- Research article
- Open Access
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
© Ertl and Schuffenhauer; licensee BioMed Central Ltd. 2009
Received: 23 March 2009
Accepted: 10 June 2009
Published: 10 June 2009
A method to estimate ease of synthesis (synthetic accessibility) of drug-like molecules is needed in many areas of the drug discovery process. The development and validation of such a method that is able to characterize molecule synthetic accessibility as a score between 1 (easy to make) and 10 (very difficult to make) is described in this article.
The method for estimation of the synthetic accessibility score (SAscore) described here is based on a combination of fragment contributions and a complexity penalty. Fragment contributions have been calculated based on the analysis of one million representative molecules from PubChem and therefore one can say that they capture historical synthetic knowledge stored in this database. The molecular complexity score takes into account the presence of non-standard structural features, such as large rings, non-standard ring fusions, stereocomplexity and molecule size. The method has been validated by comparing calculated SAscores with ease of synthesis as estimated by experienced medicinal chemists for a set of 40 molecules. The agreement between calculated and manually estimated synthetic accessibility is very good with r2 = 0.89.
A novel method to estimate synthetic accessibility of molecules has been developed. This method uses historical synthetic knowledge obtained by analyzing information from millions of already synthesized chemicals and considers also molecule complexity. The method is sufficiently fast and provides results consistent with estimation of ease of synthesis by experienced medicinal chemists. The calculated SAscore may be used to support various drug discovery processes where a large number of molecules needs to be ranked based on their synthetic accessibility, for example when purchasing samples for screening, selecting hits from high-throughput screening for follow-up, or ranking molecules generated by various de novo design approaches.
The assessment of synthetic accessibility (SA) of a lead candidate is a task which plays a role in lead discovery regardless of the method the lead candidate has been identified with. In the case of a de novo designed molecule the experimental validation of its activity requires synthesis of the compound. In the case of experimental or virtual screening exploration of the SAR around the hit, synthetic access to the chemotype is required as well. The more difficult the synthesis of the lead candidate is, the more time and resources are needed for the exploration of this particular area of chemical space. Lead candidates are normally prioritized according to criteria such as drug-likeness [1, 2], natural-product likeness , predicted activity or freedom to operate with respect to intellectual property. Since sooner or later in the drug discovery process the candidates will be ranked, or even eliminated by their synthetic accessibility, it is desirable to include this aspect into the prioritization of compounds early on. When compounds are purchased from off-the-shelf catalogues in order to augment the screening library, compounds likely to fail later on because of problems with their synthetic tractability may be removed already at this stage. Also in the selection of follow-up candidates from large primary screening results, prioritization by synthetic accessibility can ensure that compounds chosen for validation in dose-response experiments are less likely to be later rejected based on problems with their synthesis. In these two cases, the compounds that are to be validated exist, which means that chemical synthesis must in principle be feasible despite possible complications. When chemical structures are constructed during the de novo design process, one cannot take for granted that the chemical synthesis of such compounds is feasible at all. Therefore it is even more important to estimate whether these compounds can be synthesized with reasonable effort. While experienced chemists are able to estimate synthetic accessibility of individual compounds, performing this estimation for large numbers of compounds requires computational methods.
Several computational approaches to assess synthetic accessibility of molecules exist . They may be roughly divided into two groups: complexity-based and retrosynthetic-based. Complexity-based methods use sets of rules to estimate complexity of target structures (features like presence of spiro-rings, non-standard ring fusions, or large number of stereocenters) which is then directly related to SA. The second group of methods is based on the full retrosynthetic approach when the complete synthetic tree leading to the molecules needs to be processed. Such a procedure is quite time consuming, because the size of the synthetic tree grows exponentially with the number of required steps. Additionally, retrosynthetic methods rely on reaction databases as well as lists of available reagents, which both need to be kept up-to-date. This high requirement on maintenance is probably one of the reasons why methods for estimation of SA based on the retrosynthetic approach have been developed mainly by large academic teams (for example group of Prof. Gasteiger at Erlangen University with the WODCA system  or group of Prof. Johnson at Leeds University with the SPROUT/CAESA program ).
