AC-ModNet: Molecular Reverse Design Network Based on Attribute Classification
Wei Wei, Jun Fang, Ning Yang, Qi Li, Lin Hu, Lanbo Zhao, Jie Han
TL;DR
This paper introduces AC-ModNet, a new machine learning model that can generate drug-like molecules with specific properties, improving drug design.
Contribution
The novel AC-ModNet model combines VAE and AC-GAN to generate molecules with specific attribute intervals.
Findings
AC-ModNet outperforms existing models in FCD and Frag evaluation metrics.
Molecules generated by AC-ModNet show potential application value in drug design.
The model is trained and evaluated using the 250K ZINC dataset.
Abstract
Deep generative models are becoming a tool of choice for exploring the molecular space. One important application area of deep generative models is the reverse design of drug compounds for given attributes (solubility, ease of synthesis, etc.). Although there are many generative models, these models cannot generate specific intervals of attributes. This paper proposes a AC-ModNet model that effectively combines VAE with AC-GAN to generate molecular structures in specific attribute intervals. The AC-ModNet is trained and evaluated using the open 250K ZINC dataset. In comparison with related models, our method performs best in the FCD and Frag model evaluation indicators. Moreover, we prove the AC-ModNet created molecules have potential application value in drug design by comparing and analyzing them with medical records in the PubChem database. The results of this paper will provide a…
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Taxonomy
TopicsComputational Drug Discovery Methods · Machine Learning in Materials Science