TY  - JOUR
AB  - A parametric t-SNE approach based on deep feed-forward neural networks was applied to the chemical space visualization problem. It is able to retain more information than certain dimensionality reduction techniques used for this purpose (principal component analysis (PCA), multidimensional scaling (MDS)). The applicability of this method to some chemical space navigation tasks (activity cliffs and activity landscapes identification) is discussed. We created a simple web tool to illustrate our work (http://space.syntelly.com).
AU  - Karlov, D.S.*
AU  - Sosnin, S.*
AU  - Tetko, I.V.
AU  - Fedorov, M.V.*
C1  - 55597
C2  - 46418
CY  - Thomas Graham House, Science Park, Milton Rd, Cambridge Cb4 0wf, Cambs, England
SP  - 5151-5157
TI  - Chemical space exploration guided by deep neural networks.
JO  - RSC Adv.
VL  - 9
IS  - 9
PB  - Royal Soc Chemistry
PY  - 2019
SN  - 2046-2069
ER  - 

TY  - JOUR
AB  - In light of the high relevance of polypharmacology, multi-target screening is a major trend in drug discovery. As such, the increasing amount of available structure-activity data requires the application of chemoinformatic approaches to mine structure-multiple activity relationships. To this end, activity landscape methods, initially developed to explore the structure-activity relationships for compounds screened against one target, have been adapted to mine Structure-Multiple Activity Relationships (SMARt). Herein, we survey advances in the chemoinformatic approaches to retrieve SMARt from screening data sets. Case studies relevant to modern drug discovery are discussed. The methods covered in this survey are general and can be implemented to explore the SMARt of other data sets screened across multiple biologically endpoints.
AU  - Saldívar-González, F.I.*
AU  - Naveja, J.J.
AU  - Palomino-Hernández, O.*
AU  - Medina-Franco, J.L.*
C1  - 50324
C2  - 42392
CY  - Cambridge
SP  - 632-641
TI  - Getting SMARt in drug discovery: Chemoinformatics approaches for mining structure-multiple activity relationships.
JO  - RSC Adv.
VL  - 7
IS  - 2
PB  - Royal Soc Chemistry
PY  - 2017
SN  - 2046-2069
ER  -