#6110. Generative Adversarial Networks for De Novo Molecular Design

September 2026publication date
Proposal available till 10-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Computer Science Applications;
Organic Chemistry;
Drug Discovery;
Structural Biology;
Molecular Medicine;
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Abstract:
In the chemical industry, the generation of novel molecular structures with beneficial pharmacological and physicochemical properties in de novo molecular design is a critical problem. The advent of deep learning and neural generative models has recently enabled significant achievements in constructing molecular design models in de novo design. Consequently, studies on new generative models continue to generate molecules that exhibit more useful chemical properties. In this study, we propose a method for de novo design that utilizes generative adversarial networks based on reinforcement learning for realistic molecule generation. This method learns to reproduce the training data distribution of simplified molecular-input line-entry system strings. The proposed method is demonstrated to effectively generate novel molecular structures from five benchmark results using a real-world public dataset, ChEMBL.
Keywords:
de novo molecular design; deep learning; generative adversarial networks; reinforcement learning; simplified molecular-input line-entry system (SMILES) strings

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