Chemoinformatics and structural bioinformatics in OCaml

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Abstract

Background

OCaml is a functional programming language with strong static types, Hindley–Milner type inference and garbage collection. In this article, we share our experience in prototyping chemoinformatics and structural bioinformatics software in OCaml.

Results

First, we introduce the language, list entry points for chemoinformaticians who would be interested in OCaml and give code examples. Then, we list some scientific open source software written in OCaml. We also present recent open source libraries useful in chemoinformatics. The parallelization of OCaml programs and their performance is also shown. Finally, tools and methods useful when prototyping scientific software in OCaml are given.

Conclusions

In our experience, OCaml is a programming language of choice for method development in chemoinformatics and structural bioinformatics.

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Most cited references 36

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Benchmarking sets for molecular docking.

Niu Huang, Brian K. Shoichet, John Irwin (2006)

Ligand enrichment among top-ranking hits is a key metric of molecular docking. To avoid bias, decoys should resemble ligands physically, so that enrichment is not simply a separation of gross features, yet be chemically distinct from them, so that they are unlikely to be binders. We have assembled a directory of useful decoys (DUD), with 2950 ligands for 40 different targets. Every ligand has 36 decoy molecules that are physically similar but topologically distinct, leading to a database of 98,266 compounds. For most targets, enrichment was at least half a log better with uncorrected databases such as the MDDR than with DUD, evidence of bias in the former. These calculations also allowed 40x40 cross-docking, where the enrichments of each ligand set could be compared for all 40 targets, enabling a specificity metric for the docking screens. DUD is freely available online as a benchmarking set for docking at http://blaster.docking.org/dud/.

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Internal coarse-graining of molecular systems.

Walter Fontana, Russ Harmer, Jean Krivine … (2009)

Modelers of molecular signaling networks must cope with the combinatorial explosion of protein states generated by posttranslational modifications and complex formation. Rule-based models provide a powerful alternative to approaches that require explicit enumeration of all possible molecular species of a system. Such models consist of formal rules stipulating the (partial) contexts wherein specific protein-protein interactions occur. These contexts specify molecular patterns that are usually less detailed than molecular species. Yet, the execution of rule-based dynamics requires stochastic simulation, which can be very costly. It thus appears desirable to convert a rule-based model into a reduced system of differential equations by exploiting the granularity at which rules specify interactions. We present a formal (and automated) method for constructing a coarse-grained and self-consistent dynamical system aimed at molecular patterns that are distinguishable by the dynamics of the original system as posited by the rules. The method is formally sound and never requires the execution of the rule-based model. The coarse-grained variables do not depend on the values of the rate constants appearing in the rules, and typically form a system of greatly reduced dimension that can be amenable to numerical integration and further model reduction techniques.

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Satisfying general proximity / similarity queries with metric trees

Jeffrey K. Uhlmann (1991)

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Author and article information

Contributors

Francois Berenger:

ORCID: http://orcid.org/0000-0003-1377-944X

beren314@bio.kyutech.ac.jp

Kam Y. J. Zhang: kamzhang@riken.jp

Yoshihiro Yamanishi: yamani@bio.kyutech.ac.jp

Journal

Journal ID (nlm-ta): J Cheminform

Journal ID (iso-abbrev): J Cheminform

Title: Journal of Cheminformatics

Publisher: Springer International Publishing (Cham )

ISSN (Electronic): 1758-2946

Publication date (Electronic): 5 February 2019

Publication date PMC-release: 5 February 2019

Publication date Collection: 2019

Volume: 11

Electronic Location Identifier: 10

Affiliations

[1 ]ISNI 0000 0001 2110 1386, GRID grid.258806.1, Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, , Kyushu Institute of Technology, ; Iizuka, Fukuoka Japan

[2 ]ISNI 0000000094465255, GRID grid.7597.c, Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, , RIKEN, ; Yokohama, Kanagawa Japan

[3 ]ISNI 0000 0004 1754 9200, GRID grid.419082.6, PRESTO, Japan Science and Technology Agency, ; Kawaguchi, Saitama Japan

Author information

Francois Berenger http://orcid.org/0000-0003-1377-944X

Article

Publisher ID: 332

DOI: 10.1186/s13321-019-0332-0

PMC ID: 6689879

PubMed ID: 30719579

SO-VID: 4be391bf-9151-4fc5-8b26-63656c6f6f32

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 6 September 2018

Date accepted : 22 January 2019

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100002241, Japan Science and Technology Agency;

Award ID: JPMJPR15D8

Award Recipient : Yoshihiro Yamanishi

Funded by: FundRef http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;

Funded by: RIKEN ACCC

Custom metadata

ScienceOpen disciplines: Chemoinformatics

Keywords: chemoinformatics,structural bioinformatics,bisector tree,scientific software,software prototyping,open source,functional programming,ocaml

Data availability:

ScienceOpen disciplines: Chemoinformatics

Keywords: chemoinformatics, structural bioinformatics, bisector tree, scientific software, software prototyping, open source, functional programming, ocaml

Chemoinformatics and structural bioinformatics in OCaml

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Abstract

Background

Results

Conclusions

Related collections

Fracture and Structural Integrity

Most cited references 36

Benchmarking sets for molecular docking.

Internal coarse-graining of molecular systems.

Satisfying general proximity / similarity queries with metric trees

Author and article information

Contributors

Journal

Affiliations

Author information

Article

History

Funding

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