The major problem when developing methods for estimation of SA is the validation of results. It is not straightforward to extract synthetic complexity out of the protocol describing molecule synthesis. While the overall yield over the sequence of synthetic steps gives some information, this depends also on the effort which has been undertaken to optimize the process; and if only low amounts are needed for initial experiments, then a non-optimal synthesis is tolerable. Another possible measure of synthetic accessibility of a molecule could be its price in catalogues of chemical providers. The price, however, depends on too many factors not related to SA (for example novelty of the reagent, demand, packaging, marketing issues) to be relied on as an objective measure of SA. We were not able to get any reasonable correlation between normalised catalogue price and various structural descriptors for a large set of reagents. The total cost of production of pharmaceutical substances, where the whole process is highly optimized concerning the cost of goods and manufacturing expenses, would be probably the most useful parameter in this respect, but unfortunately this type of data is one of the most guarded secrets in the pharmaceutical industry.
Therefore currently the only way to assess the performance of the calculated synthetic accessibility score is to rely on a ranking done by experienced medicinal chemists.
Several studies focused on performance of chemists in ranking molecules or estimating their synthetic accessibility. In the work of Takaoka et al.  5 chemists ranked 3980 molecules according to their ease of synthesis into three categories: easy, possible and hard. Correlation coefficients between scores assigned by various chemists were in the range 0.40 to 0.56 with an average 0.46. The authors concluded, however, that the models based on the average of chemist estimations may be useful for classification of molecules. Baber and Feher  described an experiment where 8 medicinal chemists scored 100 drug-like compounds according to their ease of synthesis. The mean absolute error in chemists' estimations was around 10%, for some compounds, however, there was a variation of up to 70%. In the study of Lajiness at al. , 13 chemists reviewed sets of 2000 diverse compounds containing also a common set of 250 compounds, with the goal of removing those that are unacceptable for any reason (too complex, having too complicated synthesis, unsuitable for launching a drug discovery campaign etc): the objective was to see the consistency of chemists in picking "bad" molecules. The study has shown that chemists are not very consistent in their rejection of compounds: only 24% of the compounds rejected by one chemist were also rejected by another. Boda et al.  asked 5 chemists to rank 100 molecules selected randomly from the Journal of Medicinal Chemistry according to their ease of synthesis. The chemists seemed to agree on synthetic accessibility for very simple and quite complex molecules; in the middle range, however, larger divergence was observed. The agreement among chemists was acceptable with correlation coefficients in the range 0.73 – 0.84. The ranks entered by chemists have been then used to train the synthetic accessibility score function described in the publication.
All these studies indicate that even experienced chemists differ in their estimations of ease of synthesis. This, of course, is nothing surprising. Chemists have different backgrounds, different areas of research (medicinal chemists, natural product chemists, chemists working in combinatorial synthesis, etc.) or experience based on projects they have been working on. Therefore, to use ranks assigned by chemists as a measure of SA, a consensus score based on several estimations is required. The situation is additionally complicated by the fact that the ease of synthesis for a particular molecule is not a constant. It evolves within time as a consequence of introduction of new synthetic methods and availability of new reagents and building blocks. For example, an introduction of methods like carbon-carbon coupling reactions, sophisticated organometallic catalysts or use of enzymes in organic synthesis allows currently relatively easy synthesis of molecules, which would be very difficult to make just a decade ago .
