- Open Access
© The Author(s) 2019
- Received: 6 December 2018
- Accepted: 14 January 2019
- Published: 5 February 2019
Computational characterization of chemical structures originated before the advent of digital computers . However, the ability to represent and manipulate large collections of molecules and their associated information was enabled by the rise of cheminformatics algorithms and their implementions on digital computers. Willett  has suggested the work of Ray and Kirsch  on substructure searching as the first description of a computer implementation (on punched cards) of a cheminformatics algorithm.
Programming language research blossomed during the 1950’s and 60’s and saw the development of high level programming languages (such as FORTRAN , LISP  and ALGOL ). Cheminformatics research took advantage of these efforts, to move beyond punched cards. One of the earliest cheminformatics applications in a high level language was DENDRAL , written in LISP in 1963 
Since the 1960’s, a plethora of languages have come into existence. Each language has its distinct features (directly memory manipulation in C, code as data in LISP , automated memory management in Java, lazy evaluation  in Haskell), but useful features from one language tend to show up in others (e.g., automated memory management initially appeared in LISP, but is now found in Java, Ruby, Python, C# and others). Furthermore, all modern languages are Turing equivalent  (i.e., capable of performing any arbitrary computation). One might then ask, what does it matter what language one uses to implement cheminformatics?
A number of factors go into deciding what language to use in a given setting. These include the suitability for a specific task (web development versus statistical modeling), prior knowledge of the language, the availability of supporting tools & frameworks and their licensing requirements and of course, performance.
A key consideration is the availability of external libraries such as cheminformatics toolkits (e.g., CDK  or JChem for Java applications). Many libraries (especially those written in C or C++) can be wrapped and made accessible to other languages (e.g., OpenBabel , RDKit and OEChem which are written in C++ provide SWIG wrappers enabling their use in Python and Java). Finally, for many projects, the choice of language is dictated by historical development (such as the use of Fortran for much of scientific computing).
At a more fundamental level, there are different programming models, which require conceptually different approaches to designing an application. For example, Khomtchouk et al.  suggest that the functional paradigm is best suited for scientific software development. On the other hand, Ray et al.  show that projects using functional languages do not necessarily show better software quality. One must consider others aspects, ranging from performance issues to the availability of programmers with sufficient skills to develop and then maintain applications written in functional languages. It is useful to note that some languages such as Scala are a hybrid, supporting both functional and procedural paradigms.
The intended audience for this series are practitioners of cheminformatics who are already familiar with one programming language and would like to learn what other languages may offer in terms of language features and supported tooling.
How that language (or model of programming) affects scientific software development
How a language may enable the development of new approaches to solving a problem in cheminformatics or computational chemistry
Specific approaches to overcome language limitations when dealing with chemical of biological data types
Comments on performance and it’s relevance to the languages goals
Educational aspects of the language (is it easier for newcomers?)
Development environments and frameworks that make a language easier to use and deploy (e.g., RStudio for R and Jupyter notebooks for Python)
RG conceived and designed the thematic issue and wrote this manuscript. The author read and approved the final manuscript.
The authors declare that they have no competing interests.
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- Wiener H (1947) Structural determination of paraffin boiling points. J Am Chem Soc 69(11):2636–2638View ArticleGoogle Scholar
- Willett P (2011) Chemoinformatics: a history. WIREs Comput Mol Sci 1(1):46–56View ArticleGoogle Scholar
- Ray LC, Kirsch RA (1957) Finding chemical records by digital computers. Science 126:814–819View ArticleGoogle Scholar
- McJones P (2018) History of FORTRAN and FORTRAN II (2018). http://www.softwarepreservation.org/projects/FORTRAN Accessed Nov 2018
- Stoyan H (1984) Early lisp history (1956–1959). In: Proceedings of the 1984 ACM symposium on LISP and functional programming. LFP ’84, pp 299–310. ACM, New York. https://doi.org/10.1145/800055.802047
- McJones P (2018) History of ALGOL (2018). http://www.softwarepreservation.org/projects/ALGOL/. Accessed Nov 2018
- Lindsay RK, Buchanan BG, Feigenbaum EA, Lederberg JA (1993) DENDRAL: a case study of the first expert system for scientific hypothesis formation. Artif Intell 61(2):209–261View ArticleGoogle Scholar
- Sutherland G (1963) Letter from Georgia Sutherland to R. Shirley. https://exhibits.stanford.edu/feigenbaum/catalog/qc171fk5406
- McIlroy D (1960) Macro instruction extensions of compiler languages. Commun ACM 3(4):214–220View ArticleGoogle Scholar
- Watt DA, Findlay W (2004) Programming language design concepts. Wiley, HobokenGoogle Scholar
- Brainerd WS, Landweber LH (1974) Theory of computation. Wiley, HobokenGoogle Scholar
- Willighagen EL, May JW, Alvarsson J, Berg A, Carlsson L, Duhrkop K, Jeliazkova N, Kuhn S, Pluskal T, Rojas-Cherto M, Spjuth O, Torrance G, Evelo CT, Guha R, Steinbeck C (2017) The chemistry development kit (cdk): atom typing, rendering, molecular formulas, and substructure searching. J Cheminform 9:33View ArticleGoogle Scholar
- O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open babel: an open chemical toolbox. J Cheminform 3:33. https://doi.org/10.1186/1758-2946-3-33 View ArticlePubMedPubMed CentralGoogle Scholar
- Khomtchouk BB, Weitz E, Karp PD, Wahlestedt C (2018) How the strengths of lisp-family languages facilitate building complex and flexible bioinformatics applications. Brief Bioinform 19(3):537–543. https://doi.org/10.1093/bib/bbw130 View ArticlePubMedGoogle Scholar
- Ray B, Posnett D, Devanbu P, Filkov V (2017) A large-scale study of programming languages and code quality in github. Commun ACM 60(10):91–100View ArticleGoogle Scholar
- Berenger F, Zhang KYJ, Yamanishi Y (2019) Chemoinformatics and structural bioinformatics in OCaml. J Cheminform. https://doi.org/10.1186/s13321-019-0332-0 View ArticleGoogle Scholar
- Höck S, Riedl R (2012) chemf: a purely functional chemistry toolkit. J Cheminform 4(1):38. https://doi.org/10.1186/1758-2946-4-38 View ArticlePubMedPubMed CentralGoogle Scholar