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Recommendations for chemists: a case study

Published: 27 September 2018 Publication History

Abstract

Large pharmaceutical companies have a wealth of reaction and chemical structure data, but face a new problem: analyzing that corpus to yield project insights and future directions. One straight-forward approach would be to have a recommendation system to match drug structures with similar research endeavors across geographically- or organizationally-separated groups. We developed and deployed Chem Recommender, a system that suggests similar, related work to experiments that chemists have recently started. The goal of the system is to accelerate the drug discovery process by ensuring that chemists are aware of each other's work. To date, we have sent more than 8500 recommendations to over 800 medicinal chemists in our organization. The results have been positive, with several chemists reporting that the recommendations have aided their molecular syntheses.

References

[1]
D. Bridge, M.H. Göker, L. McGinty, and B. Smyth. 2005. Case-based Recommender Systems. Knowledge Engineering Review 20, 3 (Sept. 2005), 315--320.
[2]
J. Corkery. 2011. Recommendation System for Compound Selection. (Feb. 2011). https://www.eyesopen.com/blog/2011/02/28/recommendation-system-for-compound-selection
[3]
J.L. Durant, B.A. Leland, D.R. Henry, and J.G. Nourse. 2002. Reoptimization of MDL Keys for Use in Drug Discovery. Journal of Chemical Information and Computer Sciences 42, 6 (2002), 1273--1280.
[4]
Elastic. 2018. Elasticsearch. (2018). https://www.elastic.co/products/elasticsearch
[5]
J.A. Grant, J. A. Haigh, B.T. Pickup, A. Nicholls, and R.A. Sayle. 2006. Lingos, Finite State Machines, and Fast Similarity Searching. Journal of Chemical Information and Modeling 46, 5 (2006), 1912--1918.
[6]
R.J. Hall, C.W. Murray, and M.L. Verdonk. 2017. The Fragment Network: A Chemistry Recommendation Engine Built Using a Graph Database. Journal of Medicinal Chemistry 60, 14 (2017), 6440--6450.
[7]
J.P. Hughes, S. Rees, S.B. Kalindjian, and K.L. Philpott. 2011. Principles of early drug discovery. British Journal of Pharmacology 162, 6 (March 2011), 1239--1249.
[8]
IBM. 2016. 2016 Email Marketing Metrics Benchmark Study. (2016). https://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=UVL12406USEN
[9]
J. A. Konstan and J. Riedl. 2012. Recommended for you. IEEE Spectrum 49, 10 (Oct. 2012), 54--61.
[10]
F. Lorenzi and F. Ricci. 2005. Case-Based Recommender Systems: A Unifying View. In Proceedings of the 2003 International Conference on Intelligent Techniques for Web Personalization, B. Mobasher and S.S. Anand (Eds.). Springer-Verlag, 89--113.
[11]
H. Öztürk, E. Ozkirimli, and A. Özgür. 2016. A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction. BMC Bioinformatics 17, 128 (March 2016).
[12]
A. Purakayastha. 2017. The Consumerization Of Enterprise Technology. (Dec. 2017). https://www.forbes.com/sites/forbestechcouncil/2017/12/04/the-consumerization-of-enterprise-technology
[13]
G. Salton and M.J. McGill. 1986. Introduction to modern information retrieval. McGraw-Hill, Inc.
[14]
J. Savage, A. Kishimoto, B. Buesser, E. Diaz-Aviles, and C. Alzate. 2017. Chemical Reactant Recommendation Using a Network of Organic Chemistry. In Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM, 210--214.
[15]
J. C. Sheehan and K. R. Henery-Logan. 1957. The Total Synthesis of Penicillin V. Journal of the American Chemical Society 79, 5 (1957), 1262--1263.
[16]
D. Vidal, M. Thormann, and M. Pons. 2005. LINGO, an Efficient Holographic Text Based Method To Calculate Biophysical Properties and Intermolecular Similarities. Journal of Chemical Information and Modeling 45, 2 (2005), 386--393.
[17]
D. Weininger. 1988. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences 28, 1 (1988), 31--36.
[18]
D. Weininger, A. Weininger, and J.L. Weininger. 1989. SMILES. 2. Algorithm for generation of unique SMILES notation. Journal of Chemical Information and Computer Sciences 29, 2 (1989), 97--101.

Cited By

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  • (2022)Recommending research papers to chemistsProceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries10.1145/3529372.3533281(1-4)Online publication date: 20-Jun-2022
  • (2020)An Artificial Intelligence Approach to Proactively Inspire Drug Discovery with RecommendationsJournal of Medicinal Chemistry10.1021/acs.jmedchem.9b0213063:16(8824-8834)Online publication date: 26-Feb-2020
  • (2019)Rethinking drug design in the artificial intelligence eraNature Reviews Drug Discovery10.1038/s41573-019-0050-319:5(353-364)Online publication date: 4-Dec-2019

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cover image ACM Conferences
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems
September 2018
600 pages
ISBN:9781450359016
DOI:10.1145/3240323
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 September 2018

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Author Tags

  1. electronic lab notebook
  2. medicinal chemistry
  3. recommendations

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RecSys '18
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RecSys '18: Twelfth ACM Conference on Recommender Systems
October 2, 2018
British Columbia, Vancouver, Canada

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RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all
  • (2022)Recommending research papers to chemistsProceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries10.1145/3529372.3533281(1-4)Online publication date: 20-Jun-2022
  • (2020)An Artificial Intelligence Approach to Proactively Inspire Drug Discovery with RecommendationsJournal of Medicinal Chemistry10.1021/acs.jmedchem.9b0213063:16(8824-8834)Online publication date: 26-Feb-2020
  • (2019)Rethinking drug design in the artificial intelligence eraNature Reviews Drug Discovery10.1038/s41573-019-0050-319:5(353-364)Online publication date: 4-Dec-2019

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