Calculation of Synthetic Accessibility Score
The goal of the present study was to develop a method for estimation of SA which could be used in various drug discovery activities. The fact that the method should be able to process very large numbers of molecules (several millions when making a selection from large commercial catalogues or processing virtual libraries), as well as a decision not to rely on comprehensive databases of reactions and reagents (with the related maintenance hurdle) clearly favored implementation of a method based on molecular complexity. Pure complexity-based approaches, however, have known deficiencies: they do not take into account easy availability of complex reagents, which allows us to introduce some complex features to molecules relatively easily , neither the fact that some simple reactions can produce quite complex structures (condensation reactions, cycloadditions, various cyclizations). To account for this deficit of a pure complexity-based approach we have decided to implement a method which would be a compromise between fast complexity-based, and resource-intensive full retrosynthetic approaches. In addition to several standard rules identifying known synthetically problematic molecular features, we wanted to capture also the "synthetic chemistry knowledgebase" by analyzing common substructures in a very large number of already synthesized molecules. For this purpose, a representative subset of molecules from the PubChem database  was used. PubChem contains currently 37 million unique molecules including common drugs and agrochemicals, structures extracted from patents, and large numbers of samples from numerous compound providers. One million molecules representatively selected from PubChem served as a training set to identify common (and therefore one can assume also easy to make) structural features. Our approach is similar to those presented by Boda and Johnson  who based their estimation of molecular complexity on a set of simple fragments collected from a database of drug-like molecules. Our fragment approach differs, however, in using different types of fragments, as well as by a different method to calculate fragment contributions.
The fragmentScore, as already mentioned, was introduced to capture the "historical synthetic knowledge" by analyzing common structural features in a large number of already synthesized molecules. The score is calculated as a sum of contributions of all fragments in the molecule divided by the number of fragments in this molecule. The database of fragment contributions has been generated by statistical analysis of substructures in the PubChem collection as described in the following section.
To illustrate the performance of the new SAscore, its distribution for 100,000 synthetic molecules from catalogues of commercial compound providers (not used in the training process), 100,000 bioactive molecules randomly selected from the WDI  and MDDR  databases and 100,000 natural products from the Dictionary of Natural Products  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 . 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 100,000 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. is provided easily by several free cheminformatics toolkits . 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  using the HOSE type fragments  implemented there, and the results were practically identical to those of PipelinePilot implementation.
Validation of Synthetic Accessibility Score
The agreement among chemists in their rankings is quite good, the r2 ranges between 0.450 and 0.892 with the average r2 for all "chemist pairs" being 0.718. 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 1.23 and average standard error of mean (shown for all molecules in Figure 5 as error bars) is 0.41. 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 ). 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.
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 39,000 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 . 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 . 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.
Relation between common linkage reactions and most common fragments shown in Figure 2.
Amide bond formation or Urea formation
2, 6, 8, 12 (from primary amine) or 16 (from secondary amine), 28 (only if aniline)
6, 12 (from primary amine) or 16 (from secondary amine)
2, 6, 8, 10
4 (the CH2 group from the aldehyde carbon), 12 (from primary amine) or 16 (from secondary amine)
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 17.
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 performance of the SAscore has been validated by comparing it with the "ease of synthesis" ranks estimated by experienced medicinal chemists, with very good agreement between these two values (r2 = 0.890). 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.
The authors want to thank nine Novartis chemists who were willing to rank the test molecules and in this way helped to validate the score, as well as to Richard Lewis for critically reading the manuscript and for helpful comments.
