Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Benoit Da Mota"'
Publikováno v:
Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-17 (2021)
Abstract Chemical diversity is one of the key term when dealing with machine learning and molecular generation. This is particularly true for quantum chemical datasets. The composition of which should be done meticulously since the calculation is hig
Externí odkaz:
https://doaj.org/article/5ee95c78f5cb4002b6b284051ae3f53b
Publikováno v:
Journal of Cheminformatics, Vol 12, Iss 1, Pp 1-19 (2020)
Abstract The objective of this work is to design a molecular generator capable of exploring known as well as unfamiliar areas of the chemical space. Our method must be flexible to adapt to very different problems. Therefore, it has to be able to work
Externí odkaz:
https://doaj.org/article/6dc989babecc4c7b94b0fa7e62a5214a
Publikováno v:
Journal of Cheminformatics, Vol 11, Iss 1, Pp 1-15 (2019)
Abstract The QM9 dataset has become the golden standard for Machine Learning (ML) predictions of various chemical properties. QM9 is based on the GDB, which is a combinatorial exploration of the chemical space. ML molecular predictions have been rece
Externí odkaz:
https://doaj.org/article/171f98117f0f45a491c571af55c928bd
Publikováno v:
Digital Discovery.
Discovering an efficient new molecule can have a huge impact on a chemical research field. For several problems, the current knowledge is too scarce to train robust deep learning models. An exploratory approach can be a solution. However, when we con
Publikováno v:
Advances in Knowledge Discovery and Management ISBN: 9783030902865
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f7d3854b22ab4ac17e4782a30ceee46
https://doi.org/10.1007/978-3-030-90287-2_8
https://doi.org/10.1007/978-3-030-90287-2_8
Finding from scratch a new molecule with sought properties remains a challenge. In this chapter, we propose a presentation of the research challenges of molecular generation using methods from the artificial intelligence domain. This objective can be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c5d98d6953f4a4d5e5ab84a4bd0dc816
https://doi.org/10.1016/b978-0-12-822249-2.00004-9
https://doi.org/10.1016/b978-0-12-822249-2.00004-9
Autor:
Takashiro Akitsu, Golnaz Bissadi, Thomas Cauchy, Kevin Cremanns, Béatrice Duval, Dea Gogishvili, Junpei Iwama, Masato Kobayashi, Thierry Kogej, Jules Leguy, Benoit Da Mota, Eva Nittinger, Atanas Patronov, Shi-Ping Peng, Hiroshi Sakiyama, Christian Schmitz, Christian Tyrchan, Xin-Yu Yang, Yi Zhao
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::453a0f92a8c91705cff70bcca53b8435
https://doi.org/10.1016/b978-0-12-822249-2.09990-4
https://doi.org/10.1016/b978-0-12-822249-2.09990-4
Publikováno v:
Journal of Cheminformatics
Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-17 (2021)
Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-17 (2021)
Chemical diversity is one of the key term when dealing with machine learning and molecular generation. This is particularly true for quantum chemical datasets. The composition of which should be done meticulously since the calculation is highly time
AI-assisted molecular optimization is a very active research field as it is expected to provide the next-generation drugs and molecular materials. An important difficulty is that the properties to be optimized rely on costly evaluations. Machine lear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04e134656b0f1b28cfe36877012cf478
Publikováno v:
Extraction et Gestion des connaissances, EGC 2019
Extraction et Gestion des connaissances, EGC 2019, Jan 2019, Metz, France
HAL
Extraction et Gestion des connaissances, EGC 2019, Jan 2019, Metz, France
HAL
International audience
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::8df015af3f291178d35a23524498cf03
https://hal.archives-ouvertes.fr/hal-02889078
https://hal.archives-ouvertes.fr/hal-02889078