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Chemoinformatics: Achievements and Challenges, a Personal View

Computer-Chemie-Centrum, University of Erlangen-Nuremberg, D-91052 Erlangen, Germany
Academic Editor: Peter Willett
Molecules 2016, 21(2), 151; https://doi.org/10.3390/molecules21020151
Received: 20 November 2015 / Revised: 14 January 2016 / Accepted: 20 January 2016 / Published: 27 January 2016
(This article belongs to the Special Issue Chemoinformatics)
Chemoinformatics provides computer methods for learning from chemical data and for modeling tasks a chemist is facing. The field has evolved in the past 50 years and has substantially shaped how chemical research is performed by providing access to chemical information on a scale unattainable by traditional methods. Many physical, chemical and biological data have been predicted from structural data. For the early phases of drug design, methods have been developed that are used in all major pharmaceutical companies. However, all domains of chemistry can benefit from chemoinformatics methods; many areas that are not yet well developed, but could substantially gain from the use of chemoinformatics methods. The quality of data is of crucial importance for successful results. Computer-assisted structure elucidation and computer-assisted synthesis design have been attempted in the early years of chemoinformatics. Because of the importance of these fields to the chemist, new approaches should be made with better hardware and software techniques. Society’s concern about the impact of chemicals on human health and the environment could be met by the development of methods for toxicity prediction and risk assessment. In conjunction with bioinformatics, our understanding of the events in living organisms could be deepened and, thus, novel strategies for curing diseases developed. With so many challenging tasks awaiting solutions, the future is bright for chemoinformatics. View Full-Text
Keywords: chemoinformatics; chemical structure representation; chemical databases; data quality; inductive learning; data mining methods; property prediction; QSAR; QSPR; CASE; CASD chemoinformatics; chemical structure representation; chemical databases; data quality; inductive learning; data mining methods; property prediction; QSAR; QSPR; CASE; CASD
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MDPI and ACS Style

Gasteiger, J. Chemoinformatics: Achievements and Challenges, a Personal View. Molecules 2016, 21, 151.

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