Title |
Expanding the fragrance chemical space for virtual screening
|
---|---|
Published in |
Journal of Cheminformatics, May 2014
|
DOI | 10.1186/1758-2946-6-27 |
Pubmed ID | |
Authors |
Lars Ruddigkeit, Mahendra Awale, Jean-Louis Reymond |
Abstract |
The properties of fragrance molecules in the public databases SuperScent and Flavornet were analyzed to define a "fragrance-like" (FL) property range (Heavy Atom Count ≤ 21, only C, H, O, S, (O + S) ≤ 3, Hydrogen Bond Donor ≤ 1) and the corresponding chemical space including FL molecules from PubChem (NIH repository of molecules), ChEMBL (bioactive molecules), ZINC (drug-like molecules), and GDB-13 (all possible organic molecules up to 13 atoms of C, N, O, S, Cl). The FL subsets of these databases were classified by MQN (Molecular Quantum Numbers, a set of 42 integer value descriptors of molecular structure) and formatted for fast MQN-similarity searching and interactive exploration of color-coded principal component maps in form of the FL-mapplet and FL-browser applications freely available at http://www.gdb.unibe.ch. MQN-similarity is shown to efficiently recover 15 different fragrance molecule families from the different FL subsets, demonstrating the relevance of the MQN-based tool to explore the fragrance chemical space. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Switzerland | 1 | 25% |
Netherlands | 1 | 25% |
Germany | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 50% |
Scientists | 2 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 1 | 2% |
Greece | 1 | 2% |
Germany | 1 | 2% |
Unknown | 60 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 14 | 22% |
Researcher | 14 | 22% |
Student > Master | 9 | 14% |
Student > Bachelor | 5 | 8% |
Other | 3 | 5% |
Other | 10 | 16% |
Unknown | 8 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 22 | 35% |
Computer Science | 6 | 10% |
Agricultural and Biological Sciences | 5 | 8% |
Biochemistry, Genetics and Molecular Biology | 4 | 6% |
Chemical Engineering | 3 | 5% |
Other | 12 | 19% |
Unknown | 11 | 17% |