- Clark DE, Pickett SE: Computational methods for the prediction of 'drug-likeness'. Drug Discov Today. 2000, 5: 49-58. 10.1016/S1359-6446(99)01451-8.View ArticleGoogle Scholar
- 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. 2000, 43: 3714-3717. 10.1021/jm000942e.View ArticleGoogle Scholar
- Ertl P, Roggo S, Schuffenhauer A: Natural Product-likeness Score and Its Application for Prioritization of Compound Libraries. J Chem Inf Model. 2008, 48: 68-74. 10.1021/ci700286x.View ArticleGoogle Scholar
- Baber JC, Feher M: Predicting Synthetic Accessibility: Application in Drug Discovery and Development. Mini Rev Med Chem. 2004, 4: 681-692.View ArticleGoogle Scholar
- Gasteiger J, Ihlenfeldt WD: Computer-Assisted Planning of Organic Syntheses: The Second Generation of Programs. Angew Chem Int Ed Eng. 1996, 34: 2613-2633. 10.1002/anie.199526131.View ArticleGoogle Scholar
- Gillet VJ, Myatt G, Zsoldos Z, Johnson AP: SPROUT, HIPPO and CAESA: Tools for de novo structure generation and estimation of synthetic accessibility. Perspect Drug Disc Design. 1995, 3: 34-50. 10.1007/BF02174466.View ArticleGoogle Scholar
- Takaoka Y, Endo Y, Yamanobe S, Kakinuma H, Okubo T, Shimazaki Y, Ota T, Sumiya S, Yoshikawa K: Development of a Method for Evaluating Drug-Likeness and Ease of Synthesis Using a Data Set in Which Compounds Are Assigned Scores Based on Chemists' Intuition. J Chem Inf Comput Sci. 2003, 43: 1269-1275.View ArticleGoogle Scholar
- Lajiness MS, Maggiora GM, Shanmugasundaram V: Assessment of the Consistency of Medicinal Chemists in Reviewing Sets of Compounds. J Med Chem. 2004, 47: 4891-4896. 10.1021/jm049740z.View ArticleGoogle Scholar
- Boda K, Seidel T, Gasteiger J: Structure and Reaction based Evaluation of Synthetic Accessibility. J Comput Aided Mol Des. 2007, 21: 311-325. 10.1007/s10822-006-9099-2.View ArticleGoogle Scholar
- Kündig P: The Future of Organic Synthesis. Science. 2006, 314: 430-431. 10.1126/science.1134084.View ArticleGoogle Scholar
- The PubChem Database. [http://pubchem.ncbi.nlm.nih.gov/]
- Boda K, Johnson AP: Molecular Complexity Analysis of de Novo Designed Ligands. J Med Chem. 2006, 49: 5869-5879. 10.1021/jm050054p.View ArticleGoogle Scholar
- Pipeline Pilot, version 6.0. Accelrys, Inc., San Diego, CA, USA, [http://www.accelrys.com]
- WDI – Derwent World Drug Index, version 2007.04. [http://www.thomsonscientific.com/]
- MDDR – MDL Drug Data Report, version 2007.2. [http://www.prous.com/product/electron/mddr.html]
- CRC Dictionary of Natural Products, v 15.1. 2006, CRC Press, [http://www.crcpress.com/]
- Ertl P, Jelfs S: Designing Drugs on the Internet? Free Web Tools and Services Supporting Medicinal Chemistry. Curr Top Med Chem. 2007, 7: 1491-1501. 10.2174/156802607782194707.View ArticleGoogle Scholar
- mib – Molinspiration molecular processing engine, version 2007.10, Molinspiration Cheminformatics, Slovensky Grob, Slovak Republic. [http://www.molinspiration.com]
- Bremser W: HOSE – A Novel Substructure Code. Anal Chim Acta. 1978, 103: 355-365. 10.1016/S0003-2670(01)83100-7.View ArticleGoogle Scholar
- Kanemasa S, Sakoh H, Wada E, Tsuge O: Diene-transmissive Diels-Alder Reaction Using 2-Ethoxy-3-methylene-1,4-pentadiene and 2-(2-Bromo-1-ethoxyethyl)1,3-butadiene. Bulletin of the Chemical Society of Japan. 1985, 58: 3312-3319. 10.1246/bcsj.58.3312.View ArticleGoogle Scholar
- Ertl P: Cheminformatics Analysis of Organic Substituents: Identification of the Most Common Substituents, Calculation of Substituent Properties, and Automatic Identification of Drug-like Bioisosteric Groups. J Chem Inf Comput Sci. 2003, 43: 374-380.View ArticleGoogle Scholar
- Lewell XQ, Judd DB, Watson SP, Hann MM: RECAP – Retrosynthetic Combinatorial Analysis Procedure: A Powerful New Technique for Identifying Privileged Molecular Fragments with Useful Applications in Combinatorial Chemistry. J Chem Inf Comput Sci. 1998, 38: 511-522.View ArticleGoogle Scholar
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