AnoChem: Prediction of chemical structural abnormalities based on machine learning models
0301 basic medicine
Computational chemistry
0303 health sciences
Cheminformatics
Machine learning
Method Article
AnoChem
Drug design
TP248.13-248.65
Biotechnology
DOI:
10.1016/j.csbj.2024.05.017
Publication Date:
2024-05-16T01:03:28Z
AUTHORS (4)
ABSTRACT
drug design aims to rationally discover novel and potent compounds while reducing experimental costs during the development stage. Despite numerous generative models that have been developed, few successful cases of utilizing reported. One most common challenges is designing are not synthesizable or realistic. Therefore, methods capable accurately assessing chemical structures proposed by for needed. In this study, we present AnoChem, a computational framework based on deep learning designed assess likelihood generated molecule being real. AnoChem achieves an area under receiver operating characteristic curve score 0.900 distinguishing between real molecules. We utilized evaluate compare performances several models, using other metrics, namely SAscore Fréschet ChemNet distance (FCD). demonstrates strong correlation with these validating its effectiveness as reliable tool models. The source code available at https://github.com/CSB-L/AnoChem.
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