The application area of the SAscore is to rank large collections of molecules, for example to prioritize molecules when purchasing samples for screening, support decisions in hitlist triaging or rank de novo generated structures.
Despite the good performance of the SAscore documented above, we are well aware also of limitations of this method. The SAscore cannot compete with more sophisticated approaches for estimation of synthetic accessibility which reconstruct the full synthetic path, in cases when the results are critical, for example when making decision about selection of a development compound from several candidates.
And the ultimate measure for assessing synthetic accessibility of complex organic molecules still remains to be a cumulative experience of skilled medicinal chemists. Drug Discov Today. Ertl P, Rohde B, Selzer P: Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J Med Chem. J Chem Inf Model. Mini Rev Med Chem. Angew Chem Int Ed Eng. Perspect Drug Disc Design. J Chem Inf Comput Sci. J Comput Aided Mol Des. The PubChem Database. Pipeline Pilot, version 6. Accelrys, Inc.
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