English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In our study, we demonstrate the synergy effect between convolutional neuralnetworks and the multiplicity of SMILES. The model we propose, the so-calledConvolutional Neural Fingerprint (CNF) model, reaches the accuracy oftraditional descriptors such as Dragon (Mauri et al. [22]), RDKit (Landrum[18]), CDK2 (Willighagen et al. [43]) and PyDescriptor (Masand and Rastija[20]). Moreover the CNF model generally performs better than highly fine-tunedtraditional descriptors, especially on small data sets, which is of greatinterest for the chemical field where data sets are generally small due toexperimental costs, the availability of molecules or accessibility to privatedatabases. We evaluate the CNF model along with SMILES augmentation during bothtraining and testing. To the best of our knowledge, this is the first time thatsuch a methodology is presented. We show that using the multiplicity of SMILESduring training acts as a regulariser and therefore avoids overfitting and canbe seen as ensemble learning when considered for testing.

Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction